Image dataset for benchmarking automated fish detection and classification algorithms

1 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata Image dataset for benchmarking automated fish detection and classification algorithms Marco Francescangeli 1 ✉ , Simone Marini 2,3 ✉ , Enoc Martínez4 ✉ , Joaquín Del Río 1 ✉ , Daniel M. toma1 ✉ , Marc Nogueras1 ✉ & Jacopo aguzzi 3,5 ✉ Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. the OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets. Background & Summary In a context of global climate change and increasing human impact in coastal marine areas, the monitoring of changes in fish behaviour and population abundances is becoming strategic to provide data on ecosystem productivity, functioning and derived services (e.g., the status of already overexploited stocks)1–3. For this reason, monitoring the temporal dynamics of fish communities is of pivotal importance to distinguish the variability in species composition, due to diel and seasonal activity rhythms, from more long-lasting trends of change4,5. The temporal trend of fish presence and abundance, obtained from the analysis of imagery data, is produced by the rhythmic migration of populations into the marine 3D space seabed and water column scenario6–8. The information derived from such dynamics coupled with environmental (oceanographic and meteorological) data provide useful information regarding species ecological niche9–11, and allow understanding and forecasting the impact of anthropic activities (e.g., commercial fishing, urban and port expansion) and the consequent mitigation actions (e.g., establishment of marine protected areas)7,12,13. Cabled video-observatory monitoring technology is considered as the core of growing in situ and robotized marine ecological laboratories in coastal and deep-sea areas14,15. International initiatives about marine observatories infrastructures, like for example the European Multidisciplinary Seafloor and water column Observatory (EMSO-ERIC), the Joint European Research Infrastructure of Coastal Observatories (JERICO-RI), or the Ocean Network Canada (ONC) are becoming widespread all over the world16, and increasingly install multiparametric sensors that, beside the imaging depicting biological information, also acquire oceanographic and geo-chemical data13,17. Unlike other types of data, the scientific content of videos and images is not immediately usable. To overcome this problem, the image content is often inspected by trained operators in order to manually extract relevant biological information, such as the number of individuals and the corresponding classification into species18–20. This manual process requires a considerable human effort, and it is really time demanding. For this reason, automated image analysis methodologies for the extraction and coding of the image content need to be urgently defined and developed in order to transform imaging devices into actual biological tools for the underwater observing systems21,22. 1Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. 2institute of Marine Sciences, national Research council of italy, La Spezia, italy. 3Stazione Zoologica Anton Dohrn (SZN), Naples, 80127, Italy. 4european Multidisciplinary Seafloor and Water column Observatory, Rome, italy. 5Department of Marine Renewable Resources, Institute of Marine Science (ICM-CSIC), Barcelona, 08016, Spain. ✉e-mail: [email protected]; [email protected].cnr.it; [email protected]; joaquin.del. [email protected]; [email protected]; [email protected]; [email protected] Data DEScRIptoR opEN 2 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ This article describes a dataset of underwater images suitable for studying, developing and testing methodologies for automated image analysis. The images were acquired at the seafloor cabled multiparametric video-platform “Observatory of the Sea” (OBSEA; www.obsea.es), located in a fishing protected area, 20 m depth, 4 km off the Vilanova i la Geltrú coast, near Barcelona (Spain)23,24. The image dataset consists of 33805 images containing 69917 manually tagged fish specimens, acquired every 30 minutes over day and night, during two consecutive years (i.e., from 1st January 2013 to 31st December 2014). The dataset encompasses and replicates the most relevant seasonal dynamics of environmental change affecting fish species abundance and assemblage at the study site25. In fact, coastal fish physiology and behaviour are highly responsive to changes in photo-period (i.e., light intensity and photophase duration)26, nutrients and pollutants27,28 and oceanographic regimes (i.e., currents, temperature, and salinity)29–31. Thus, OBSEA monitoring area represents a real-world operational context common to many other temperate coastal underwater observing systems. Together with the image dataset, we also provided oceanographic and meteorological time series, whose readings have been averaged and recorded synchronously with time-lapse images. Those data are for water temperature, change in depth, salinity, air temperature, wind speed and direction, solar irradiance and water precipitation. We added those environmental time series as contemporarily acquired, in order to provide a quality aspect to the real-time world context of image acquisition, to be used as metrics for image processing efficiency32. Moreover, the use of those data has been of relevance to provide hints in cause-effect studies linking fish presence and behaviour upon changing environmental conditions, being already successfully exploited for automated fish recognition32, and for studying the temporal modulation of the species niches33,34. The manually tagged fish individuals for each image make the dataset a valuable benchmark for the multidisciplinary marine science community consisting of biologists, oceanographers, and a growing community of computer scientists and mathematicians skilled in Artificial Intelligence and data science. Methodological comparison could be not only specifically conceived for fish detection and classification, such as Fish4Knowledge35, but also for the emerging approaches for active and incremental learning36–38, or for techniques aimed at mitigating the “Concept Drift” phenomenon, when the classification performance drop for varying species assemblages at changing environmental conditions and training need to be updated39–42. Finally, the reported dataset of labelled images is worthwhile for global image repositories that aim to reduce annotation effort, such as Fathomnet43, and, thanks to the tags and the bounding boxes associated to each individual, it can be easily split into training, validation, and test subsets (e.g., K-fold Cross-validation) in order to fit the needs of the specific image analysis algorithm used on the image dataset32,42,44–47. Methods OBSEA video-image underwater platform and routine. The OBSEA seafloor cabled observatory was deployed in 2009 within a Natura 2000 marine reserve, named “Colls i Miralpeix”, at 20 m depth and at 4 km off Vilanova i la Gertrú harbour (i.e., the Catalan coast of the NW Mediterranean, Spain: 41°10′54.87″N and 1°45′8.43″E) (Fig.1). The cable observatory is located on a mixed sand and seagrass meadows (Posidonia oceanica) bed, being surrounded by artificial concrete reefs, deployed to protect the area from illegal trawling23,24. The OBSEA node structure has a size in terms of width, height, and length of 1x2x1 m, respectively, with an overall weight of 5 tons. The observatory is equipped with a camera approximately at 3.5 m distance from one of Fig. 1 Location of the OBSEA video platform in the North-Western (NW) Mediterranean. The figure indicates the “Development Centre of Remote Acquisition and Information Processing” (SARTI) and the Sant Pere de Ribes Meteorological Station (Sant Pere Met.) positions relative to the Catalan coasts (a), indicating also the OBSEA position off the harbour of Vilanova i la Geltrú (b). Power and broadband Ethernet communications are provided to OBSEA through an underwater cable from the SARTI building (green and red tracks). The OBSEA platform is surrounded by three biotopes (c) and focusing on one of them (Biotope 1, c). 3 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ these artificial reefs, with a Field of View (FOV) area of about 3 × 3 m, resulting in a 10.5 m3 of imaged volume (Fig.2). The image monitoring was performed in a 30 min time-lapse mode, by synchronising illumination at nighttime at the moment of shooting. To shoot photos at night, the camera was associated with two illuminators located beside the camera at 1 m distance from each other, each one consisting of 13 high-luminosity white LEDs. The lights were emitting 2900 lumens, with a colour temperature of 2700 kelvin and an illumination angle of 120°. An automated protocol, controlled by a LabView application, switched on-and-off the lights before and after the camera shooting, resulting in a 30 s light-on period, to allow the lights to warm up and attain the maximum amount of homogeneous illumination. Two different cameras were used during the monitoring period: an OPT-06 Underwater IP Camera (Sony SNC-RZ25N) from 1st January 2013 to 11th December 2014, and an Axis P1346-E Camera thereafter until 31st December 2014 (Table1). The selected resolution of images for the first cameras was 640 × 480 pixels, Fig. 2 Examples of photos acquired by the different cameras used at the OBSEA. The Sony SNC-RZ25N (CAM1) (a,b) and the Axis P1346-E (CAM2) (c,d) cameras’ acquired photos during day and night. Sony SNC-RZ25N (CAM1) Axis P1346-E (CAM2) N. of Pixels 3.8 MP 3 MP Varifocal 4.1–73.8 mm 3.5–10 mm Pan Angle −170°–170° 72°-27° Tilt Angle −90° - 30° / Focal Length-Aperture ratio F1.4 F1.6 Light Sensitivity 0.7 lux 0.5 lux Day-Night Function Yes Yes Infrared Filter Yes Yes Zoom 18x Digital Zoom Image Sensor 1/4 type CCD Imager CMOS RGB of progressive scan 1/3” Obturation Speed /1/35500 - 1/6 sec Image Size 640 × 480, 480 × 360, 384 × 288, 320 × 240, 256 × 192, 160 × 120 from 2048 × 1536 to 160 × 90 Table 1. Technical characteristic of the two cameras used for the monitoring at the OBSEA. Technical characteristics of the two cameras (i.e., Sony SNC-RZ25N and Axis P1346-E) used between 2013–2014 at the OBSEA platform: number of pixels (N. of Pixels), varifocal, pan and tilt angle, focal length-aperture ratio, light sensitivity, presence/ absence of the day-night filter, zoom, image sensor, obturation speed, and size of the saved images. 4 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ whereas the second camera image resolution was 2048 × 1536 pixels (Fig.2). The acquired images have a JPEG format for both cameras. Fish tags and annotation procedure. In order to tag the relevant biological content of the images (i.e., fish individuals), a Python code was developed based on the OpenCV framework for Python (https://opencv. org/)48 (Fig.3). The script allowed tracing a line around the biological subjects, calculating afterwards a bounding box (bbox). The script and all the instructions of the tagging procedure are available through the Zenodo repository49. The species classification was performed according to FISHBase50. In those cases where the fish was not fully classifiable because too distant or badly positioned within the FOV we classified them as “Unknown fish”. This is because these unclassified fishes are important for the estimate of fish biomass (Fig.3). Some examples deal with individuals appearing in the photo like dots. Other examples deal with overlapping fishes, such as when they form schools. oceanographic and meteorological data acquisition and processing. The OBSEA was equipped with a CTD probe to measure the water temperature, salinity, and the changes of depth, calculated from shifts in water pressure (as proxy for tides). During the period between 2013–2014, two CTD probes were sequentially deployed to avoid data gaps during sensor maintenance operations (Table2). In Table3 the deployment periods of both CTD probes are depicted. Fig. 3 Flowchart for the tagging procedure. The tagging procedure of the photos were carried out with a Python code, at the end of which it releases as output a list of tags in text format and save the images with their bounding boxes (rectangles of different colours). Here, we report an example of a processed photo with tagged specimens and untagged fishes (green circle). Range Accuracy Stability Resolution Time of Acquisition SBE 37-SMP Conductivity 0–7 S/m 0.0003 S/m 0.0003 S/m per month 0.00001 S/m 10 sec Temperature −5 °C–35 °C 0.002 °C 0.0002 °C per month 0.0001 °C 10 sec Pressure 20–7000 m 0.1% of full-scale range 0.05% of full-scale range per year 0.002% of full-scale range 10 sec SBE 16plus V2 Conductivity 0–9 S/m 0.0005 S/m 0.0003 S/m per month 0.00005 S/m 10 sec Temperature −5 °C–35 °C 0.005 °C 0.0002 °C per month 0.0001 °C 10 sec Pressure 20–7000 m 0.1% of full-scale range 0.1% of full-scale range per year 0.002% of full-scale range 10 sec UPC Weather Station (Station 1) Air Temperature −40 °C–65 °C 0.3 °C /0.1 °C 10 sec Wind Speed 0–322 km/h 3 km/h /1 km/h 10 min Wind Direction 0–360° 3° / 1° 10 min Sant Pere de Ribes Weather Station(Station 2) Solar Irradiance 0–5000 W/m typ. <3%, 5% maximum /1 W/m2 10 min Rain 0–20 mm/min 0.1 mm /0.001 mm 10 min Table 2. Technical characteristics of the two CTD probes, and of the two meteorological stations. Technical characteristic of the two CTD sensors (i.e., SBE16 and SBE37) installed at the OBSEA, the meteorological station of the Polytechnic University of Catalonia (UPC) in Vilanova i la Geltrú (i.e., Station 1), and the meteorological station of Sant Pere de Ribes (i.e., Station 2) present during the period between 2013–2014. 5 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ Sensor Deployment Recovery SBE 16plus V2 2013-01-09 2013-04-10 SBE 37-SMP 2013-04-10 2013-04-19 SBE 16plus V2 2013-04-19 2013-12-05 SBE 37-SMP 2013-12-05 2014-03-20 SBE 16plus V2 2014-03-20 2014-09-12 SBE 37-SMP 2014-09-12 2014-12-31 Table 3. Deployment periods of the CTD sensors of the OBSEA. Details of the deployment and recovery of the CTD probes during the period between 2013–2014. Flag Value Flag Meaning 1Good Data 2QC Not Applied 3Suspicious Data 4Bad Data 9Missing Data Table 4. Quality control flags’ codes and meanings. Quality control flags values and respective meanings applied to the environmental data. Taxa N % Diplodus vulgaris 14328 20.49 Diplodus sargus 2727 3.90 Diplodus puntazzo 374 0.53 Diplodus cervinus 415 0.59 Diplodus annularis 1268 1.81 Oblada melanura 6898 9.87 Dentex dentex 615 0.88 Sparus aurata 34 0.05 Sarpa salpa 208 0.30 Boops boops 10 0.01 Spondyliosoma cantharus 1001 1.43 Pagrus pagrus 50 0.07 Pagellus sp. 9 0.01 Spicara maena 1826 2.61 Chromis chromis 2762 3.95 Symphodus tinca 7 0.01 Symphodus mediterraneus 209 0.30 Symphodus cinereus 54 0.08 Coris julis 1589 2.27 Thalassoma pavo 53 0.08 Serranus cabrilla 258 0.37 Epinephelus marginatus 5 0.01 Sciaena umbra 50 0.07 Seriola dumerili 72 0.10 Trachurus sp. 1 0.00 Apogon sp. 822 1.18 Atherina sp. 101 0.14 Conger conger 14 0.02 Scorpaena sp. 1017 1.45 Unknown fish 33140 47.40 TOTAL 69917 100.00 Table 5. List of fish taxa with their respective number of tags and relative percentage. Number of tags (N) and relative percentage (%) for each fish taxa, unclassified individuals and total of fishes detected during 2013 and 2014 at the OBSEA platform. 6 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ Moreover, meteorological variables were measured from the meteorological station on the roof of the Polytechnic University of Catalonia (UPC) building in Vilanova i la Geltrú, and from the meteorological station of Sant Pere de Ribes, Spain (www.meteo.cat) (Table2). The first one was a Vantage Pro2 meteorological station. This station was installed to collect data on the air temperature, wind speed and direction. Furthermore, we compiled data for solar irradiance and rain from the meteorological station in Sant Pere de Ribes. This station was equipped with a Pyranometer SKS 1110 to measure solar irradiance, and a Rain[e] sensor for the rain. All the oceanographic and meteorological data were averaged every 30 min, in order to have mean and standard deviation measurements contemporary to the timing of all acquired images (see above), except for the irradiance and rain, that were compiled selecting and extracting only readings correspondent to the acquired image timings (see above). Fig. 4 Photomosaic of the fish taxa encountered during the tagging procedure. Examples of photos of the 29 fish taxa recognized during the tagging, plus an example of an unclassified fish: (a) Diplodus vulgaris, (b) Diplodus sargus, (c) Diplodus puntazzo, (d) Diplodus cervinus, (e) Diplodus annularis, (f) Oblada melanura, (g) Dentex dentex, (h) Sparus aurata, (i) Sarpa salpa, (j) Boops boops, (k) Spondyliosoma cantharus, (l) Pagrus pagrus, (m) Pagellus sp., (n) Spicara maena, (o) Chromis chromis, (p) Symphodus tinca, (q) Symphodus mediterraneus, (r) Symphodus cinereus, (s) Coris julis, (t) Thalassoma pavo, (u) Serranus cabrilla, (v) Epinephelus marginatus, (w) Sciaena umbra, (x) Seriola dumerili, (y) Trachurus sp., (z) Apogon sp., (a.a) Atherina sp., (a.b) Conger conger, (a.c) Scorpaena sp., and (a.d) Unknown fish. Column Labels Description Event “OBSEA:CAM1:2013_14” if the Sony SNC-RZ25N camera was used to take the photo, or “OBSEA:CAM2:2013_14” if the Axis P1346-E camera was used. Date/Time The time stamp information in UTC with “yyyy-mm-ddThh:mm:ss” as format IMAGE The image name in the repository that include the time stamp and the type of camera used to take the photo Species The species’ Latin name checked with the taxonomy site www.fishbase.org bboxx1 [pixel] abscissa value of the first vertex of the tag bboxy1 [pixel] ordinate value of the first vertex of the tag bboxx2 [pixel] abscissa value of the second vertex of the tag bboxy2 [pixel] ordinate value of the second vertex of the tag bboxx3 [pixel] abscissa value of the third vertex of the tag bboxy3 [pixel] ordinate value of the third vertex of the tag bboxx4 [pixel] abscissa value of the fourth vertex of the tag bboxy4 [pixel] ordinate value of the fourth vertex of the tag Table 6. Details of the dataset with the tags of the fish specimens. The details of each variable of the dataset for the manual tagging of the OBSEA photos for the years 2013 and 2014 are reported here, with the timestamp in Universal Time Coordinates (UTC) and the bounding boxes (bbox) coordinates. 7 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ In order to filter these data, we applied a Quality Control (QC) procedure for all the environmental variables except for the solar irradiance and rain, considered prefiltered and institutional data. This procedure is based on the guidelines from the Quality Assurance of Real-Time Oceanographic Data (QARTOD), issued by the United States Integrated Ocean Observing System (US-IOOS) Program Office, as part of its Data MAnagement and Cyberinfrastructure (DMAC) (https://ioos.noaa.gov/project/qartod/). This QC procedure was based on the IOOS QC python tools (https://github.com/ioos/ioos_qc). Following the QARTOD guidelines, the following tests were applied: • Gross Range test. Highlight data points that exceeded sensors or operator selected minimum and maximum levels. • Climatology test. Data points that fall outside the seasonal ranges introduced by the operator. • Spike test. Data points n-1 that exceeded a selected threshold relative to adjacent points. • Rate of change test. Examination of excessive rises or falls in the data. • Flat line test. Examination of invariant values in the data. Column Labels Description Date/Time The time stamp information in UTC with “yyyy-mm-ddThh:mm:ss”, as format Temp [°C] average value of water temperature QF Water Temperature Quality Flag of the water temperature measurement Temp std dev [±]standard deviation of the water temperature measurement Cond [mS/cm] average value of conductivity QF conduct Quality Flag of the conductivity measurement Cond std dev [±]standard deviation of the conductivity measurement Press [dbar] average value of water pressure QF water press Quality Flag of the water pressure measurement Press std dev [±]standard deviation of the water pressure measurement Sal average value of water salinity QF sal Quality Flag of the water salinity measurement Sal std dev [±]standard deviation of the water salinity measurement SV [m/s] average value of sound velocity QF SV Quality Flag of the sound velocity measurement SV std dev [±]standard deviation of the sound velocity measurement Event “OBSEA:SBE16:2013_14” if the SEA-BIRD SBE16plus V2 SeaCAT device was used for the measurement, or “OBSEA:SBE37:2013_14” if the SEA-BIRD SBE 37-SMP MicroCAT device was used. Table 7. Details of the CTD probes measurements’ dataset. The details of each variable of the dataset for the OBSEA CTD probes for the years 2013 and 2014 are reported here with the timestamp in Universal Time Coordinates (UTC). Column Labels Description Date/Time The time stamp information in UTC with “yyyy-mm-ddThh:mm:ss” as format T air [K] average value of air temperature QF air temp Quality Flag of the air temperature measurement TTT std dev [±]standard deviation of the air temperature measurement ff [m/s] average value of wind speed QF wind speed Quality Flag of the wind speed measurement ff std [±]standard deviation of the wind speed measurement dd [deg] average value of wind direction QF wind dir Quality Flag of the wind direction measurement PPPP [hPa] average value of atmospheric pressure QF atmos press Quality Flag of the atmospheric pressure measurement PPPP std [±]standard deviation of the atmospheric pressure measurement RH [%] average value of relative humidity QF RH Quality Flag of the relative humidity measurement RH std [±]standard deviation of the relative humidity measurement Table 8. Details of the SARTI rooftop meteorological station dataset. The details of each variable of the dataset for the “Development Centre of Remote Acquisition and Information Processing” (SARTI) meteorological station for the years 2013 and 2014 are reported here with the timestamp in Universal Time Coordinates (UTC). 8 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ Column Labels Description Date/Time The time stamp information in UTC with “yyyy-mm-ddThh:mm:ss” as format E [W/m**2] value of Irradiance heat flux density measurement Rain [mm] value of rainfall measurement Table 9. Details of the Sant Pere de Ribes meteorological station dataset. The details of each variable of the dataset for the Sant Pere de Ribes meteorological station for the years 2013 and 2014 are reported here with the timestamp in Universal Time Coordinates (UTC). Station Variable Temporal Coverage (%) OBSEA sea water temperature 93.49 OBSEA sea water electrical pressure 93.49 OBSEA sea water salinity 89.74 UPC air temperature 94.68 UPC wind speed 94.68 UPC wind direction 94.68 St Pere solar irradiance 75.77 St Pere rain intensity 51.42 Table 10. Temporal coverage of the different environmental data. Temporal coverage as percentage (%) for the environmental data acquired at the OBSEA, and at the meteorological stations on the Polytechnic University of Catalonia (UPC) building in Vilanova i la Geltrù and in Sant Pere de Ribes during 2013 and 2014. Fig. 5 Time series plots of fish individuals. Here we report the time series for the 3 most abundant species (i.e., Diplodus vulgaris, Oblada melanura, and Chromis chromis) and total of individuals for the tagged fishes at the OBSEA platform between 2013 and 2014. 9 Scientific Data | (2023) 10:5 | https://doi.org/10.1038/s41597-022-01906-1 www.nature.com/scientificdata www.nature.com/scientificdata/ Each time that the quality test was run, each value of the dataset was flagged with a quality control code. The QC flags and meanings are shown in Table4. The oceanographic and meteorological data were annotated into comma delimited files (CSV) with additional information on QC flags, time stamps, and measurement devices used for their acquisition51–53. Data Records tagging outputs. All time-lapse images were saved with the filename indicating the date (i.e., the year, the month, and the day), the timestamp in Universal Time Coordinates (UTC) (i.e., hour, minutes and seconds), the name of the platform, and finally the camera used for the acquired image48. As a result, we had an inspected dataset of 33805 images, depicting a total of 69917 manually tagged fish specimens, 36777 of which pertaining to 29 different taxa (Fig.4) (Table5). The remaining specimens (i.e., 33140) were attributed to the unclassified category (see previous section). In the dataset file for manual tagging48, we reported the timestamp in UTC (yyyy-mm-ddThh:mm:ss) and the filename (e.g., timestamp associated) of the tagged image, plus the fish taxa name and the image vertices’ coordinates of the bounding box (bbox) containing the identified specimens in the OBSEA photo (Fig.4). Fig. 6 Time series plots of the environmental variables. Here we report the time series for the three oceanographic variables (i.e., water temperature, salinity and depth), and the five meteorological variables (i.e., air temperature, wind speed and direction, solar irradiance and rain) at the OBSEA platform, and meteorological stations on the “Development Centre of Remote Acquisition and Information Processing” (SARTI) rooftop and in Sant Pere de Ribes between 2013 and 2014. In the seawater temperature, pressure and salinity graphs we highlighted the use of SBE37 CTD probe with grey bands, and the SBE16 CTD probe with light yellow bands. The green points in the time series are the good quality data, the yellow ones the suspicious and the red ones the bad. Relative percentage of each QC Indexes was reported in the time series, except for rain and solar irradiance data, considered a prefiltered and institutional source (see previous section).