GeoAI at the forefront of climate action: Mapping mitigation and adaptation with Artificial Intelligence

 Corresponding author: OHAKA AMARACHI MAVISCLARA. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. GeoAI at the forefront of climate action: Mapping mitigation and adaptation with Artificial Intelligence OHAKA AMARACHI MAVISCLARA 1, *, Jeffery Omozokpia Esekie 2, Temitope Olufunmi Atoyebi 3, Prayer Erumusele ATUMAH 4, Oluwatoyin Olawale Akadiri 5, Rildwan Adekunle JIMOH 6 and ISIAKA OSHOBUGIE IBRAHIM 7 1 Department of Geography and Planning, Abia State University Uturu, Nigeria. 2 Department of Civil Engineering, University of Benin, Nigeria. 3 Department of Information Technology and Information Systems, Nile University of Nigeria, Abuja, NIGERIA. 4 Department of Geology, University of Benin, NIGERIA. 5 Department of Information Sciences, Bay Atlantic University, United States. 6 Department of Computer science, Federal University of Technology Akure, Nigeria. 7 Department of Engineering Management, University of Houston Clear Lake, USA. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 Publication history: Received on 12 July 2025; revised on 18 August 2025; accepted on 21 August 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.24.2.0248 Abstract GeoAI, merging artificial intelligence with geospatial data, is transforming climate change mitigation and adaptation. This review synthesizes 2020–2025 advancements, focusing on deep learning models like convolutional neural networks (CNNs) and transformers, achieving 90–95% accuracy in flood prediction, carbon sequestration mapping, and urban heat mitigation. Key mitigation strategies include forest biomass estimation in the Amazon and renewable energy optimization in India, while adaptation efforts encompass real-time flood mapping in Bangladesh and coastal resilience modeling in the Pacific Islands. Despite successes, challenges persist, including data biases, computational costs, and ethical concerns like privacy in urban GeoAI applications. Public discourse on platforms like X highlights demand for equitable climate solutions, reflected in discussions on wildfires and Arctic rain. Future directions involve federated learning for privacy-preserving GeoAI and generative AI for climate scenario modeling. Aligning with Sustainable Development Goal 13, GeoAI offers transformative potential to enhance global climate resilience, necessitating investment in open-access tools and interdisciplinary collaboration to address research gaps and ensure inclusivity. Keywords: Geoai; Deep Learning; Climate Change; Mitigation; Adaptation; Sustainability; Geospatial Analysis. 1. Introduction Climate change, with its escalating impacts like rising sea levels and intensifying wildfires, demands innovative solutions to mitigate emissions and adapt to irreversible shifts, positioning GeoAI as a transformative tool [1]. This review synthesizes 2020–2025 advancements in GeoAI, integrating artificial intelligence with geospatial data to map climate mitigation and adaptation strategies, aligning with Sustainable Development Goal 13 (Climate Action) [2]. By examining applications, challenges, and future directions, the article underscores GeoAI’s role in addressing global environmental crises. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 218 1.1. The Climate Crisis Context The climate crisis has intensified, with global temperatures rising 1.1°C above pre-industrial levels and projections estimating a 0.4m sea-level rise by 2100 [1]. According to IPCC et al. [2022], extreme weather events, including floods and wildfires, have increased in frequency, disrupting ecosystems and economies, particularly in vulnerable regions like Sub-Saharan Africa and South Asia [1]. For instance, the 2025 Pantanal wildfires devastated 17% of Brazil’s wetlands, highlighting the urgency for scalable solutions [3]. GeoAI, leveraging satellite imagery and deep learning, offers precision in monitoring these impacts, enabling timely interventions [4]. Economic losses from climate-related disasters reached $270 billion annually by 2023, underscoring the need for proactive strategies [5]. Smith et al. [2023] emphasize that traditional geospatial tools lack the computational power to process vast datasets like Sentinel-2 imagery, limiting real-time responses [5]. GeoAI addresses this gap, achieving 95% accuracy in flood mapping and carbon monitoring, as demonstrated in Bangladesh and the Amazon [6]. Public discourse on X, with hashtags like #ClimateAction, reflects growing awareness, amplifying the demand for technology-driven solutions [7]. The societal implications of climate change, including displacement and food insecurity, further elevate GeoAI’s relevance [8]. Findings from Jones et al. [2024] indicate that 200 million people could be displaced by 2050 due to sealevel rise, necessitating adaptive measures like coastal resilience modeling [8]. By integrating AI with GIS, GeoAI empowers policymakers to prioritize resources, aligning with global frameworks like the Paris Agreement [9]. This subsection sets the stage for exploring GeoAI’s potential to transform climate action. 1.2. Emergence of GeoAI GeoAI, the convergence of artificial intelligence and geospatial analysis, has emerged as a pivotal tool since 2020, revolutionizing climate science [10]. According to Guo et al. [2023], GeoAI employs deep learning models like convolutional neural networks (CNNs) and transformers to process geospatial data, achieving 90–95% accuracy in tasks like land-use classification [10]. For example, Sentinel-2 imagery analysis in the Arctic has improved ice melt monitoring, informing mitigation strategies [11]. This precision distinguishes GeoAI from traditional GIS, which struggles with big data [12]. The rise of GeoAI coincides with advancements in computational power and data availability [13]. Zhang et al. [2022] highlight that cloud platforms like Google Earth Engine enable real-time processing of petabytes of satellite data, facilitating applications from urban heat mitigation to deforestation tracking [13]. In India, GeoAI optimized solar energy site selection, boosting efficiency by 15% [14]. Such successes have spurred global adoption, with 60% of climate research incorporating AI by 2024, per a ScienceDirect analysis [15]. GeoAI’s interdisciplinary nature bridges geography, computer science, and environmental policy [16]. Findings from Li et al. [2024] indicate that GeoAI’s ability to predict flood risks in Bangladesh with 95% accuracy has saved lives, demonstrating its societal impact [16]. However, high computational costs and data biases pose challenges, particularly in low-resource regions [17]. X discussions on #GeoAI underscore public excitement for its potential, setting the context for its climate applications. 1.3. Scope and Objectives This review synthesizes GeoAI’s contributions to climate action from 2020–2025, focusing on mitigation and adaptation [18]. According to Brown et al. [2024], GeoAI’s applications span carbon sequestration, renewable energy optimization, flood prediction, and urban heat mitigation, each addressing critical climate challenges [18]. The primary objective is to evaluate these applications’ effectiveness, using case studies like Amazon deforestation monitoring and African flood mapping [6]. A secondary goal is to assess technical and ethical challenges, ensuring a balanced perspective [19]. The scope encompasses peer-reviewed literature and credible internet sources, including X posts reflecting public sentiment [7]. Findings from Khan et al. [2025] highlight GeoAI’s 90% accuracy in wildfire detection in California, illustrating its practical impact [20]. The review prioritizes global examples to reflect diverse needs, from coastal resilience in Oceania to emission reduction in Asia [21]. By limiting references to 65, the article maintains conciseness while covering key advancements [22]. Another objective is to propose future directions, such as federated learning for privacy-preserving GeoAI [23]. Wang et al. [2023] suggest that open-access platforms could democratize GeoAI, benefiting low-income regions [24]. The Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 219 review also examines public engagement, with X discussions on #ClimateJustice emphasizing equity in technology deployment [25]. This subsection clarifies the article’s focus, guiding readers through its structure. 1.4. Significance for Climate Action GeoAI’s significance lies in its ability to address climate change’s multifaceted challenges, aligning with SDG 13 [2]. According to IPCC et al. [2022], achieving net-zero emissions by 2050 requires scalable tools like GeoAI for carbon monitoring and renewable energy planning [1]. For instance, CNN-based forest biomass estimation in the Amazon supports carbon markets, reducing emissions by 10% in test areas [6]. Such impacts underscore GeoAI’s policy relevance [9]. Adaptation strategies benefit equally, with GeoAI enabling resilient infrastructure [16]. Findings from Lee et al. [2024] indicate that urban heat mitigation in European cities, using GeoAI-driven green space planning, reduced temperatures by 2°C, improving public health [26]. In Bangladesh, real-time flood mapping saved 50,000 lives in 2023, per a ScienceDirect study [16]. These successes highlight GeoAI’s life-saving potential, resonating with X discussions on #ClimateAction [7]. GeoAI also fosters public and private sector collaboration [27]. Chen et al. [2023] note that GeoAI’s economic benefits, like 70% cost savings in land-use mapping, attract investment, with the GeoAI market projected to reach $10 billion by 2030 [27]. However, equitable access remains critical, as X posts on #ClimateJustice emphasize [25]. This subsection underscores GeoAI’s transformative role, justifying the review’s focus. 2. Foundations of GeoAI in Climate Science GeoAI, the fusion of artificial intelligence and geospatial technologies, forms the backbone of innovative climate change solutions, enabling precise monitoring and prediction [28]. This section explores GeoAI’s technical foundations, evolution, climate-specific capabilities, and global adoption trends, providing a framework for understanding its mitigation and adaptation applications. 2.1. Defining GeoAI GeoAI integrates AI techniques, such as deep learning, with geospatial data to analyze environmental systems [28]. According to Rolnick et al. [2022], GeoAI leverages models like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and transformers to process satellite imagery (e.g., Sentinel-2), LiDAR, and GIS data, achieving 90–95% accuracy in tasks like land-use classification [28]. For instance, CNNs excel in identifying deforestation patterns in the Amazon, supporting carbon sequestration efforts [6]. Unlike traditional GIS, GeoAI handles massive datasets, making it ideal for climate applications [12]. The core strength of GeoAI lies in its ability to extract spatial and temporal patterns [29]. Findings from VoPham et al. [2022] indicate that GeoAI’s use of Sentinel-3 imagery improved sea surface temperature monitoring, critical for predicting marine heatwaves [29]. In 2023, over 50% of geospatial studies incorporated AI, per a ScienceDirect analysis, reflecting its growing dominance [15]. GeoAI’s versatility spans urban planning, disaster response, and climate modeling, as seen in flood prediction in Bangladesh [16]. GeoAI’s data sources are diverse, including open-access platforms like Google Earth Engine [30]. Chen et al. [2023] highlight that these platforms democratize access, enabling researchers in Africa to map drought risks with 85% accuracy [30]. However, challenges like data preprocessing complexity persist, requiring specialized skills [17]. X posts on #GeoAI emphasize public curiosity about its climate potential, underscoring its relevance [7]. This subsection defines GeoAI’s technical scope, setting the stage for its climate applications. 2.2. Evolution of GeoAI GeoAI has evolved rapidly since 2020, driven by computational advancements and data availability [31]. According to Zhu et al. [2023], the transition from traditional machine learning to deep learning models like transformers has improved GeoAI’s predictive power, achieving 95% accuracy in urban heat mapping [31]. For example, transformerbased models enhanced sea-level rise projections for Pacific Islands, informing adaptation strategies [32]. This evolution reflects a 70% increase in GeoAI publications from 2020 to 2024, per SCOPUS data [33]. The integration of cloud computing has been pivotal [34]. Findings from Yang et al. [2022] indicate that platforms like AWS and Google Cloud reduced processing times for satellite imagery by 80%, enabling real-time wildfire detection in Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 220 California [34]. In 2023, GeoAI’s application in India’s solar energy mapping boosted efficiency by 15% [14]. Public interest, reflected in X discussions on #ClimateTech, highlights GeoAI’s growing visibility [35]. GeoAI’s interdisciplinary growth bridges geography and AI [36]. Li et al. [2024] note that collaborations between computer scientists and climatologists have refined models, such as LSTMs for drought forecasting in East Africa [36]. However, high computational costs limit adoption in low-resource regions [17]. The evolution of GeoAI underscores its transformative potential, preparing readers for its climate-specific roles. 2.3. Climate-Relevant Capabilities GeoAI’s capabilities in climate science include real-time monitoring, predictive modeling, and decision support [11]. According to Zhang et al. [2024], CNNs analyzing MODIS imagery detected Arctic ice melt with 90% accuracy, informing mitigation policies [11]. In Bangladesh, GeoAI’s flood prediction models, achieving 95% accuracy, saved 50,000 lives in 2023 [16]. These capabilities address urgent climate needs, aligning with SDG 13 [2]. Predictive analytics is a cornerstone of GeoAI [37]. Findings from Patel et al. [2023] indicate that LSTM models forecasted drought risks in Sub-Saharan Africa, enabling preemptive agricultural planning [37]. In 2024, GeoAI’s wildfire detection in Australia, using drone imagery, reduced response times by 40% [38]. X posts on #ClimateAction reflect public appreciation for such life-saving technologies [7] Table 1 Summarizes key models, their data inputs, applications, and performance metrics, highlighting their role in mitigation and adaptation strategies [11, 16, 28].” Model Type Data input Climate application Region Performance metrics Limitations CNN Sentinel 2, MODIS Deforestation monitoring Amazon 90% accuracy Dense canopy resolution LSTM Rainfall, River Flow Drought Forecasting Sub-Saharan Africa 85% accuracy Data scarcity in rural area Transformer LiDAR, Tide Gauge Sea-Level Rise Prediction Pacific Islands 90% accuracy High computational cost Random forest IoT, GIS Urban Heat Mapping Europe 88% accuracy Limited interpretability Hybrid ML Satellite, Sensors Wildfire Detection Australia 40% faster response Model generalizability GeoFM Multispectral, Imagery Hydrological Modeling East Africa 92% accuracy Requires spatial knowledge Decision support systems powered by GeoAI enhance policy-making [6]. Liu et al. [2022] highlight that GeoAI’s carbon sequestration models in the Amazon guided reforestation, increasing carbon storage by 10% [6]. However, data resolution limits precision in remote regions [39]. GeoAI’s ability to integrate diverse data sources, like LiDAR and social media, strengthens its climate applications [40]. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 221 Figure 1 GeoAI Workflow for Landslide Susceptibility Mappings “This illustrates a GeoAI workflow for landslide susceptibility mapping, showcasing how machine learning integrates satellite and GIS data to predict climate-driven hazards, enhancing disaster preparedness [6, 43].” 2.4. Global Adoption Trends GeoAI’s adoption has surged globally, with diverse applications [18]. According to Brown et al. [2024], North America leads in urban GeoAI, with 80% of smart city projects using deep learning for traffic and heat management [18]. In Asia, India’s GeoAI-driven solar mapping supports renewable energy goals, per a 2023 study [14]. Europe focuses on adaptation, with GeoAI reducing urban heat in cities like Paris [26]. Africa’s adoption is growing despite challenges [41]. Findings from Osei et al. [2023] indicate that GeoAI mapped drought risks in Ghana with 85% accuracy, aiding farmers [41]. In Oceania, transformer models improved coastal resilience planning in Fiji, per a 2024 study [42]. X discussions on #GeoAI highlight public enthusiasm for these regional impacts [35]. Interdisciplinary collaboration drives adoption [36]. Li et al. [2023] note that partnerships between governments and tech firms have scaled GeoAI in 70% of climate projects [36]. However, Global South regions face barriers like data scarcity [43]. GeoAI’s market, valued at $5 billion in 2024, is projected to reach $10 billion by 2030, per ScienceDirect [27]. 3. GeoAI Applications in Climate Mitigation GeoAI’s transformative potential in climate mitigation lies in its ability to reduce greenhouse gas emissions and enhance carbon sequestration, leveraging deep learning and geospatial data [28]. This section examines GeoAI’s applications in Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 222 mapping carbon sinks, optimizing renewable energy, reducing emissions, and impactful case studies, highlighting their contributions to global climate goals. 3.1. Mapping Carbon Sinks GeoAI’s application in mapping carbon sinks, particularly forests, is pivotal for climate mitigation [6]. The study by Zhang et al. [2023] shows that convolutional neural networks (CNNs) analyzing Sentinel-2 imagery achieved 90% accuracy in estimating forest biomass in the Amazon, supporting reforestation efforts [6]. These models quantify carbon storage, guiding carbon credit markets that incentivize preservation, with Brazil’s carbon market growing by 25% in 2024 [6]. GeoAI’s precision surpasses traditional methods, which often underestimate biomass by 15% [12]. Deforestation monitoring is a critical GeoAI application [6]. According to Liu et al. [2022], GeoAI’s real-time analysis of MODIS data detected illegal logging in the Congo Basin, reducing deforestation rates by 10% in test areas [6]. In 2023, GeoAI mapped 70% of global forest cover changes, per a MDPI Forests study, aiding policy enforcement [6]. X posts on #Deforestation highlight public support for such technologies, amplifying their impact [7]. Challenges include data resolution in dense canopies [39]. Findings from Chen et al. [2023] indicate that LiDAR integration improves accuracy but increases computational costs, limiting adoption in low-resource regions [39]. In Indonesia, GeoAI-driven mangrove restoration enhanced carbon sequestration by 12%, per a 2024 study [44]. GeoAI’s role in carbon sink mapping underscores its potential to achieve net-zero goals [1]. GeoAI also supports soil carbon monitoring [45]. Wang et al. [2024] note that deep learning models analyzing hyperspectral imagery mapped soil organic carbon in Australia with 85% accuracy, informing agricultural practices [45]. This application, combined with forest mapping, positions GeoAI as a cornerstone of carbon mitigation strategies [2]. 3.2. Optimizing Renewable Energy GeoAI enhances renewable energy deployment by optimizing site selection for wind and solar projects [14]. The study by Patel et al. [2022] shows that GeoAI, using CNNs and GIS data, mapped solar potential in India with 95% accuracy, boosting energy efficiency by 15% [14]. By 2024, India’s solar capacity increased by 20%, partly due to GeoAI-guided planning [14]. Such applications align with global renewable energy targets [9]. Wind energy benefits similarly [46]. According to Gupta et al. [2023], GeoAI’s analysis of wind speed data in Europe identified optimal turbine sites, reducing installation costs by 18% [46]. In 2023, GeoAI supported 30% of new wind projects globally, per Energy Policy [14]. X discussions on #RenewableEnergy reflect public enthusiasm for these advancements [35]. Challenges include integrating diverse datasets [17]. Findings from Khan et al. [2023] indicate that combining meteorological and topographic data requires advanced preprocessing, increasing computational demands [17]. In China, GeoAI optimized offshore wind farms, increasing output by 12%, per a 2024 study [47]. GeoAI’s scalability makes it vital for transitioning to clean energy [2]. GeoAI also predicts energy demand [36]. Li et al. [2023] note that LSTM models forecasted solar energy needs in urban areas, improving grid stability [36]. This capability, demonstrated in California’s smart grids, underscores GeoAI’s role in sustainable energy systems, supporting SDG 7 [2]. 3.3. Reducing Emissions GeoAI reduces emissions by optimizing urban systems, particularly transportation [10]. The study by Wang et al. [2023] shows that GeoAI-driven traffic flow modeling in Beijing, using IoT and CNNs, cut emissions by 20% in 2023 [10]. By analyzing real-time traffic data, GeoAI minimizes congestion, a major source of urban CO2 [18]. This application is critical as cities account for 70% of global emissions [1]. Industrial emissions are another focus [48]. According to Chen et al. [2024], GeoAI monitored factory emissions in Southeast Asia using satellite imagery, enabling 15% reductions through targeted regulations [48]. In 2024, 40% of emission monitoring programs adopted GeoAI, per Environmental Science & Policy [9]. X posts on #ClimateAction highlight public demand for cleaner cities [7]. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 223 Challenges include model generalizability [17]. Findings from Brown et al. [2023] indicate that GeoAI models trained on urban data may underperform in rural areas, requiring localized datasets [17]. In Europe, GeoAI optimized public transit in Berlin, reducing emissions by 10%, per a 2023 study [18]. GeoAI’s urban applications are key to low-carbon futures [2]. GeoAI also supports agricultural emission reductions [49]. Zhang et al. [2024] note that deep learning models mapped methane emissions from rice paddies in India, guiding sustainable practices [49]. These diverse applications demonstrate GeoAI’s versatility in emission mitigation [28]. 3.4. Case Studies and Impacts GeoAI’s mitigation impact is best illustrated through case studies [6]. The study by Zhang et al. [2023] shows that GeoAI’s deforestation monitoring in the Amazon, using CNNs, reduced illegal logging by 12% in 2023, preserving 10 million tons of carbon [6]. This effort supported Brazil’s Paris Agreement commitments, per Nature Sustainability [9]. Public engagement, reflected in X posts on #AmazonRainforest, underscores its global significance [7] Figure 2 Vegetation Dynamics in Arid Region This figure shows a spatial map of vegetation changes in arid and semi-arid regions, driven by climate change and human activities, using GeoAI-based remote sensing. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 224 Table 2 GeoAI’s mitigation impacts across regions, highlighting applications and outcomes, underscoring its role in emission reduction [6, 10, 14].” Region Application Technology Data source Impact Economic benefit Amazon Deforestation Monitoring CNN,Sentinel2 Satellite, Imagery 12%reduction in illegal logging $10M in carbon credit India Solar Site Selection CNN, GIS Topographic Data 20%increase in solar capacity $50M energy savings China Traffic Optimization IoT, CNN Real-Time Traffic 15%emission reduction $50M fuel savings Congo Basin Reforestation Mapping MODIS, DL Satellite Imagery 7% increase in carbon storage $5M in ecosystem services Southeast Asia Emission Monitoring Satellite, ML Industrial Data 15%emission reduction $20Mregulatory Savings In India, GeoAI’s solar mapping accelerated renewable energy adoption [14]. According to Patel et al. [2022], GeoAI identified 500,000 hectares of high-potential solar sites, contributing to 20% of India’s 2024 solar capacity [14]. This reduced coal reliance by 8%, per Renewable Energy [14]. Such impacts highlight GeoAI’s economic and environmental benefits [28]. In China, GeoAI’s traffic optimization in Shanghai cut emissions by 15% in 2024 [10]. Findings from Wang et al. [2023] indicate that IoT integration enhanced model accuracy, saving $50 million in fuel costs [10]. X discussions on #SmartCities amplify public interest [35]. These case studies demonstrate GeoAI’s scalability [36] GeoAI’s global reach is evident in Africa, where carbon sink mapping in the Congo Basin supported reforestation [6]. Liu et al. [2022] note that GeoAI increased carbon storage by 7%, aligning with SDG 13 [6]. These case studies underscore GeoAI’s transformative role in climate mitigation [2]. 4. GeoAI Applications in Climate Adaptation GeoAI’s role in climate adaptation harnesses deep learning and geospatial data to enhance resilience against climate impacts like floods, urban heat, and sea-level rise [16]. This section explores GeoAI’s applications in flood prediction, urban heat mitigation, coastal resilience, and impactful case studies, highlighting their contributions to sustainable adaptation strategies. 4.1. Flood Prediction and Response GeoAI revolutionizes flood prediction by leveraging deep learning for real-time risk assessment [16]. Research done by Li et al. [2024] shows that convolutional neural networks (CNNs) analyzing Sentinel-2 imagery predicted floods in Bangladesh with 95% accuracy, enabling timely evacuations that saved 50,000 lives in 2023 [16]. These models process rainfall and topographic data, outperforming traditional hydrological models by 20% [16]. GeoAI’s rapid response capabilities are critical for flood-prone regions [1]. Real-time flood mapping enhances disaster response [50]. According to Khan et al. [2025], transformer models integrated with GIS data mapped flood extents in East Africa, reducing response times by 40% during 2024 monsoons [50]. By 2025, 60% of global flood prediction systems adopted GeoAI, per Journal of Hydrology [16]. X posts on #FloodResilience reflect public appreciation for these life-saving technologies [7]. Challenges include data scarcity in rural areas [17]. Findings from Khan et al. [2023] indicate that limited ground-truth data in Sub-Saharan Africa reduces model accuracy by 10% [17]. In Pakistan, GeoAI-driven flood forecasting mitigated damages worth $100 million in 2023, per a ScienceDirect study [51]. GeoAI’s scalability strengthens adaptation in vulnerable regions [2]. Global Journal of Engineering and Technology Advances, 2025, 24(02), 217-234 225 GeoAI also supports early warning systems [52]. The study by Rahman et al. [2024] shows that LSTM models, analyzing real-time river flow data, improved flood warnings in India, increasing evacuation success by 30% [52]. This application underscores GeoAI’s role in enhancing community resilience [9]. 4.2. Mitigating Urban Heat GeoAI mitigates urban heat islands, a growing climate challenge in cities [26]. Research done by Lee et al. [2024] shows that GeoAI, using CNNs and street view imagery, mapped urban green spaces in European cities, reducing temperatures by 2°C in 2024 [26]. This approach optimized green roof placement, cutting cooling energy costs by 15% [18]. Urban heat mitigation is vital as cities face 50% more heatwaves by 2030 [1]. GeoAI’s integration with IoT enhances precision [10]. According to Wang et al. [2023], IoT sensors combined with deep learning models monitored urban heat in Beijing, informing real-time mitigation strategies [10]. In 2024, 70% of smart cities adopted GeoAI for heat management, per Sustainable Cities and Society [18]. X discussions on #UrbanClimate highlight public demand for cooler cities [35]. Challenges include model interpretability [17]. Findings from Brown et al. [2023] indicate that complex GeoAI models require simplified outputs for urban planners, limiting adoption [17]. In Singapore, GeoAI-driven urban planning reduced heat stress by 12%, per a 2023 study [53]. This application improves public health and livability [2]. GeoAI also predicts heatwave impacts [36]. The study by Li et al. [2023] shows that transformer models forecasted heat stress in North American cities, guiding public health responses [36]. These efforts demonstrate GeoAI’s role in adapting urban environments to climate change [9]. 4.3. Enhancing Coastal Resilience GeoAI strengthens coastal resilience against sea-level rise and storms [32]. Research done by Patel et al. [2023] shows that transformer models analyzing LiDAR data predicted sea-level rise impacts in the Pacific Islands with 90% accuracy, informing infrastructure planning [32]. By 2024, GeoAI supported 50% of coastal adaptation projects, per Ocean & Coastal Management [42]. Coastal regions face a 0.4m sea-level rise by 2100, necessitating such tools [1]. Storm surge modeling is a key application [54]. According to Taylor et al. [2024], GeoAI’s integration of satellite and tide gauge data improved storm surge predictions in the Caribbean, reducing damages by 25% [54]. X posts on #CoastalResilience reflect public concern for vulnerable island nations [35]. GeoAI’s precision enhances adaptation planning [9]. Challenges include high computational costs [39]. Findings from Chen et al. [2023] indicate that LiDAR-based models require expensive GPU resources, limiting use in small island states [39]. In Bangladesh, GeoAI mapped coastal erosion, guiding mangrove restoration that reduced flooding by 15%, per a 2024 study [44]. This application supports ecosystem-based adaptation [2]. GeoAI also aids relocation planning [8]. The study by Jones et al. [2024] shows that GeoAI identified safe zones for 10,000 coastal residents in Fiji, mitigating displacement risks [8]. These efforts highlight GeoAI’s role in protecting coastal communities [2]. 4.4. Case Studies and Impacts GeoAI’s adaptation impact shines through global case studies [50]. Research done by Khan et al. [2025] shows that GeoAI’s flood mapping in East Africa, using transformers, reduced economic losses by $200 million in 2024 [50]. This effort supported humanitarian aid, aligning with SDG 13 [2]. X posts on #FloodResilience amplify public support [7 In Europe, GeoAI’s urban heat mitigation transformed cities [26]. According to Lee et al. 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