Field Work’s Optimization for the Digital Capture of Large University Campuses, Combining Various Techniques of Massive Point Capture

  Citation: Pérez, J.J.; Senderos, M.; Casado, A.; Leon, I. Field Work’s Optimization for the Digital Capture of Large University Campuses, Combining Various Techniques of Massive Point Capture. Buildings 2022,12, 380. https://doi.org/ 10.3390/buildings12030380 Academic Editor: Fahim Ullah Received: 21 February 2022 Accepted: 16 March 2022 Published: 18 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). buildings Article Field Work’s Optimization for the Digital Capture of Large University Campuses, Combining Various Techniques of Massive Point Capture JoséJavier Pérez, María Senderos, Amaia Casado and Iñigo Leon * Department of Architecture, University of the Basque Country UPV/EHU, Plaza Oñati 2, 20018 Donostia-San Sebastián, Spain; josejavier[email protected] (J.J.P.); maria.sender[email protected] (M.S.); [email protected] (A.C.) *Correspondence: [email protected]; Tel.: +34-943-01-7192 Abstract: The aim of the study is to obtain fast digitalization of large urban settings. The data of two university campuses in two cities in northern Spain was captured. Challenges were imposed by the lockdown situation caused by the COVID-19 pandemic, which limited mobility and affected the field work for data readings. The idea was to significantly reduce time spent in the field, using a number of resources, and increasing efficiency as economically as possible. The research design is based on the Design Science Research (DSR) concept as a methodological approach to design the solutions generated by means of 3D models. The digitalization of the campuses is based on the analysis, evolution and optimization of LiDAR ALS points clouds captured by government bodies, which are open access and free. Additional TLS capture techniques were used to complement the clouds, with the study of support of UAV-assisted automated photogrammetric techniques. The results show that with points clouds overlapped with 360 images, produced with a combination of resources and techniques, it was possible to reduce the on-site working time by more than two thirds. Keywords: LIDAR; TLS; UAV; point cloud; 3D modelling 1. Introduction The global environmental situation is critical, with the depletion of natural resources, global warming and CO 2 emissions leading to greater environmental awareness [ 1 ]. The building sector alone accounts for 18.4% of total anthropogenic greenhouse gas emissions [ 2 ]. It is necessary for cities and the built environment to fulfill their potential to enhance energy efficiency [ 3 ]. Digitalization, defined as the development and deployment of digital technologies and processes, is considered crucial for the required transformation of the construction industry to improve productivity according to the report of World Economic Forum [4]. In this sense, our research took two main lines from an architectural perspective: On the one hand, the optimization of the digital capture of a constructed setting [ 5 ], and the use of digital 3D models for environmental assessment of urban settings [ 6 ]. In early March 2020 , we commenced a research project that linked both areas of research. We were then faced with the crisis caused by the COVID-19 pandemic, which led to the state of national confinement decreed on 14 March 2020 in Spain [ 7 ], as was the case in many other countries. The first task of the project consisted of the digital capture of two large university campuses. This process usually entails a great deal of on-site work. Given the situation of confinement imposed by COVID-19, it was very difficult to spend long periods of time on site to take data readings. Mass capture of points in the urban settings was necessary, to obtain multiple data (coordinates, distances, surface areas, angles, temperatures of facades, etc. ), and this had to be performed at breakneck speed with the fewest possible resources. Buildings 2022,12, 380. https://doi.org/10.3390/buildings12030380 https://www.mdpi.com/journal/buildings Buildings 2022,12, 380 2 of 32 Several options had to be studied to find a combination of resources and techniques. The decision was made to start the work by using Light Amplification by Stimulated Emission of Radiation (LiDAR) points clouds, captured by manned Airborne Laser Scanning (ALS), carried out by the government in both cities (a resource that is free in many countries). The LiDAR ALS points clouds are an easily accessible and cheap resource, but their accuracy and performance need to be complemented by other capture techniques. The research conducted with the LiDAR ALS clouds, access to which is instant, open and free, including analysis, editing and optimization, enabled the additional techniques needed to complete the final points cloud of each campus to be estimated. In this, particular, case study, Terrestrial Laser Scanning (TLS) was considered to be the fastest option to complement the final points cloud of the urban area, extending the study to the support of automated photogrammetric techniques assisted by Unmanned Aerial Vehicle (UAV). Design Science Research (DSR) is a methodology that can provide solutions for research through the use of three-dimensional models. It employs techniques such as casestudies, data collection and document analysis, among others, and centers on creating and optimizing artifacts to improve processes and their operative performance [ 8 ]. With this methodology, research objectives are approached more pragmatically than in explanatory scientific investigation [9]. After the digital capture of the urban setting in 3D, the second phase of the project focused on the environmental assessment of the campuses. The tool used was Neighborhood Evaluation for Sustainable Territories (NEST), a tool based on life cycle evaluation methodology (ACV) [ 10 , 11 ]. Although some results of this assessment have already been published [ 12 ], this article does not focus on this phase, it only shows the minimum information necessary to give context to the research as a whole. This article focuses on the results of the optimization work performed on the digital capture of the two university campuses up to when the 3D simulation models are obtained. The conclusions include the result that after previously working with the LiDAR ALS points cloud of the Government of Navarra, the normal on-site reading period with TLS, estimated at 52 days for the campus of Pamplona, could be reduced to 7 days. The combination of devices, software and applications that were used made it possible to reduce the scanning time with overlapped 360 image capturing by more than 75%, in comparison to customary scanning times on the market. We found that even with such a fast capture time we were able to obtain errors of 1 mm, with a strength and overlap that was accepted as valid by the processing software. Therefore, this article could be of great benefit to the scientific community engaged in work of this nature, since it would help them to be more efficient and make effective use of resources. The article is structured as follows: The introduction consists of two sections. Section 1 contextualizes the research. Section 2describes the case study, including the current state of research into different capture techniques. Section 3describes the Methods and Materials. Section 4describes the results of generation of LiDAR point clouds, modelling and simulation. Section 5presents the Discussion, and the article ends with the final conclusions. 2. Digital Survey of Large Urban Areas in a Short Time, Study Cases The case studies focus on two universities in northern Spain: the campus of the University of the Basque Country (UPV-/EHU) in Donostia-San Sebastián (DSS) and the University of Navarra (UNAV) in Pamplona (Figure 1). The UNAV campus has an area of approximately 113 ha, which includes large open grassy areas, slightly wooded areas and a riverbed flanked by a dense mass of trees. The buildings cover only 6.8% of the total area. The UPV/EHU university campus in DSS has a much smaller area (approximately 18 ha) and a much higher building density, with a much lower proportion of green areas. As previously mentioned, the aim of this work is, to carry out the field work in the shortest time possible, with the fewest number of resources. Buildings 2022,12, 380 3 of 32 Buildings 2022, 12, x FOR PEER REVIEW 3 of 33 (a) (b) Figure 1. (a) Aerial view of the UNAV university campus in Pamplona; (b) aerial view of the UPV/EHU university campus in DSS. The UNAV campus has an area of approximately 113 ha, which includes large open grassy areas, slightly wooded areas and a riverbed flanked by a dense mass of trees. The buildings cover only 6.8% of the total area. The UPV/EHU university campus in DSS has a much smaller area (approximately 18 ha) and a much higher building density, with a much lower proportion of green areas. As previously mentioned, the aim of this work is, to carry out the field work in the shortest time possible, with the fewest number of resources. The modeling phase in NEST requires a prior graphic survey of the current state [13]. Depending on the reason for using the model, it will require a sufficient degree of precision to accurately determine the geometric configuration of buildings and their surroundings [14]. Although the NEST 3D model does not require excessive precision [15], the survey does require definition of the building envelopes [16], marking the number of floors, window configurations and opaque elements, and of the spaces occupied by roads and highways; green spaces and trees must also be defined [17], so that forest biomass can be calculated [18–20]. Given the limited time available for carrying out fieldwork, the use of massive point capture techniques will allow highly precise geometry to be obtained in digital format in a very short time. The combined use of digital geometric data collection techniques [21], is currently the most effective procedure for conducting a precise geo-referenced architectural survey [22]. In such cases, it includes a topographic survey with a total station [23], terrestrial laser scanner and short-range photogrammetry assisted by an RPA (RemotelyPiloted Aircraft) or UAV [24–27]. LiDAR technology also makes it possible to acquire massive amounts of 3D geospatial information in urban scenarios [28–31]. LiDAR technology measures the properties of reflected laser pulses to determine the range of a distant object [32]. That range is obtained by measuring the delay time between transmission of a laser pulse and detection of the reflected signal [33]. Due to LiDAR’s ability to generate 3D data with high precision and spatial resolution, a new era is opening for the development of research objectives such as the one presented [34]. LiDAR scanning can be classified in four categories: Satellite-based Laser Scanning (SLS), Airborne Laser Scanning (ALS) [35–38], Mobile Laser Scanning (MLS) and Terrestrial Laser Scanning (TLS) [39]. ALS is ideal for large areas of cities [40,41]; it can be conducted by UAVs or by manned aircraft, which usually fly at higher altitudes and capture larger areas than UAVs, though the latter are cheaper and less polluting, among other advantages [42–44]. SLS data points can be tens of meters apart and the respective point clouds are therefore unsuitable for extracting geometries from urban features such as buildings or masses of trees [45]. TLS data has the highest point density and can be used to specify data for those urban elements at individual level [46–48]. Some publications claim that TLS sometimes has poor mobility and occlusion issues that make Figure 1. ( a ) Aerial view of the UNAV university campus in Pamplona; ( b ) aerial view of the UPV/EHU university campus in DSS. The modeling phase in NEST requires a prior graphic survey of the current state [ 13 ]. Depending on the reason for using the model, it will require a sufficient degree of precision to accurately determine the geometric configuration of buildings and their surroundings [ 14 ]. Although the NEST 3D model does not require excessive precision [ 15 ], the survey does require definition of the building envelopes [ 16 ], marking the number of floors, window configurations and opaque elements, and of the spaces occupied by roads and highways; green spaces and trees must also be defined [ 17 ], so that forest biomass can be calculated [18–20]. Given the limited time available for carrying out fieldwork, the use of massive point capture techniques will allow highly precise geometry to be obtained in digital format in a very short time. The combined use of digital geometric data collection techniques [ 21 ], is currently the most effective procedure for conducting a precise geo-referenced architectural survey [ 22 ]. In such cases, it includes a topographic survey with a total station [ 23 ], terrestrial laser scanner and short-range photogrammetry assisted by an RPA (RemotelyPiloted Aircraft) or UAV [24–27]. LiDAR technology also makes it possible to acquire massive amounts of 3D geospatial information in urban scenarios [ 28 – 31 ]. LiDAR technology measures the properties of reflected laser pulses to determine the range of a distant object [ 32 ]. That range is obtained by measuring the delay time between transmission of a laser pulse and detection of the reflected signal [ 33 ]. Due to LiDAR’s ability to generate 3D data with high precision and spatial resolution, a new era is opening for the development of research objectives such as the one presented [ 34 ]. LiDAR scanning can be classified in four categories: Satellite-based Laser Scanning (SLS), Airborne Laser Scanning (ALS) [ 35 – 38 ], Mobile Laser Scanning (MLS) and Terrestrial Laser Scanning (TLS) [ 39 ]. ALS is ideal for large areas of cities [ 40 , 41 ]; it can be conducted by UAVs or by manned aircraft, which usually fly at higher altitudes and capture larger areas than UAVs, though the latter are cheaper and less polluting, among other advantages [ 42 – 44 ]. SLS data points can be tens of meters apart and the respective point clouds are therefore unsuitable for extracting geometries from urban features such as buildings or masses of trees [ 45 ]. TLS data has the highest point density and can be used to specify data for those urban elements at individual level [ 46 – 48 ]. Some publications claim that TLS sometimes has poor mobility and occlusion issues that make it difficult to collect data on an urban scale. When TLS is not effective, MLS has been used in some research, such as for collection and analysis of information on trees in urban areas [49,50]. Various techniques have been studied, and the method that best fit the objectives of this work involved the combination of different resources: ALS LiDAR clouds captured by public administrations, point clouds captured using TLS and, finally, point clouds produced from automated photogrammetry assisted by UAVs. Buildings 2022,12, 380 4 of 32 3. Methods and Materials In this section the technologies enabling the massive point capture to engender the point cloud of each university campus in a very short time [ 5 ], are explained, specifying the techniques, methods and materials used. The research design is based on DSR. DSR is used to design and assess manmade artifacts meant to resolve real-world problems [ 51 ]. This method helps find practical solutions for common problems affecting design, with a view to achieving expected results [ 8 ], and employs computer-based tools to streamline processes [ 51 ]. When a problem is associated to a physical object, the respective solution may appear as a 3D model, plan or drawing; when it requires optimizing an action, the solution may take the form of new digital software or be developed as a flowchart diagram [ 51 ]. DSR includes other non-habitual forms for conveying knowledge, such as models or constructs [ 52 ]. That is why DSR expresses knowledge based on different formats not commonly found in other scientific investigations, such as, for example, 3D models, architectures, design theories or principles and artifacts [ 53 ]. Two main activities are put forward in design science: to build the solution and to evaluate it [ 54 ]. The construct is a stage within the process of creating an artifact that can be used to resolve a specific problem. The evaluation is the action that must validate how effectively that artifact serves the purpose for which it was created. This is precisely what is going to be conducted in this investigation: to make, evaluate and optimize a 3D digital model of the urban area in the form of a point cloud with 360 image that contains all the information needed to achieve the project’s objectives. The construct stage must necessarily be iterative and incremental, as the evaluation phase will endow it with the feedback needed to optimize the solution. DSR enables relevant problems to be resolved based on applied research appearing in some scientific investigations linked to architecture [55]. 3.1. Analysis of LiDAR Clouds Obtained by Public Services In Spain, different public services offer that LiDAR data, thereby simplifying the data capture process for these kinds of projects, with the respective point clouds obtained using manned aircraft. The great advantage of these clouds is that they are public and can be consulted for free. In the case of point clouds obtained by ALS systems, the latest sensor technology has significantly increased the number of laser light beams per square meter. As a result, the density of the point clouds generated during the data collection process shows a range of between 12–30 points/m 2 , compared to the range of 1 point/m 2 obtained by previous sensors. The Chartered Community of Navarre was one of the first European regions to apply LiDAR technology using these new sensors, specifically the Leica Single Photon LiDAR (SPL100). A sensor is able to capture light particles with a laser light beam that can be divided into a 10 × 10 matrix, operating in practice as 100 sensors in parallel, each of which is captured by an independent channel of the detector. The experimental flights were conducted in 2017; after processing and classifying the data obtained using Artificial Intelligence (AI) techniques, it was possible to cover an area of 10,391 km 2 , generating meshes of 1 × 1 km, with a point cloud density of 14 points/m 2 and a precision of 20 cm on the XY axis and 15 cm on the Z axis. The Navarre government’s partial LiDAR clouds from 2017 were initially used, and a point cloud of the entire UNAV Campus in Pamplona was composed. Different cuts to the cloud were performed at strategic points; the precision and suitability of the cloud were also analyzed to study the combination of techniques (Figure 2). The campus cloud was segmented to form detailed sets of buildings and check their geometry with the density value of 14 points/m 2 . The definition of that façade would apparently suffice to obtain a 3D simulation model (Figure 3). Dimensional checks of the result will, subsequently, be carried out to ascertain which buildings need to complete the point cloud with other LiDAR techniques. Buildings 2022,12, 380 5 of 32 Buildings 2022, 12, x FOR PEER REVIEW 5 of 33 the cloud were performed at strategic points; the precision and suitability of the cloud were also analyzed to study the combination of techniques (Figure 2). (a) (b) Figure 2. LiDAR 2017 point cloud, density: 14 points/m 2 . UNAV campus: (a) 3D color cloud; (b) vertical section of the cloud through the north façade of the central campus building. The campus cloud was segmented to form detailed sets of buildings and check their geometry with the density value of 14 points/m 2 . The definition of that façade would apparently suffice to obtain a 3D simulation model (Figure 3). (a) (b) Figure 2. LiDAR 2017 point cloud, density: 14 points/m 2 . UNAV campus: ( a ) 3D color cloud; ( b ) vertical section of the cloud through the north façade of the central campus building. Buildings 2022, 12, x FOR PEER REVIEW 5 of 33 the cloud were performed at strategic points; the precision and suitability of the cloud were also analyzed to study the combination of techniques (Figure 2). (a) (b) Figure 2. LiDAR 2017 point cloud, density: 14 points/m 2 . UNAV campus: (a) 3D color cloud; (b) vertical section of the cloud through the north façade of the central campus building. The campus cloud was segmented to form detailed sets of buildings and check their geometry with the density value of 14 points/m 2 . The definition of that façade would apparently suffice to obtain a 3D simulation model (Figure 3). (a) (b) Figure 3. 2017 LiDAR cloud density: 14 points/m 2 . UNAV campus: ( a ) 3D detail of the central campus building; (b) north façade of the same building. To achieve the objectives of this research, other data of interest included the estimated approximate volume of the campuses’ forest biomass; detailed tests of tree masses were accordingly conducted. It was thereby possible to verify another fundamental characteristic of these new sensors, used in 2017. They enable capture of the terrestrial relief, devoid Buildings 2022,12, 380 6 of 32 of any artificial and/or natural element other than the ground (DTM—Digital Terrain Model), and of the earth’s surface with all built or natural bodies on it (DSM—Digital Surface Model). The use of specific wavelengths enables penetration between tree masses, capturing the lower ground level, which facilitates the height measurement of those masses. In this regard, an example of the capture of plant masses at the UNAV university campus in Pamplona is shown below. Cloud cuts were conducted in wooded areas. In the case of the densely populated vegetation zone, the scanner’s ability to penetrate the tree mass and record the ground level is observed [ 14 , 42 ], (Figure 4). Although the trees’ compactness makes it difficult to fully record the respective mass, the information captured allows for approximate measurements of the height and volume of the vegetation, with a precision that can be estimated to the nearest decimeter. Buildings 2022, 12, x FOR PEER REVIEW 6 of 33 Figure 3. 2017 LiDAR cloud density: 14 points/m2. UNAV campus: (a) 3D detail of the central campus building; (b) north façade of the same building. Dimensional checks of the result will, subsequently, be carried out to ascertain which buildings need to complete the point cloud with other LiDAR techniques. To achieve the objectives of this research, other data of interest included the estimated approximate volume of the campuses’ forest biomass; detailed tests of tree masses were accordingly conducted. It was thereby possible to verify another fundamental characteristic of these new sensors, used in 2017. They enable capture of the terrestrial relief, devoid of any artificial and/or natural element other than the ground (DTM—Digital Terrain Model), and of the earth’s surface with all built or natural bodies on it (DSM—Digital Surface Model). The use of specific wavelengths enables penetration between tree masses, capturing the lower ground level, which facilitates the height measurement of those masses. In this regard, an example of the capture of plant masses at the UNAV university campus in Pamplona is shown below. Cloud cuts were conducted in wooded areas. In the case of the densely populated vegetation zone, the scanner’s ability to penetrate the tree mass and record the ground level is observed [14,42], (Figure 4). Although the trees’ compactness makes it difficult to fully record the respective mass, the information captured allows for approximate measurements of the height and volume of the vegetation, with a precision that can be estimated to the nearest decimeter. (a) (b) Figure 4. UNAV campus, vegetation strip example: (a) 2017 LiDAR Cloud Plan, density: 14 points/m2; (b) profile of tree mass and level of ground under that mass. After analyzing the possibilities of the LiDAR cloud of the UNAV campus in Pamplona, with a density of 14 p/m2, the LiDAR clouds currently available for the territory of Gipuzkoa province, where the UPV/EHU campus is located in DSS, are analyzed. In that province, LiDAR clouds captured in 2012 and 2017 are currently available (Figure 5). Figure 4. UNAV campus, vegetation strip example: ( a ) 2017 LiDAR Cloud Plan, density: 14 points/m2; (b) profile of tree mass and level of ground under that mass. After analyzing the possibilities of the LiDAR cloud of the UNAV campus in Pamplona, with a density of 14 p/m 2 , the LiDAR clouds currently available for the territory of Gipuzkoa province, where the UPV/EHU campus is located in DSS, are analyzed. In that province, LiDAR clouds captured in 2012 and 2017 are currently available (Figure 5). The 2012 LiDAR flight presents meshes of 2 × 2 km with a density of 1 point/m 2 , while the 2017 LiDAR flight presents 500 × 500 m meshes with a density of 2.2 points/m 2 . As in the UNAV’s case, a partial cloud was created for the entire UPV/EHU campus, for both the 2012 and 2017 clouds. Partial sections of the campus buildings and trees were likewise made to compare the accuracy and usefulness of the clouds. Although specific measurements of these LiDAR clouds will be presented in the Section 4with the analysis of these two examples from the UPV/EHU campus, several limitations can be appreciated. In the 2012 cloud, total height of buildings could be obtained; however, the volumes of buildings are not intuited, nor are vertical stripes marked. In addition, the profile of tree masses presents excessively isolated points, and it cannot be determined whether they are masses or specific trees. In the 2017 cloud of the same campus, building heights are correctly appreciated, volumes are marked with vertical stripes and there is greater definition of tree masses. Even with this definition of 2.2 points/m 2 , building façades could not be modeled nor could biomass volumes be calculated, unlike what was seen in the cloud of the UNAV campus. Buildings 2022,12, 380 7 of 32 Buildings 2022, 12, x FOR PEER REVIEW 7 of 33 Figure 5. 2012 flight LiDAR point cloud of the UPV/EHU university campus in DSS. Cloud density: 1 point/m2. The 2012 LiDAR flight presents meshes of 2 × 2 km with a density of 1 point/m2, while the 2017 LiDAR flight presents 500 × 500 m meshes with a density of 2.2 points/m2. As in the UNAV’s case, a partial cloud was created for the entire UPV/EHU campus, for both the 2012 and 2017 clouds. Partial sections of the campus buildings and trees were likewise made to compare the accuracy and usefulness of the clouds. Although specific measurements of these LiDAR clouds will be presented in the results section with the analysis of these two examples from the UPV/EHU campus, several limitations can be appreciated. In the 2012 cloud, total height of buildings could be obtained; however, the volumes of buildings are not intuited, nor are vertical stripes marked. In addition, the profile of tree masses presents excessively isolated points, and it cannot be determined whether they are masses or specific trees. In the 2017 cloud of the same campus, building heights are correctly appreciated, volumes are marked with vertical stripes and there is greater definition of tree masses. Even with this definition of 2.2 points/m2, building façades could not be modeled nor could biomass volumes be calculated, unlike what was seen in the cloud of the UNAV campus. The precision of these clouds will condition subsequent data collection at the two campuses to complete the point cloud that allows the 3D simulation model to be achieved. 3.2. Data Collection to Complete the LiDAR, MLS and TLS Clouds TLS and MLS technologies make it possible to obtain highly accurate point clouds. However, managing those technologies to capture urban environments of a certain size requires in-depth study to ensure that they can be effective, sustainable and relatively cheap. MLS scanners are generally much more expensive than TLS scanners. However, depending on the urban environment, they can reduce execution times and therefore be a more efficient option. In any case, the scanning of such areas must be planned very well so that the point clouds are not excessively dense and can be handled by standard hardware. Regarding the MLS LiDAR options, some work has opted for models, such as the Leica Pegasus, which allows reality to be captured from a vehicle, train or ship. It is an expensive option that requires very specific capture conditions for large areas in cities, though it is very useful for capturing linear infrastructures. Was not considered due to the characteristics of the two campuses. One option that was tested is the Leica BLK2GO handheld scanner, which captures moving images and point clouds in real time, using SLAM (Simultaneous Localization and Mapping) technology to record their course through space [56,57]. That scanner combines dual-axis LiDAR, a 4.3 Mpx 360° panoramic Figure 5. 2012 flight LiDAR point cloud of the UPV/EHU university campus in DSS. Cloud density: 1 point/m2. The precision of these clouds will condition subsequent data collection at the two campuses to complete the point cloud that allows the 3D simulation model to be achieved. 3.2. Data Collection to Complete the LiDAR, MLS and TLS Clouds TLS and MLS technologies make it possible to obtain highly accurate point clouds. However, managing those technologies to capture urban environments of a certain size requires in-depth study to ensure that they can be effective, sustainable and relatively cheap. MLS scanners are generally much more expensive than TLS scanners. However, depending on the urban environment, they can reduce execution times and therefore be a more efficient option. In any case, the scanning of such areas must be planned very well so that the point clouds are not excessively dense and can be handled by standard hardware. Regarding the MLS LiDAR options, some work has opted for models, such as the Leica Pegasus, which allows reality to be captured from a vehicle, train or ship. It is an expensive option that requires very specific capture conditions for large areas in cities, though it is very useful for capturing linear infrastructures. Was not considered due to the characteristics of the two campuses. One option that was tested is the Leica BLK2GO handheld scanner, which captures moving images and point clouds in real time, using SLAM (Simultaneous Localization and Mapping) technology to record their course through space [ 56 , 57 ]. That scanner combines dual-axis LiDAR, a 4.3 Mpx 360 ◦ panoramic viewing system, a 12 Mpx high-resolution camera for detailed photos and an inertial measurement unit that enables self-navigation, capturing 420,000 pts/s with a capture range of 0–25 m. System performance based on SLAM technology offers 6–15 mm relative accuracy and 20 mm absolute positioning accuracy for maximum range [ 5 ]. Considering the size and characteristics of the campuses, the scanning process with this device was ruled out both due to execution times and the excessive amount of information that would be captured. Bearing in mind the accuracy of the LiDAR clouds discussed in the previous point, TLS was deemed the fastest, most efficient and least problematic option to complete the clouds, although it is true that specific locations can be complemented with captures made using UAVs. The geometric data capture technique using terrestrial laser scanning allows this capture to be performed quickly and expeditiously, capturing a large amount of information at very high speed, from medium and long distances and with a high degree of accuracy. The generated high-density point cloud can be supplemented by 360 ◦ panoramic photography taken at each scan position. The point cloud with the overlapping 360 image, makes it possible to configure a three-dimensional visual environment wherein it is feasible to make millimetric measurements and develop virtual visits. Some scanners also have Buildings 2022,12, 380 8 of 32 a built-in thermographic camera that can discern the temperature of each of the millions of points captured when scanning. The temperature of the facades is of special interest in this type of project in which energy improvement is proposed by means of passive solutions such as energy-minded reform of building façades. The five methodological stages followed when scanning the two campuses with TLS will be described next. 3.2.1. Survey of Control Points in UTM Coordinates Using a Total Station The main objective of the control point system is to obtain a three-dimensional digital model of the geo-referenced survey in absolute UTM coordinates. Obtaining a georeferenced model is not an essential requirement in cases where the data collection procedure is accomplished by laser scanning, since a local coordinate system can be used. However, this information’s implementation in the photogrammetric processing ensures greater accuracy of the three-dimensional digital model. Moreover, the control points guarantee the rigor and accuracy of the data processing and facilitates the union between different captures. The materialization of those points is performed using adhesive targets for fixation on the different supports, in the form of rigid plates of variable size. The control points, located on the ground, are permanently referenced by topographic nails for their maintenance during execution of the work. The checkpoint system layout follows the following criteria: • Link checkpoints, for joining point clouds corresponding to the different laser scans. Located on vertical walls of the façade and meant to cover the maximum possible width, both vertically and horizontally. Some of these control points, if located on vertical roof faces, can help facilitate the union between the data captured by laser scanner and the UAV-assisted photographic capture; • Checkpoints on the roof, the topographical targets that we usually place to give more precision to the photogrammetric work of the UAV in relation to the work of the laser scanner. We placed them at the ends of the roofs at different heights (especially on the campus buildings that had flat roofs with several volumes of different heights). That way some targets are captured by the 3D laser scanner and by the UAV, and this facilitates the union between points clouds; • Checkpoints on the ground, common to both data collection procedures for later integration. Situated in such a way that they are recorded by both laser scanning and UAV-assisted photographic capture. 3.2.2. Scanning Plan For the scanning process to be effective, it is recommended that a prior study be conducted of the scan positions for the set to be captured. With respect to university campuses, the capture focused on two important aspects: • The exterior survey of each campus’s buildings, both in cloud format and in a 360◦image , to obtain a multitude of data so that the 3D simulation model could be created from the office without having to venture into the field; • Registration of the buildings’ external environment, where the main aim focused on capturing the green areas with more or less trees, to record and measure the amount of available forest Biomass. Because the situation generated by the COVID-19 health crisis meant that movement was very limited, optimizing the fieldwork was vitally important, it meant to reduce the scanning process to the shortest time possible. To be efficient, it was essential to study the campuses’ cartography before proposing a scanning plan. The UNAV campus in Pamplona, has terrain with large slopes in some areas and that this needs to be taken into account when preparing the scan position plan. If a plan is drawn up without taking the slopes into account, as if the terrain were flat, the distances between scan positions are horizontal projections of the actual distance. The research focus in this article is on reducing the number of scan points as much as possible, therefore, the need to take the slopes into account in the scan plan is an important one. Buildings 2022,12, 380 9 of 32 As mentioned above, the two campuses have very different characteristics that affect the scanning plan. The Donostia campus is a relatively flat urban campus without large areas of trees; it is therefore practically possible to arrange its scanning plan using an orthophoto. The work was divided into three different areas that cover the entire UPV/EHU campus. However, the Pamplona campus is overwhelmingly complex. The extent of the campus and the large number of buildings are a challenge that cannot be practically covered in a short time by a terrestrial laser scanner. In many cases, the terrain’s unevenness exceeds the height of buildings, which are located at very different heights and very far apart. Furthermore, the medium-height vegetation and above all the large trees in many cases prevent the capture of many building façades’ geometry. If it were not for the high quality of the 2017 LiDAR clouds, this would be an overwhelming task in a short period of time, even using a scanner as versatile as the RTC 360. On the UNAV campus, simplified CAD planimetry was used to conduct a prior study of possible scan positions. Considering the high number of scan positions and the large area covered by the cloud take, it was decided to divide the work into seven zones. However, this depends a great deal on the power of the computer that will be used when processing the clouds. 3.2.3. Laser Scanning, Pre-Processed in the Field with Mobile Devices The building façades on the different campuses were scanned, as well as the exterior environments, with special attention given to green areas and vegetation. In the survey of the buildings the main objective was to measure the façades’ dimensions, differentiating the sizes of window openings and opaque surfaces. A minimum of three scans were conducted for each façade of the campus buildings. Diagonal scans were also performed to capture the internal faces of the façades. Depending on the distance between buildings, the remaining scans were distributed on the ground. A color point cloud treatment was conducted, since a 360 ◦ panoramic view was also captured at each scan point. Two terrestrial laser scanners were used: an RTC 360 and a BLK 360, both from Leica Geosystems (Figure 6). They stand out due to three characteristics: they are extremely light, it is not necessary to spend time levelling and they capture 360 ◦ spherical images in HDR in a short time, generating a color point cloud [ 23 ]. We have worked with scanners of other brands, and we are aware that in such cases capture with a 360 HDR image under 8 min is complicated. The two Leica scanners used in this study enabled the project objectives to be achieved. The characteristics and features of the devices are very important in enabling us to obtain the results mentioned in the article. Buildings 2022, 12, x FOR PEER REVIEW 10 of 33 be achieved. The characteristics and features of the devices are very important in enabling us to obtain the results mentioned in the article. The first one was the BLK360, which has a registry range of 60 m and gathers 360,000 points/second. It has a pair of special features that make it very interesting. One is that it has a 360 thermal camera that is very useful for sustainability and energy efficiency issues. The other is that it is very small and only weighs 1 kg. It acted as a complement in some specific tasks for the second scanner, which was the main device used in field capture. The second device, the RTC 360, has a registry range of up to 130 m, which enables it to cover large areas. It is especially interesting from an energy perspective, because it has VIS technology that enables it to automatically register device displacement without targets from one scanning point to another, so that the partial points clouds are registered in a special location related to the other scans. The device’s measuring rate is up to 2 million points/second, and for high resolution, (3 mm@10 m), it can scan in 1:42 min without HDR. Bearing in mind that medium resolution is often enough in many cases, a point cloud with 360 image can be obtained in a scan of less than 2 min. We can safely say that this is “little time” in comparison to other scanners. In fact, we have checked and found that the 360image captured with other 3D laser scanners in 8 min is of poorer quality than the one captured in one minute with these scanners. (a) (b) Figure 6. Scanners used: (a) RTC 360; (b) BLK 360. Unlike other scanners, the BLK and the RTC enable pre-processing work to be performed on site, via a mobile device (tablet or mobile phone), thanks to the Leica Cyclone FIELD 360 application. While the scanner is capturing points, we can check the results obtained and move forward with the processing work, uniting the points clouds of each scan position. These scanners transmit a Wi-Fi network that enables the mobile device to be linked to the scanner, so that all the scanner data can be transferred in real time to the Cyclone FIELD 360 application. Any tablet or mobile phone that uses iOS or Android can be used for this purpose. Represents an advance that further streamlines work, besides enabling the campus digitalization work to be evaluated, optimized and validated to obtain the points cloud in 3D. It also enables the basics of the DSR method to be complied with: implement, evaluate and optimize. The scan positions pre-processing app has several work tabs. In the “map” format the clouds are joined in a plan following the “cloud-to-cloud” method, and the accuracy of the union in plan, section and perspective can be consulted. The union must always be conducted between two nearby point clouds, which will be shown in two different colors to facilitate the process (usually orange and blue-cyan). The “360” section allows immersion in each scan point, to view details. Lastly, a specific cloud or the assembled set of clouds can be viewed in 3D (Figure 7). Figure 6. Scanners used: (a) RTC 360; (b) BLK 360. The first one was the BLK360, which has a registry range of 60 m and gathers 360,000 points/second . It has a pair of special features that make it very interesting. One is that it has a 360 thermal camera that is very useful for sustainability and energy efficiency issues. The other is that it is very small and only weighs 1 kg. It acted as a complement in Buildings 2022,12, 380 16 of 32 Table 4. Cont. Work Phase Software Scan Data Import Import Formats Export Formats Results visualization TruView * [72] Only Leica LGS Almost any format: cloud of points and geometries. Accuracy reports. 360 images. Jetstream Viewer * [73] Only Leica LGS Point information, CAD formats (free) ReCap Autodesk [71] Independent XYZ, E57 Autocad (DWG), E57 PTS RCP/RCS * Leica. 4. Results Although part of the results were shown in the previous section to enable better and more graphic understanding of the methodological process, this section focuses on the partial results that will allow the 3D simulation model to be obtained. Some quantitative data of the LiDAR cloud, the error from the union of the TLS clouds and the UAV flight operation will be shown. In addition, graphic results of the cloud will be explained, so that the way data is extracted to generate the 3D model can be checked. Finally, some values from the environmental assessment will be presented, though these are not the direct objective of this publication. 4.1. ALS LiDAR Clouds In this section, the results of the ALS LiDAR point cloud produced using the LiDAR of public services involved in the project will be analyzed. Two main aspects will be studied: the cloud’s suitability for modeling the façades of campus buildings, considering that the buildings’ volumes, the windows and opaque parts of façades must be defined; and the suitability for calculating biomass volumes by making sections of trees masses in the cloud. As for the campus buildings, twenty buildings were evaluated at the UPV/EHU campus in DSS, while 31 were evaluated at the UNAV campus in Pamplona [ 12 ]. Before determining the scanning plan strategy for TLS, the 51 buildings had to be analyzed by making partial sections of the ALS cloud. Below is an example of measurements made in a building on the UNAV campus, where the cloud has a density of 14 pts/m2(Figure 11). Buildings 2022, 12, x FOR PEER REVIEW 17 of 33 values from the environmental assessment will be presented, though these are not the direct objective of this publication. 4.1. ALS LiDAR Clouds In this section, the results of the ALS LiDAR point cloud produced using the LiDAR of public services involved in the project will be analyzed. Two main aspects will be studied: the cloud’s suitability for modeling the façades of campus buildings, considering that the buildings’ volumes, the windows and opaque parts of façades must be defined; and the suitability for calculating biomass volumes by making sections of trees masses in the cloud. As for the campus buildings, twenty buildings were evaluated at the UPV/EHU campus in DSS, while 31 were evaluated at the UNAV campus in Pamplona [12]. Before determining the scanning plan strategy for TLS, the 51 buildings had to be analyzed by making partial sections of the ALS cloud. Below is an example of measurements made in a building on the UNAV campus, where the cloud has a density of 14 pts/m 2 (Figure 11). (a) (b) Figure 11. 2017 LiDAR cloud, density: 14 points/m 2 . UNAV campus: (a) measurements on the point cloud of the campus’s central building; (b) graphic survey of the same building façade. As an example, a series of basic measurements were made to calculate building height, façade surface and openings on the north façade of the central building at the UNAV university campus in Pamplona. The point density value of 14 p/m 2 allows approximate measurements of part of the building elements to be obtained, whose accuracy may sometimes be sufficient for the 3D simulation model. The results obtained in the example are shown below (Table 5). Table 5. Example of façade measurement table in the ALS LiDAR cloud. Facade Length Height to Eave Facade Surface Window Dimensions F0/F1 Window Dimensions F2/F3 51.90 m. 15.50 m. 804.45 m 2 1.55 × 1.35 m. 1.55 × 2.30 m. It was thus possible to obtain measurements for all buildings on the UNAV campus; if any additional measurement was necessary, supplementary measurements from the Figure 11. 2017 LiDAR cloud, density: 14 points/m 2 . UNAV campus: ( a ) measurements on the point cloud of the campus’s central building; (b) graphic survey of the same building façade. Buildings 2022,12, 380 17 of 32 As an example, a series of basic measurements were made to calculate building height, façade surface and openings on the north façade of the central building at the UNAV university campus in Pamplona. The point density value of 14 p/m 2 allows approximate measurements of part of the building elements to be obtained, whose accuracy may sometimes be sufficient for the 3D simulation model. The results obtained in the example are shown below (Table 5). Table 5. Example of façade measurement table in the ALS LiDAR cloud. Facade Length Height to Eave Facade Surface Window Dimensions F0/F1 Window Dimensions F2/F3 51.90 m. 15.50 m. 804.45 m21.55 ×1.35 m. 1.55 ×2.30 m. It was thus possible to obtain measurements for all buildings on the UNAV campus; if any additional measurement was necessary, supplementary measurements from the cloud obtained with TLS techniques was used. On the UPV/EHU campus, the LiDAR clouds in Gipuzkoa province were not able to obtain the same results. It was only possible to obtain measurements of façades (height and width), but not of window opening sizes. To conduct that, a more exhaustive capture had to be performed with TLS. The data used to model buildings on the UPV/EHU campus in DSS was extracted directly from the LiDAR clouds obtained with TLS techniques. The results were also analyzed to obtain the campuses’ approximate forest biomass volume. The measurement of an isolated tree or vegetation element will be used here as an example, starting with analysis of the suitability of the LiDAR clouds at the UPV/EHU campus. In Figure 6, the 2012 cloud barely shows points of vegetation or soil. The 2017 cloud of the two campuses is analyzed in comparison (Figures 12 and 13). Buildings 2022, 12, x FOR PEER REVIEW 18 of 33 cloud obtained with TLS techniques was used. On the UPV/EHU campus, the LiDAR clouds in Gipuzkoa province were not able to obtain the same results. It was only possible to obtain measurements of façades (height and width), but not of window opening sizes. To conduct that, a more exhaustive capture had to be performed with TLS. The data used to model buildings on the UPV/EHU campus in DSS was extracted directly from the LiDAR clouds obtained with TLS techniques. The results were also analyzed to obtain the campuses’ approximate forest biomass volume. The measurement of an isolated tree or vegetation element will be used here as an example, starting with analysis of the suitability of the LiDAR clouds at the UPV/EHU campus. In Figure 6, the 2012 cloud barely shows points of vegetation or soil. The 2017 cloud of the two campuses is analyzed in comparison (Figures 12 and 13). Figure 12. Isolated vegetation element. 2017 LiDAR cloud, density: 2.2 points/m 2 . UPV/EHU campus in DSS. In the cloud in Figure 12, the terrain’s configuration can be observed, though it is difficult to measure tree volume. After making several measurements in that LiDAR cloud, the estimate obtained has a maximum precision of 30 cm in the XY axes and 20 cm in the Z axis. In contrast, it was verified that in the LiDAR cloud of the UNAV campus in Pamplona, the tree masses have enough precision to make measurements. In Figure 13 the tree’s height is exactly 22.45 m. Figure 13. 2017 flight LiDAR point cloud, density: 14 points/m 2 . Isolated vegetation element where the complete section of its mass is observed, as well as level of the ground. Many publications present multiple ways to calculate tree volumes [74]. Considering the requirements of the NEST evaluation software, calculations have been performed in two ways: for isolated trees such as the one in the example, their volume is assimilated to Figure 12. Isolated vegetation element. 2017 LiDAR cloud, density: 2.2 points/m 2 . UPV/EHU campus in DSS. In the cloud in Figure 12, the terrain’s configuration can be observed, though it is difficult to measure tree volume. After making several measurements in that LiDAR cloud, the estimate obtained has a maximum precision of 30 cm in the XY axes and 20 cm in the Z axis. In contrast, it was verified that in the LiDAR cloud of the UNAV campus in Pamplona, the tree masses have enough precision to make measurements. In Figure 13 the tree’s height is exactly 22.45 m. Many publications present multiple ways to calculate tree volumes [ 74 ]. Considering the requirements of the NEST evaluation software, calculations have been performed in two ways: for isolated trees such as the one in the example, their volume is assimilated to a cone, cylinder or sphere [ 75 ]. In Figure 13b it is assimilated to a cone. The tree’s total height and the base of the branches are measured; the volume of the cone is then calculated. For continuous masses, such as in Figure 4, partial sections of the cloud are used, and the approximate volume is extracted considering the contours of the mass. To Buildings 2022,12, 380 18 of 32 calculate the final volume of the campus’s biomass, that tree data must be completed with the volumes of green areas associated to the terrain’s green surfaces. In the case of the UNAV campus, its total area was calculated as being 1,547,278 m 2 , with a green space surface area of 1,082,210 m2 , accounting for 70% of the total. At the UPV/EHU campus in DSS, the total area of the campus was estimated to be 565,140 m 2 , with a green space area of 168,816 m 2 , accounting for approximately 30% of the total. Numerous publications have explained different ways of calculating forest biomass [ 75 – 77 ]. With this data it was possible to feed the NEST model to carry out the evaluation. Buildings 2022, 12, x FOR PEER REVIEW 18 of 33 cloud obtained with TLS techniques was used. On the UPV/EHU campus, the LiDAR clouds in Gipuzkoa province were not able to obtain the same results. It was only possible to obtain measurements of façades (height and width), but not of window opening sizes. To conduct that, a more exhaustive capture had to be performed with TLS. The data used to model buildings on the UPV/EHU campus in DSS was extracted directly from the LiDAR clouds obtained with TLS techniques. The results were also analyzed to obtain the campuses’ approximate forest biomass volume. The measurement of an isolated tree or vegetation element will be used here as an example, starting with analysis of the suitability of the LiDAR clouds at the UPV/EHU campus. In Figure 6, the 2012 cloud barely shows points of vegetation or soil. The 2017 cloud of the two campuses is analyzed in comparison (Figures 12 and 13). Figure 12. Isolated vegetation element. 2017 LiDAR cloud, density: 2.2 points/m 2 . UPV/EHU campus in DSS. In the cloud in Figure 12, the terrain’s configuration can be observed, though it is difficult to measure tree volume. After making several measurements in that LiDAR cloud, the estimate obtained has a maximum precision of 30 cm in the XY axes and 20 cm in the Z axis. In contrast, it was verified that in the LiDAR cloud of the UNAV campus in Pamplona, the tree masses have enough precision to make measurements. In Figure 13 the tree’s height is exactly 22.45 m. Figure 13. 2017 flight LiDAR point cloud, density: 14 points/m 2 . Isolated vegetation element where the complete section of its mass is observed, as well as level of the ground. Many publications present multiple ways to calculate tree volumes [74]. Considering the requirements of the NEST evaluation software, calculations have been performed in two ways: for isolated trees such as the one in the example, their volume is assimilated to Figure 13. 2017 flight LiDAR point cloud, density: 14 points/m 2 . Isolated vegetation element where the complete section of its mass is observed, as well as level of the ground. 4.2. TLS LiDAR Clouds The measurements that could not be obtained from the ALS LiDAR cloud were made from the cloud supplemented by TLS techniques. All kinds of geometric data (lengths, angles, areas, etc.) and thermal data of the points in the cloud can be extracted from that cloud using laser scanning. At the end of this section, in the visualization part of the resulting clouds, some examples of quantitative results in cloud measurements can be seen. Although we have shown that the field work in 7 days enabled enough results to be obtained to enter all the necessary data in NEST, we shall show how the previous estimate of 7 days was calculated. The basis for everything is to draw up different scanning plans, placing the scan positions at different distances. The lesser the distance, the more scan points there are, and therefore, more scanning time. The scanning time can be calculated with the data we show in response 2.6. We could estimate a mean scanning time of 2 min (the maximum would be 2:42 with an HDR image in color). 3D laser scanners present data at resolutions or precisions expressed at 10 m. In the case of the RTC360, with a measurement rate of up to 2 million points/second, the data is as follows: For high resolution, (3 mm@10 m); for medium resolution, (6 mm@10 m); for low resolution, (12 mm@10 m). If we want to make sure of these resolutions, the logical thing to do is establish a scanning plan with the scan points at 10 m. However, such a decision would mean that the scan with TLS would take much longer. The UNAV campus is 113 Ha., 70% are green spaces, 10% is occupied by buildings and the other 20% consists of car parks, roads, etc. Three calculation scenarios were established: In (I) we set out to estimate a scanning plan with a benchmark distance of 10 m. 100 scan positions/Ha . were estimated in the green spaces, making a total of 7910 scan/pos. 31 buildings associated with the campus and its activities had to be modelled and simulated. Only the exterior geometry of the facades had to be captured. In all, 775 scans were estimated for the facade perimeters (an average of 25 scans per building). The other areas, (parking, roads, etc.) were not as important for the environmental evaluation of the model, although they take up a lot of surface area, and so a total of 678 scan/pos was estimated. The total scan positions was 9363 that, with a scanning time of 2:42 min, made for a total of Buildings 2022,12, 380 19 of 32 52 days of field work without including the displacements of the scanner from one position to another. In scenario (II) we estimated a scanning plan at 33 m in the green spaces, with an estimate of 16 scan positions per Ha. with a total of 1265 scan/pos. Scan positions were planned at 30 m in the building perimeters, but with a minimum of 3 scans per facade. 258 scan/pos were calculated for the characteristics of the buildings. In all, 339 scan/pos were estimated for the rest. The total number of scan positions was 1862, which with a scanning time of 2:42 min makes a total of 10.5 days of work without including the displacements of the scanner from one position to another. This would mean over 2 weeks’ work. In scenario (III) we estimated a scanning plan in the green spaces at distances under 50 m , with an estimate of 8 scan positions per Ha. with a total of 632 scan/pos. The previous plan of 30 m with 258 scan/pos was maintained for the building perimeters. In all, 169 scan/pos were estimated for the rest. The total number of scan positions was 1059, which with a scanning time of 2:42 min makes a total of 6 days field work. Displacements of the scanner from one scan/position to another (a minimum estimate of 30 s) adds one more day. This makes for a total of 7 days. The estimates calculated for working days of 8 h/day , although in early April there was more than 12 h of natural light a day, which gave a degree of margin for contingencies in the 7 days. After presenting the estimation of the reduction of scan process in the field, next section shows how the cloud processing results obtained with TLS can be improved. Since the LiDAR clouds already have basic information on which to add the TLS clouds, the error and overlap of the resulting clouds is not the same as when scanning a single building using only TLS techniques. The main accuracy parameters to upgrade are: set error, overlap, strength of link and cloud-to-cloud error. An example of the direct result after the scanning and pre-processing phase of Zone 2 of the UPV/EHU campus in DSS is shown (Figure 14). Buildings 2022, 12, x FOR PEER REVIEW 20 of 33 In scenario (III) we estimated a scanning plan in the green spaces at distances under 50 m, with an estimate of 8 scan positions per Ha. with a total of 632 scan/pos. The previous plan of 30 m with 258 scan/pos was maintained for the building perimeters. In all, 169 scan/pos were estimated for the rest. The total number of scan positions was 1059, which with a scanning time of 2:42 min makes a total of 6 days field work. Displacements of the scanner from one scan/position to another (a minimum estimate of 30 s) adds one more day. This makes for a total of 7 days. The estimates calculated for working days of 8 h/day, although in early April there was more than 12 h of natural light a day, which gave a degree of margin for contingencies in the 7 days. After presenting the estimation of the reduction of scan process in the field, next section shows how the cloud processing results obtained with TLS can be improved. Since the LiDAR clouds already have basic information on which to add the TLS clouds, the error and overlap of the resulting clouds is not the same as when scanning a single building using only TLS techniques. The main accuracy parameters to upgrade are: set error, overlap, strength of link and cloud-to-cloud error. An example of the direct result after the scanning and pre-processing phase of Zone 2 of the UPV/EHU campus in DSS is shown (Figure 14). Figure 14. Review and optimization of the scan of the Donostia-San Sebastián campus in plan, using the Leica Cyclone Register, Zone 2. As detailed previously, the processing phase has several stages separated in four tabs in the software. The first allows the data collected on-site to be imported. In this case, as pre-processing or pre-registration has been conducted, the scan points appear linked in the import from Cyclone FIELD 360 to Leica Cyclone REGISTER 360. At this point, the processing software analyzes the joint data in the field and assigns a color to each joint based on its strength and accuracy. Green indicates the highest strength and red the lowest strength; two other colors, yellow and blue, are in between. In the case of Figure 16, the input data for the processing marks the following results: set error 1 mm, overlap 33%, strength 34%, cloud-to-cloud error 1 mm. This data could be optimized in REGISTER 360 by optimizing the cloud-to-cloud joints, though the 1 mm assembly error is more than enough to meet the needs of the 3D model in NEST. Furthermore, in environments with large vegetation, although the scan’s accuracy is high (1–3 mm error), it may happen that the overlap between clouds is not as appropriate, due to the singularities of moving branches and leaves (Figure 15). Figure 14. Review and optimization of the scan of the Donostia-San Sebastián campus in plan, using the Leica Cyclone Register, Zone 2. As detailed previously, the processing phase has several stages separated in four tabs in the software. The first allows the data collected on-site to be imported. In this case, as pre-processing or pre-registration has been conducted, the scan points appear linked in the import from Cyclone FIELD 360 to Leica Cyclone REGISTER 360. At this point, the processing software analyzes the joint data in the field and assigns a color to each joint based on its strength and accuracy. Green indicates the highest strength and red the lowest strength; two other colors, yellow and blue, are in between. In the case of Figure 16, the input data for the processing marks the following results: set error 1 mm, overlap 33%, strength 34%, cloud-to-cloud error 1 mm. This data could be optimized in REGISTER 360 by optimizing the cloud-to-cloud joints, though the 1 mm assembly error is more than enough Buildings 2022,12, 380 20 of 32 to meet the needs of the 3D model in NEST. Furthermore, in environments with large vegetation, although the scan’s accuracy is high (1–3 mm error), it may happen that the overlap between clouds is not as appropriate, due to the singularities of moving branches and leaves (Figure 15). Buildings 2022, 12, x FOR PEER REVIEW 21 of 33 Figure 15. TLS LiDAR clouds in the tree-lined area of the UPV/EHU DSS campus. Forest biomass data can be obtained from these clouds, which offset the lack of ALS LiDAR clouds. At the UNAV campus, the processing stage was very similar to that of the Donostia campus. Although, since the ALS LiDAR cloud is much more accurate, the scanning points per m 2 of campus are much lower. Considering also that 70% of the campus comprises green surfaces, means that there are no very reliable references between adjoining clouds. After the scan data dump before processing, the initial set therefore had much fewer joints in green (because of that lack of strength and overlap). Although the error was acceptable (2 mm), initially, the force was only 22% and the overlap 19%. Indeed, in some areas there is a previous joint that the software stopped linking so that it could be studied and improved during processing (Figure 16). Figure 16. 3D point cloud. Review and optimization of scan of the UPV/EHU Campus, DSS, Zone 1. Description of the joints before the processing and optimization stage. As the scan positions have been created at longer distances than usual to streamline the process, the pre-registration work conducted on site undergoes a revision. Since image 16 is in perspective, it is harder to see the color of the unions between scan points. When the data obtained in the field is imported, the software analyzes whether the points of a cloud of a scan position overlap enough with those of the previous and posterior clouds. The software checks and colors the unions in line with three concepts: Error, strength of union and overlap between clouds. If they have any undesirable parameters, the may color them in red, yellow or blue or it may not directly propose the union because it is outside a minimum range. What happens then, is that an optimization process commences analyses and improves the cloud-to-cloud union (as in Figure 8), between the two clouds that do not have a connection line in green (Figures 14 and 16). If we can improve the overlap parameters in this optimization process, the line of union changes to green and it is accepted as valid. If we cannot change the union to green in the optimization process, there is still the option of carrying out another scan on site the next day and further strengthening the cloud of the set. This means that the field work should be processed every day, and so it is importance to use the tools mentioned in this article. The devices, Figure 15. TLS LiDAR clouds in the tree-lined area of the UPV/EHU DSS campus. Forest biomass data can be obtained from these clouds, which offset the lack of ALS LiDAR clouds. At the UNAV campus, the processing stage was very similar to that of the Donostia campus. Although, since the ALS LiDAR cloud is much more accurate, the scanning points per m 2 of campus are much lower. Considering also that 70% of the campus comprises green surfaces, means that there are no very reliable references between adjoining clouds. After the scan data dump before processing, the initial set therefore had much fewer joints in green (because of that lack of strength and overlap). Although the error was acceptable (2 mm), initially, the force was only 22% and the overlap 19%. Indeed, in some areas there is a previous joint that the software stopped linking so that it could be studied and improved during processing (Figure 16). Buildings 2022, 12, x FOR PEER REVIEW 21 of 33 Figure 15. TLS LiDAR clouds in the tree-lined area of the UPV/EHU DSS campus. Forest biomass data can be obtained from these clouds, which offset the lack of ALS LiDAR clouds. At the UNAV campus, the processing stage was very similar to that of the Donostia campus. Although, since the ALS LiDAR cloud is much more accurate, the scanning points per m 2 of campus are much lower. Considering also that 70% of the campus comprises green surfaces, means that there are no very reliable references between adjoining clouds. After the scan data dump before processing, the initial set therefore had much fewer joints in green (because of that lack of strength and overlap). Although the error was acceptable (2 mm), initially, the force was only 22% and the overlap 19%. Indeed, in some areas there is a previous joint that the software stopped linking so that it could be studied and improved during processing (Figure 16). Figure 16. 3D point cloud. Review and optimization of scan of the UPV/EHU Campus, DSS, Zone 1. Description of the joints before the processing and optimization stage. As the scan positions have been created at longer distances than usual to streamline the process, the pre-registration work conducted on site undergoes a revision. Since image 16 is in perspective, it is harder to see the color of the unions between scan points. When the data obtained in the field is imported, the software analyzes whether the points of a cloud of a scan position overlap enough with those of the previous and posterior clouds. The software checks and colors the unions in line with three concepts: Error, strength of union and overlap between clouds. If they have any undesirable parameters, the may color them in red, yellow or blue or it may not directly propose the union because it is outside a minimum range. What happens then, is that an optimization process commences analyses and improves the cloud-to-cloud union (as in Figure 8), between the two clouds that do not have a connection line in green (Figures 14 and 16). If we can improve the overlap parameters in this optimization process, the line of union changes to green and it is accepted as valid. If we cannot change the union to green in the optimization process, there is still the option of carrying out another scan on site the next day and further strengthening the cloud of the set. This means that the field work should be processed every day, and so it is importance to use the tools mentioned in this article. The devices, Figure 16. 3D point cloud. Review and optimization of scan of the UPV/EHU Campus, DSS, Zone 1. Description of the joints before the processing and optimization stage. As the scan positions have been created at longer distances than usual to streamline the process, the pre-registration work conducted on site undergoes a revision. Since image 16 is in perspective, it is harder to see the color of the unions between scan points. When the data obtained in the field is imported, the software analyzes whether the points of a cloud of a scan position overlap enough with those of the previous and posterior clouds. The software checks and colors the unions in line with three concepts: Error, strength of union and overlap between clouds. If they have any undesirable parameters, the may color them in red, yellow or blue or it may not directly propose the union because it is outside a minimum range. What happens then, is that an optimization process commences analyses and improves the cloud-to-cloud union (as in Figure 8), between the two clouds that do not have a connection line in green (Figures 14 and 16). If we can improve the overlap parameters in this optimization process, the line of union changes to green and it is Buildings 2022,12, 380 21 of 32 accepted as valid. If we cannot change the union to green in the optimization process, there is still the option of carrying out another scan on site the next day and further strengthening the cloud of the set. This means that the field work should be processed every day, and so it is importance to use the tools mentioned in this article. The devices, software and applications enable pre-processing that greatly reduces the amount of daily processing work; they also make it easier to check that the on-site capture is satisfactory. Once the processing stage is finished, to view the results and extract the necessary information the most appropriate file format is LGS. The free Leica Jetstream Viewer software enables the viewing and consultation of data from the digital model comprising the set of point clouds and the 360 ◦ images of each of the scan positions. This application allows a visual and metric inspection to be carried out virtually along the route. With this resource you can accomplish the analysis, verification and data extraction tasks needed for the modeling and simulation process in NEST, such as distances, areas, angles or even surface temperatures (Figure 17). Buildings 2022, 12, x FOR PEER REVIEW 22 of 33 software and applications enable pre-processing that greatly reduces the amount of daily processing work; they also make it easier to check that the on-site capture is satisfactory. Once the processing stage is finished, to view the results and extract the necessary information the most appropriate file format is LGS. The free Leica Jetstream Viewer software enables the viewing and consultation of data from the digital model comprising the set of point clouds and the 360° images of each of the scan positions. This application allows a visual and metric inspection to be carried out virtually along the route. With this resource you can accomplish the analysis, verification and data extraction tasks needed for the modeling and simulation process in NEST, such as distances, areas, angles or even surface temperatures (Figure 17). (a) (b) Figure 17. Visualization of results of the TLS LiDAR clouds in Jetstream Viewer, obtaining quantitative data for the model: (a) 3D colored cloud visualization, geometric data, Gipuzkoa School of Engineering building, UPV/EHU campus in DSS; (b) southwest façade of the same building. Visualization from the 360° image of the building, geometric data (green and gray) and thermal data (red). Before starting to survey the campuses with TLS, the feasibility was analyzed in terms of resources and time needed to complete the ALS LiDAR cloud. TLS runtimes were calculated for comparison to UAV ones, with automated photogrammetry. In the case of Figure 17. Visualization of results of the TLS LiDAR clouds in Jetstream Viewer, obtaining quantitative data for the model: ( a ) 3D colored cloud visualization, geometric data, Gipuzkoa School of Engineering building, UPV/EHU campus in DSS; ( b ) southwest façade of the same building. Visualization from the 360 ◦ image of the building, geometric data (green and gray) and thermal data (red). Buildings 2022,12, 380 22 of 32 Before starting to survey the campuses with TLS, the feasibility was analyzed in terms of resources and time needed to complete the ALS LiDAR cloud. TLS runtimes were calculated for comparison to UAV ones, with automated photogrammetry. In the case of the UPV/EHU campus in DSS, the configuration of the urban environment, its dimensions and the buildings’ closeness enable very fast complementary capture with TLS, for which additional work of three days was estimated, in three zones. However, the UNAV campus in Pamplona presents groups of buildings, though with large distances between some of them. Capturing the entire campus with TLS would have taken several weeks, with an overwhelming amount of information. However, as the ALS LiDAR cloud, with a density of 14 pts/m 2 , provided very acceptable measurement results, the complementary survey with TLS was estimated, after previous scanning plans, to take seven days, one day for each zone. After the TLS calculations, the resources were estimated with the UAV to decide which would be the fastest and most efficient combination. 4.3. Capture by UAV and SfM Photogrammetric Processing The combination of data collection by UAV and the SfM automated photogrammetry method facilitates documentation due to the simplicity of the process and the width of the working range [ 78 , 79 ]. The study focuses on an assessment of the operation and efficiency of the capture for these large urban areas [ 14 ]. It concerns assessment of whether it could be faster and more efficient than additional captures with TLS to complete the ALS LiDAR cloud of the campuses. At the UPV/EHU campus, flight restrictions due to the location in controlled airspace made operations difficult. For that reason, it was ultimately decided to not complete the clouds with UAV-assisted photogrammetry. The survey forecast was performed at the UNAV campus in Pamplona. The programmable application (DJI-GS Pro) allows configuration of the automatic flight mission through navigation based on satellite positioning (GNSS). The UAV used is a DJI Phantom 4 Pro model, equipped with a 1” CMOS sensor that reduces radial distortion and improves the metric quality of the SfM restitution method [ 80 ]. A grid comprising 147 square sectors measuring 100 m on each side and an area of 1 Ha was created, establishing a low-altitude flight parameter (37.3 m) to guarantee the model’s accuracy [ 5 ]. This altitude offers a GSD factor of 1 cm/pixel with an overlap between photos, front and side, of 75%. The combined use of nadir shots (gimbal tilt—90 ◦ ) and oblique shots (gimbal tilt—45 ◦ ) was determined. One nadir and four oblique shots were planned per sector, the latter aligned with the four trajectories that join the vertices of each sector to its center (Figure 18). The results of the resource feasibility study are presented in Table 6. Table 6. Summary of UAV photogrammetry capture data. Sectors Sectors Overlap Flight Height Ground Sample Distance GSD Nadir Shot (−90◦) Oblique Shot (−65◦) Frontal Overlap Lateral Overlap Flight Time Batteries Photos Sector type 1 ha - 37.3 m 1 cm/pixel 1 4 75% 75% 100 min 5 sets 450 Set of sectors 147 ha 10 m. (10%) 37.3 m 1 cm/pixel 147 588 75% 75% 245 h 735 sets 66,150 Capturing data from the entire campus would require 245 flight hours, which, added to the preparation work, could mean a field task of more than one month. As previously justified, the additional works of the ALS LiDAR cloud with TLS techniques was estimated to take seven days, so the UAV was used to complement the TLS data in those sectors of the 147 planned sectors where there are no buildings. Buildings 2022,12, 380 23 of 32 Buildings 2022, 12, x FOR PEER REVIEW 23 of 33 the UPV/EHU campus in DSS, the configuration of the urban environment, its dimensions and the buildings’ closeness enable very fast complementary capture with TLS, for which additional work of three days was estimated, in three zones. However, the UNAV campus in Pamplona presents groups of buildings, though with large distances between some of them. Capturing the entire campus with TLS would have taken several weeks, with an overwhelming amount of information. However, as the ALS LiDAR cloud, with a density of 14 pts/m2, provided very acceptable measurement results, the complementary survey with TLS was estimated, after previous scanning plans, to take seven days, one day for each zone. After the TLS calculations, the resources were estimated with the UAV to decide which would be the fastest and most efficient combination. 4.3. Capture by UAV and SfM Photogrammetric Processing The combination of data collection by UAV and the SfM automated photogrammetry method facilitates documentation due to the simplicity of the process and the width of the working range [78,79]. The study focuses on an assessment of the operation and efficiency of the capture for these large urban areas [14]. It concerns assessment of whether it could be faster and more efficient than additional captures with TLS to complete the ALS LiDAR cloud of the campuses. At the UPV/EHU campus, flight restrictions due to the location in controlled airspace made operations difficult. For that reason, it was ultimately decided to not complete the clouds with UAV-assisted photogrammetry. The survey forecast was performed at the UNAV campus in Pamplona. The programmable application (DJI-GS Pro) allows configuration of the automatic flight mission through navigation based on satellite positioning (GNSS). The UAV used is a DJI Phantom 4 Pro model, equipped with a 1” CMOS sensor that reduces radial distortion and improves the metric quality of the SfM restitution method [80]. A grid comprising 147 square sectors measuring 100 m on each side and an area of 1 Ha was created, establishing a low-altitude flight parameter (37.3 m) to guarantee the model’s accuracy [5]. This altitude offers a GSD factor of 1 cm/pixel with an overlap between photos, front and side, of 75%. The combined use of nadir shots (gimbal tilt—90°) and oblique shots (gimbal tilt—45°) was determined. One nadir and four oblique shots were planned per sector, the latter aligned with the four trajectories that join the vertices of each sector to its center (Figure 18). (a) (b) Figure 18. ( a ) Work planning prevision in 147 sectors; ( b ) planning of flight directions in one of the sectors, from top to bottom: 1. Shooting overlap, 2. Nadiral shooting/G.P.A.: − 90 ◦ /C.A.: 0 ◦ , 3. Oblique shooting/G.P.A.: − 45 ◦ /C.A.: 45 ◦ , 4. Oblique shooting/G.P.A.: − 45 ◦ /C.A.: 135 ◦ , 5. Oblique shooting/G.P.A.: − 45 ◦ /C.A.: 225 ◦ , 6. Oblique shooting/G.P.A.: − 45 ◦ /C.A.: 315 ◦ , 7. Orthophoto, (G.P.A: Gimbal Pitch Angle; C.A: Course Angle). 4.4. Modeling The point cloud of the ensemble will be used to perform the simulation 3D model of each campus in NEST (Figure 19). Buildings 2022, 12, x FOR PEER REVIEW 24 of 33 Figure 18. (a) Work planning prevision in 147 sectors; (b) planning of flight directions in one of the sectors, from top to bottom: 1. Shooting overlap, 2. Nadiral shooting/G.P.A.: −90°/C.A.: 0°, 3. Oblique shooting/G.P.A.: −45°/C.A.: 45°, 4. Oblique shooting/G.P.A.: −45°/C.A.: 135°, 5. Oblique shooting/G.P.A.: −45°/C.A.: 225°, 6. Oblique shooting/G.P.A.: −45°/C.A.: 315°, 7. Orthophoto, (G.P.A: Gimbal Pitch Angle; C.A: Course Angle). The results of the resource feasibility study are presented in Table 6. Table 6. Summary of UAV photogrammetry capture data. Sectors Sectors Overlap Flight Height Ground Sample Distance GSD Nadir Shot (−90°) Oblique Shot (−65°) Frontal Overlap Lateral Overlap Flight Time Batteries Photos Sector type 1 ha - 37.3 m 1 cm/pixel 1 4 75% 75% 100 min 5 sets 450 Set of sectors 147 ha 10 m. (10%) 37.3 m 1 cm/pixel 147 588 75% 75% 245 h 735 sets 66,150 Capturing data from the entire campus would require 245 flight hours, which, added to the preparation work, could mean a field task of more than one month. As previously justified, the additional works of the ALS LiDAR cloud with TLS techniques was estimated to take seven days, so the UAV was used to complement the TLS data in those sectors of the 147 planned sectors where there are no buildings. 4.4. Modeling The point cloud of the ensemble will be used to perform the simulation 3D model of each campus in NEST (Figure 19). Figure 19. Final 3D model of the UNAV campus in Pamplona for simulation in NEST, (see Appendix A). The resulting final cloud can also be segmented to model isolated buildings in greater detail and make specific simulations of the campuses (Figure 20). Figure 19. Final 3D model of the UNAV campus in Pamplona for simulation in NEST, (see Appendix A). The resulting final cloud can also be segmented to model isolated buildings in greater detail and make specific simulations of the campuses (Figure 20). Buildings 2022,12, 380 24 of 32 Buildings 2022, 12, x FOR PEER REVIEW 25 of 33 Figure 20. BIM model in Autodesk Revit software. School of Engineering building. Campus UPV/EHU DSS, Zone 2 of the scan. 4.5. NEST—Environmental Assessment Results As previously stated, it is not the purpose of this article to analyze the results of the assessment, which has already been dealt with in other specific articles [11,12]. In any case, to round up the flow of the project, we felt it was appropriate to show a very reduced sample of the results given by NEST. An image of the simulation model and a summary table of the two campuses will be presented (Figure 21 and Table 7). Figure 21. NEST model with simulation of CO2 impacts. UPV/EHU DSS campus. Table 7. Results obtained in the NEST simulation of the improvement scenarios for the years 2030 and 2050. Impact Indicator Sector Life-Cycle Stage * Baseline Scenario 2030 2050 UNAV UPV/EHU UNAV UPV/EHU UNAV UPV/EHU PE (MJ/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 7.5 × 106 1.0 × 107 1.7 × 107 1.5 × 107 3.4 × 107 1.7 × 107 Buildings (BU) B6 1.3 × 108 3.8 × 107 1.1 × 108 3.7 × 107 9.3 × 107 3.5 × 107 Public lighting (PL) B6 2.2 × 106 3.1 × 106 1.7 × 106 2.5 × 106 1.1 × 106 1.6 × 106 Mobility A1–3, B6, C1–4 1.8 × 103 3.1 × 103 1.8 × 103 3.1 × 103 1.8 × 103 3.1 × 103 GWP (kgeqCO2/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 3.5 × 105 4.1 × 105 8.1 × 105 6.3 × 105 9.7 × 105 7.2 × 105 Buildings (BU) B6 6.3 × 106 1.9 × 106 5.0 × 106 1.8 × 106 4.4 × 106 1.7 × 106 Public lighting (PL) B6 2.3 × 105 3.3 × 105 1.9 × 105 2.7 × 105 1.2 × 105 1.7 × 105 Mobility A1–3, B6, C1–4 1.0 × 102 1.4 × 102 1.0 × 102 1.4 × 102 1.0 × 102 1.4 × 102 Energy consumption (kWh/year) Natural gas (NG) B6 1.7 × 107 1.5 × 106 9.4 × 106 1.2 × 106 5.7 × 106 6.9 × 105 Electricity (E) B6 9.7 × 106 5.9 × 106 1.0 × 107 5.7 × 106 1.1 × 107 5.5 × 106 Biomass (B) B6 7.0 × 103 0.0 6.3 × 105 4.5 × 104 1.0 × 106 9.8 × 104 Renewable energy production (kWh) Thermal solar (TS) 3.6 × 104 7.1 × 104 2.4 × 105 1.9 × 105 6.6 × 105 4.7 × 105 Photovoltaic (P) 0.0 4.7 × 105 1.8 × 106 7.8 × 105 3.7 × 106 1.6 × 106 * See Table 2. Figure 20. BIM model in Autodesk Revit software. School of Engineering building. Campus UPV/EHU DSS, Zone 2 of the scan. 4.5. NEST—Environmental Assessment Results As previously stated, it is not the purpose of this article to analyze the results of the assessment, which has already been dealt with in other specific articles [ 11 , 12 ]. In any case, to round up the flow of the project, we felt it was appropriate to show a very reduced sample of the results given by NEST. An image of the simulation model and a summary table of the two campuses will be presented (Figure 21 and Table 7). Buildings 2022, 12, x FOR PEER REVIEW 25 of 33 Figure 20. BIM model in Autodesk Revit software. School of Engineering building. Campus UPV/EHU DSS, Zone 2 of the scan. 4.5. NEST—Environmental Assessment Results As previously stated, it is not the purpose of this article to analyze the results of the assessment, which has already been dealt with in other specific articles [11,12]. In any case, to round up the flow of the project, we felt it was appropriate to show a very reduced sample of the results given by NEST. An image of the simulation model and a summary table of the two campuses will be presented (Figure 21 and Table 7). Figure 21. NEST model with simulation of CO2 impacts. UPV/EHU DSS campus. Table 7. Results obtained in the NEST simulation of the improvement scenarios for the years 2030 and 2050. Impact Indicator Sector Life-Cycle Stage * Baseline Scenario 2030 2050 UNAV UPV/EHU UNAV UPV/EHU UNAV UPV/EHU PE (MJ/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 7.5 × 106 1.0 × 107 1.7 × 107 1.5 × 107 3.4 × 107 1.7 × 107 Buildings (BU) B6 1.3 × 108 3.8 × 107 1.1 × 108 3.7 × 107 9.3 × 107 3.5 × 107 Public lighting (PL) B6 2.2 × 106 3.1 × 106 1.7 × 106 2.5 × 106 1.1 × 106 1.6 × 106 Mobility A1–3, B6, C1–4 1.8 × 103 3.1 × 103 1.8 × 103 3.1 × 103 1.8 × 103 3.1 × 103 GWP (kgeqCO2/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 3.5 × 105 4.1 × 105 8.1 × 105 6.3 × 105 9.7 × 105 7.2 × 105 Buildings (BU) B6 6.3 × 106 1.9 × 106 5.0 × 106 1.8 × 106 4.4 × 106 1.7 × 106 Public lighting (PL) B6 2.3 × 105 3.3 × 105 1.9 × 105 2.7 × 105 1.2 × 105 1.7 × 105 Mobility A1–3, B6, C1–4 1.0 × 102 1.4 × 102 1.0 × 102 1.4 × 102 1.0 × 102 1.4 × 102 Energy consumption (kWh/year) Natural gas (NG) B6 1.7 × 107 1.5 × 106 9.4 × 106 1.2 × 106 5.7 × 106 6.9 × 105 Electricity (E) B6 9.7 × 106 5.9 × 106 1.0 × 107 5.7 × 106 1.1 × 107 5.5 × 106 Biomass (B) B6 7.0 × 103 0.0 6.3 × 105 4.5 × 104 1.0 × 106 9.8 × 104 Renewable energy production (kWh) Thermal solar (TS) 3.6 × 104 7.1 × 104 2.4 × 105 1.9 × 105 6.6 × 105 4.7 × 105 Photovoltaic (P) 0.0 4.7 × 105 1.8 × 106 7.8 × 105 3.7 × 106 1.6 × 106 * See Table 2. Figure 21. NEST model with simulation of CO2impacts. UPV/EHU DSS campus. Table 7. Results obtained in the NEST simulation of the improvement scenarios for the years 2030 and 2050. Impact Indicator Sector Life-Cycle Stage * Baseline Scenario 2030 2050 UNAV UPV/EHU UNAV UPV/EHU UNAV UPV/EHU PE (MJ/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 7.5 × 10 61.0 ×107 1.7 × 10 71.5 ×107 3.4 × 10 71.7 ×107 Buildings (BU) B6 1.3 × 10 83.8 ×107 1.1 × 10 83.7 ×107 9.3 × 10 73.5 ×107 Public lighting (PL) B6 2.2 × 10 63.1 ×106 1.7 × 10 62.5 ×106 1.1 × 10 61.6 ×106 Mobility A1–3, B6, C1–4 1.8 × 10 33.1 ×103 1.8 × 10 33.1 ×103 1.8 × 10 33.1 ×103 GWP (kgeqCO2/year) Buildings (BP) A1–3, A4–5, B2, B4, C1–4 3.5 × 10 54.1 ×105 8.1 × 10 56.3 ×105 9.7 × 10 57.2 ×105 Buildings (BU) B6 6.3 × 10 61.9 ×106 5.0 × 10 61.8 ×106 4.4 × 10 61.7 ×106 Public lighting (PL) B6 2.3 × 10 53.3 ×105 1.9 × 10 52.7 ×105 1.2 × 10 51.7 ×105 Mobility A1–3, B6, C1–4 1.0 × 10 21.4 ×102 1.0 × 10 21.4 ×102 1.0 × 10 21.4 ×102 Energy consumption (kWh/year) Natural gas (NG) B6 1.7 × 10 71.5 ×106 9.4 × 10 61.2 ×106 5.7 × 10 66.9 ×105 Electricity (E) B6 9.7 × 10 65.9 ×106 1.0 × 10 75.7 ×106 1.1 × 10 75.5 ×106 Biomass (B) B6 7.0 × 10 30.0 6.3 × 10 54.5 ×104 1.0 × 10 69.8 ×104 Renewable energy production (kWh) Thermal solar (TS) 3.6 × 10 47.1 ×104 2.4 × 10 51.9 ×105 6.6 × 10 54.7 ×105 Photovoltaic (P) 0.0 4.7 ×105 1.8 × 10 67.8 ×105 3.7 × 10 61.6 ×106 * See Table 2. Buildings 2022,12, 380 25 of 32 5. Discussion The starting hypothesis focused on the possibilities of obtaining rapid digitization of two university campuses that occupy a large area of the cities where they are located. The lockdown situation caused by COVID-19 did not allow long stays in the field to collect data, and a combination of techniques were studied so that the survey could be performed in just a few days. After analyzing the results, it can be stated that it was possible to capture both university campuses in 3D, while field work time was reduced to just 10 days (7 + 3), with a satisfactory outcome that was economical and efficient. It was possible to complete the fieldwork in ten days, thanks to a combination of resources and techniques. After studying different technologies and resources, the work was accomplished by using ALS LiDAR point clouds captured by public services and by capturing complementary point clouds with greater density and accuracy by means of TLS; finally, UAV-assisted automated photogrammetric techniques were used to complement previous clouds. The study of each of these technologies used to achieve the objectives of this work enabled discussion of several specific advantages and disadvantages for the specific situation arising in this article. The ALS LiDAR clouds obtained by public services are free and thus did not involve any investment of resources, finances or time. Although, it was discerned that those LiDAR clouds for the two cities in question have very different qualities, which affected the approach to the work and its development. The LiDAR cloud of the UNAV campus in Pamplona, with a density of 14 points/m 2 , allows measurements to be made with sufficient precision to do the simulation model in NEST. However, the LiDAR cloud of the UPV/EHU campus in the city of Donostia-San Sebastián, with a maximum density of 2.2 points/m 2 , has many limitations. Regarding the measurement of parts of building elements, it would be limited to dimensioning the total height of the built volume, without being able to make other kinds of measurements such as height of standard floor plan, opening sizes and even, in some cases, size of the façade surfaces. With respect to plant masses, it is also verified that the cloud’s low density does not allow estimates of mass volume and in some cases, not even basic measurements of the plant element’s height. That is why it was decided to complement the LiDAR data with massive point capture based on TLS, since that technique allows capture in the shortest possible time. Considering the characteristics of the LiDAR clouds found, the number of scanning points for each campus was more or less intensified. We have used this procedure to try out scan points at longer distances than usual (30 m, 50 m and 80 m , without exceeding the capture distance limit presented by the laser scanner, which is 130 m). It should be borne in mind that this work with TLS sets out to complement the LiDAR cloud of the Government of Navarra. With these tests we found that most scan points at distances of 30 m give acceptable results (unions in green) and that the maximum range of the scanner at high resolution is actually 60 m, even when the capacity is 130 m. Regarding the complementary work of the point cloud with TLS techniques, the UPV/EHU campus has a higher scan position density than Pamplona, since its 2017 ALS LiDAR cloud is much less precise than that of the UNAV campus. The work with TLS showed some differences at each campus. Considering the maximum range of the scanner (130 m), a minimum of three scans per building façade could initially be considered. However, due to the characteristics of each campus, the pre-registration and processing work had different disadvantages. At the UPV/EHU campus, when joining the adjacent clouds, the pre-registration was strong enough because it was a flat area with high building density and little vegetation. Better cloud strength and overlap results were obtained, even though the scanner used the same millimetric precision. However, at UNAV, the campus’s size makes the buildings more distant, so when scanning a building it is not easy to record many points from another adjacent building to strengthen the joints. In addition, the dense vegetation and the unevenness of the steep terrain make the overlap and strength of the final clouds lower. In any case, in Pamplona, this aspect was supplied with the good quality of the 2017 ALS LiDAR clouds. 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