Search Results (17)

Energy Geographies has a unique position at the intersection of geospatial and social science, and it now faces a defining methodological development with the rapid rise in Geospatial Artificial Intelligence (GeoAI). This paper examines where GeoAI has and has not been applied within energy research through two bibliometric analyses using the Dimensions database. The first establishes an updated picture of energy geographies scholarship from 2020 to 2026, mapping the field’s current priorities and geographic distribution as a baseline for evaluating GeoAI’s role. The second conducts a bibliometric analysis of GeoAI-specific energy publications from 2020 to 2026, which reveals significant GeoAI Application Gaps: a heavy concentration in energy extraction and production research and in renewable energy siting and grid optimization, while energy transition, justice, and the energy problems of underrepresented regions remain substantially underserved. GeoAI energy research is also more geographically concentrated than the broader field, dominated by a small number of countries, raising questions about the applicability of these tools to the energy challenges facing the rest of the world. We argue that this gap reflects a pattern of problem selection as much as technological limitation, and that energy geographers are well positioned to redirect the development of this new field. We outline three directions for future research: developing Explainable GeoAI to ensure transparency and accountability, expanding geographic coverage to address data biases that favor a small set of well-resourced countries, and confronting the computational energy paradox of carbon-intensive AI applied to sustainability-oriented research. Full article

Wetlands and surface-water features regulate the thermal environment of cities through evaporative cooling, yet in arid metropolitan regions these hydrological buffers are scarce and rarely quantified against urban heat. Here, we link satellite-derived surface-water wetness to land surface temperature (LST) and urban heat island (UHI) intensity in Riyadh, Saudi Arabia, using an explainable Geospatial Artificial Intelligence (GeoAI) framework. We assembled 2000 cloud-masked Landsat 8/9 sample points for July 2014 and 2024 in Google Earth Engine and derived the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and two surface-water indices, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Water Index (NDWI), together with LST, UHI, terrain and population. Surface-water wetness was the strongest cool-side correlate of thermal stress: MNDWI related negatively to LST (r = −0.48) and to UHI intensity (r = −0.53), stronger than either vegetation or built-up density (both p < 0.001). Each 0.1 increase in MNDWI corresponded to a 2.2 °C reduction in LST. Five machine-learning algorithms predicted LST with test R² of 0.71–0.76 and UHI with R² of 0.68–0.72, and SHapley Additive exPlanations (SHAPs) identified MNDWI as the single most important thermal driver, ahead of elevation and vegetation. Point-level LST rose by 1.99 °C between 2014 and 2024 (p < 0.001), while open surface water was absent from all 2000 samples, indicating a hydrological deficit in the city’s thermal regulation. These findings suggest that protecting and expanding blue–green features along corridors such as Wadi Hanifah offers a measurable cooling lever for arid-city climate adaptation. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

The rapid transition toward renewable energy requires transparent and spatially explicit methods for identifying suitable photovoltaic (PV) development areas. This study develops a geospatial artificial intelligence (GeoAI) decision-support framework for PV site suitability assessment in Rajasthan, India. Eleven harmonized predictors were used: global horizontal irradiance (GHI), photovoltaic power output (PVOUT), temperature, wind speed, aerosol optical depth (AOD), elevation, slope, albedo, land use/land cover (LULC), distance to roads, and distance to power lines. Reference labels were generated from an explicit rule-derived suitability index, class thresholds, and exclusion logic; therefore, the machine-learning task was to reproduce a transparent suitability framework rather than to predict observed PV yield or project-level performance. Extreme Gradient Boosting (XGBoost) was compared with simpler baseline models, evaluated using random and spatial-block validation, and interpreted using SHapley Additive exPlanations (SHAP). Independent overlays with known solar-installation records, presence-background robustness testing, and uncertainty/sensitivity analysis were used to examine spatial plausibility, spatial autocorrelation, deterministic label effects, and parameter uncertainty. The resulting outputs include pixel-level suitability zones, contiguous candidate polygons, district-level capacity-oriented summaries, and planning-priority classes. The framework is intended as a risk-aware regional screening tool: high model agreement indicates consistency with the constructed suitability labels, while final project decisions require parcel-scale land, grid, environmental, social, and economic assessment. Full article

Wildfires pose significant threats to ecosystems, economies, and human health. The integration of remote sensing (RS), geospatial information systems (GIS), and artificial intelligence (AI) has emerged as a powerful approach for addressing wildfire-related challenges. However, existing review studies typically focus on specific wildfire tasks and lack a comprehensive synthesis of how geospatial data and supervised AI techniques interact across the full wildfire management cycle. Therefore, this study aims to provide a meta-analysis review of the integration of RS, GIS, and supervised AI methods in wildfire science. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to systematically analyze 449 peer-reviewed journal articles published between 1994 and 2024. The review examines various wildfire-related tasks, data sources, algorithmic approaches, spatial scales, performance metrics, and other aspects used in wildfire geospatial AI (GeoAI) studies. The results reveal a strong concentration of research on tasks such as burned area mapping (BAM), wildfire detection, and susceptibility mapping, while critical areas, such as fuel mapping, wildfire vulnerability, and post-fire recovery, remain underexplored. The analysis also identifies a dominant use of traditional machine learning (ML) algorithms, such as Random Forest (RF), and an increasing adoption of deep learning (DL) models, particularly convolutional neural networks (CNNs). Furthermore, the geographic distribution of studies highlights significant global disparities, with most research conducted in high-income regions, while wildfire-prone areas in developing regions remain underrepresented. The review also reveals limited adoption of advanced AI techniques, including transfer learning, transformer architectures, Geo-foundation AI models, and explainable AI (XAI). These findings provide a comprehensive synthesis of GeoAI applications in wildfire management and highlight critical methodological, geographic, and application-level gaps. Addressing these gaps through improved data accessibility, adoption of advanced AI methods, and increased research focus on underrepresented wildfire tasks and regions will be essential for developing scalable, interpretable, and globally applicable wildfire management systems. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article

Understanding how transport conditions and the built environment shape urban CO₂ emissions is critical for low-carbon urban development. This study analyses CO₂ emission intensity across fifty major Chinese cities using integrated ODIAC emissions, VIIRS night-time lights, traffic performance indicators, built-environment morphology, population/POI structure, and socioeconomic controls. We develop a GeoAI workflow that couples XGBoost modelling with SHAP interpretation, congestion-based city grouping, and 1 km grid-level GNNWR to map intra-urban spatial non-stationarity. The global model identifies night-time light intensity as the strongest predictor, followed by population density and building density. SHAP results reveal pronounced nonlinearities, with high sensitivity at low–medium levels and diminishing marginal effects as activity and density increase. Although transport indicators are less influential in the aggregate model, their roles differ across congestion regimes: in low-congestion cities, emissions align more consistently with overall activity intensity, whereas in high-congestion cities they respond more strongly to population distribution, motorisation, and built-form intensity, with less stable relationships. Grid-level GNNWR further shows that key mechanisms are spatially uneven within cities, with local effects concentrating in specific cores and corridors or fragmenting across multiple subareas. These findings demonstrate that emission drivers are context-dependent across and within cities. Accordingly, uncongested cities may gain more from activity-related energy-efficiency measures, while highly congested cities may require congestion-sensitive land-use planning, spatial-structure optimisation, and motorisation control. Integrating explainable GeoAI with regime differentiation and spatial heterogeneity mapping provides actionable evidence for targeted low-carbon planning. Full article

Understanding the spatial distribution and determinants of perceived fear of crime is essential for enhancing urban safety and promoting equitable city development. This study models and explains perceived fear of crime from street view imagery using a GeoAI framework that integrates deep learning, semantic segmentation, and explainable AI techniques. Focusing on Yeongdeungpo-gu in Seoul, South Korea—a district characterized by diverse urban morphologies—we collected 171,942 pairwise comparison responses through a large-scale crowdsourced survey designed to capture visual perceptions of crime-related fear. A Vision Transformer-based Siamese network (RSS-Swin) was employed to predict continuous fear-of-crime scores, while semantic segmentation (SegFormer-B5) and AutoML regression were applied to identify built-environment features influencing these perceptions. SHAP-based interpretability analysis was then used to quantify the importance and interactions of key visual elements. The results reveal that open and accessible streetscape components, such as roads and sidewalks, consistently mitigate perceived fear, whereas enclosed or unmanaged features, including walls, poles, and narrow alleys, heighten it. Moreover, the effects of vegetation, fences, and buildings vary across spatial contexts, emphasizing the need for place-sensitive interpretation. By integrating predictive modeling and explainable analysis, this study advances a transparent and scalable GeoAI framework for understanding the visual and environmental determinants of crime-related fear and supporting perception-aware CPTED strategies. Full article

As digital transformation intensifies, the governance of spatial data infrastructures is becoming increasingly dependent on the capacity to generate and sustain trust—technological, institutional and civic. This challenge is particularly acute in the Mediterranean region, where disparities in how geospatial data are produced, accessed, and validated are created by uneven digital development and fragmented governance structures. In response to this, this paper introduces an integrated framework combining geospatial artificial intelligence (GeoAI) and blockchain technologies to support transparent, verifiable and spatially explicit models of digital trust. Based on case studies from the Horizon 2020 TRUST project, the framework defines trust through territorial indicators across three dimensions: digital infrastructure, institutional transparency, and civic engagement. The system uses interpretable AI models, such as Random Forests, K-means clustering and convolutional neural networks, to classify regions into trust typologies based on multi-source geospatial data. These outputs are then transformed into semantically structured spatial products and anchored to the Ethereum blockchain via smart contracts and decentralized storage (IPFS), thereby ensuring data integrity, auditability and version control. Experimental results from pilot regions in Italy, Greece, Spain and Israel demonstrate the effectiveness of the framework in detecting spatial patterns of trust and producing interoperable, reusable datasets. The findings highlight significant spatial asymmetries in digital trust across the Mediterranean region, suggesting that trust is a measurable territorial condition, not merely a normative ideal. By combining GeoAI with decentralized verification mechanisms, the proposed approach helps to develop accountable, explainable and inclusive spatial data infrastructures, which are essential for democratic digital governance in complex regional environments. Full article

Cities are major sources of anthropogenic carbon dioxide (CO₂) emissions, making the study of intra-urban CO₂ concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO₂ concentrations in urban areas. We combined OCO-2 satellite-derived XCO₂ data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO₂ concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO₂ concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO₂ variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO₂ concentrations, offering insights for policy, planning, and mitigation. Full article

Understanding the spatial drivers of community satisfaction is crucial for achieving inclusive and sustainable urban development. However, traditional spatial regression models often assume linearity and fail to capture complex, spatially heterogeneous relationships. This study integrates a GeoShapley-based explainable GeoAI framework with the XGBoost algorithm to identify and quantify spatially varying factors influencing community satisfaction in Chengdu, China. By incorporating geographic coordinates as explicit spatial features, the GeoShapley method decomposes model outputs into intrinsic spatial effects and feature-specific interaction effects, enabling the interpretation of how and where each factor matters. Results show significant spatial clustering (Moran’s I = 0.60, p < 0.01) and a distinct south–north gradient in satisfaction. Built environment indicators—including building coverage ratio (BCR), walkability index (WI), and distance to green space (DGS)—exhibit nonlinear relationships and clear thresholds (e.g., BCR > 0.15, DGS > 590 m). Social vitality (Weibo check-ins) emerges as a key local differentiator, while education and healthcare accessibility remain spatially uniform. These findings reveal a dual structure of public service homogenization and spatial-quality heterogeneity, highlighting the need for place-specific, precision-oriented community renewal. The proposed GeoXAI framework provides a transferable pathway for integrating explainable AI into spatial sustainability research and urban governance. Full article

Extreme precipitation events, rapid urbanization, and irregular land use have significantly increased flood risk in recent years. In order to mitigate risks and enhance urban resilience, there is a need for the integration of innovative approaches with classical disaster management methods. This study uses geospatial artificial intelligence (GeoAI) methods to develop a flood risk analysis model. The proposed methodology is applied in the Marmara Region of Türkiye as a case study to highlight flood risk by evaluating factors such as precipitation, drainage density, and distance to waterways, population density, topography, water flow direction, and accumulation. Areas with high flood risk in the region are identified through the integration of hazard and vulnerability assessments, and explainable artificial intelligence (XAI) techniques are employed to identify the most significant factors contributing to flood susceptibility. Thus, a flood risk map of the Marmara Region is produced for eleven cities, utilizing open-source and government data to serve as an accessible guide for decision makers. This study aims to develop a flood risk analysis model through the integration of AHP-based hazard analysis and machine learning-based vulnerability assessment. This comprehensive hybrid approach facilitates the development of strategies for practical disaster risk reduction studies in a data-driven manner. Full article

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial. Full article

Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often lack a comprehensive analysis of these mechanisms due to methodological limitations. This study investigates the eco-environmental effects of land-use transitions in the Poyang Lake Region over the past 30 years from the perspective of the production-living-ecological space (PLES) framework. Additionally, a geographically explainable artificial intelligence (GeoXAI) framework is introduced to further explore the mechanisms underlying these eco-environmental effects. The GeoXAI framework effectively addresses the challenges of integrating nonlinear relationships and spatial effects, which are often not adequately captured by traditional models. The results indicate that (1) the conversion of agricultural space to forest and lake spaces is the primary factor contributing to eco-environmental improvement. Conversely, the occupation of forest and lake spaces by agricultural and residential uses constitutes the main driver of eco-environmental degradation. (2) The GeoXAI demonstrated excellent performance by incorporating geographic variables to address the absence of spatial causality in traditional machine learning. (3) High-altitude and protected water areas are more sensitive to human activities. In contrast, geographic factors have a greater impact on densely populated urban areas. The results and methodology presented here can serve as a reference for eco-environmental assessment and decision-making in other areas facing similar land-use transformation challenges. Full article

A novel framework called TravelRAG is introduced in this paper, which is built upon a large language model (LLM) and integrates Retrieval-Augmented Generation (RAG) with knowledge graphs to create a retrieval system framework designed for the tourism domain. This framework seeks to address the challenges LLMs face in providing precise and contextually appropriate responses to domain-specific queries in the tourism field. TravelRAG extracts information related to tourist attractions from User-Generated Content (UGC) on social media platforms and organizes it into a multi-layer knowledge graph. The travel knowledge graph serves as the core retrieval source for the LLM, enhancing the accuracy of information retrieval and significantly reducing the generation of erroneous or fabricated responses, often termed as “hallucinations”. As a result, the accuracy of the LLM’s output is enhanced. Comparative analyses with traditional RAG pipelines indicate that TravelRAG significantly boosts both the retrieval efficiency and accuracy, while also greatly reducing the computational cost of model fine-tuning. The experimental results show that TravelRAG not only outperforms traditional methods in terms of retrieval accuracy but also better meets user needs for content generation. Full article