Geospatial AI: Systems, Model, Methods, and Applications

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Atmosphere atmosphere	2.3	4.9	2010	19.7 Days	CHF 2400	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	33.1 Days	CHF 1900	Submit
Land land	3.2	5.9	2012	17.5 Days	CHF 2600	Submit
Systems systems	3.1	4.1	2013	20.1 Days	CHF 2400	Submit
Urban Science urbansci	2.9	3.7	2017	21.6 Days	CHF 1800	Submit
Water water	3.0	6.0	2009	18.9 Days	CHF 2600	Submit

25 pages, 41819 KB

Open AccessArticle

Comparative Analysis of Machine Learning–Kriging Integrative Approaches for Enhanced Spatial Prediction of Mineral Exploration Data

by Hosang Han and Jangwon Suh

ISPRS Int. J. Geo-Inf. 2026, 15(4), 175; https://doi.org/10.3390/ijgi15040175 - 15 Apr 2026

Viewed by 427

Abstract

Accurate prediction of mineral concentrations from sparse exploration data is important for resource estimation. This study evaluates hybrid prediction models combining machine learning (ML) and geostatistics to predict aluminum (Al) concentrations. Twelve hybrid configurations were generated by combining six ML backbones—Random Forest, XGBoost, [...] Read more.

Accurate prediction of mineral concentrations from sparse exploration data is important for resource estimation. This study evaluates hybrid prediction models combining machine learning (ML) and geostatistics to predict aluminum (Al) concentrations. Twelve hybrid configurations were generated by combining six ML backbones—Random Forest, XGBoost, AdaBoost, ResNet, U-Net, and Spatial Transformer Network—with Ordinary Kriging (OK) and Universal Kriging (UK). Model performance was evaluated using 10-fold spatial cross-validation (CV) to reduce spatial leakage, and hyperparameters were tuned by grid-search CV within the training folds. For the hybrid models, residual kriging was fitted using cross-fitted out-of-fold residuals to reduce optimistic bias and prevent information leakage. The results showed no consistent performance separation between OK and UK variants. More importantly, the effect of integration was backbone dependent rather than uniformly beneficial. RF-based predictions showed the strongest overall out-of-sample performance, whereas hybrid gains for other backbones were generally modest. After multiple-comparison correction, most differences between standalone and hybrid models were not statistically significant. These findings indicate that increasing model complexity through hybridization does not guarantee improved accuracy and highlight the importance of spatially explicit, bias-aware evaluation when selecting prediction strategies for mineral resource exploration. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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29 pages, 30463 KB

Open AccessArticle

Gray–Green Spatial Structure and Nonlinear Threshold Effects on Street Crime: A CatBoost-Based Analysis of Day–Night Patterns in Shanghai

by Xuefei Gu and Jieun Seo

ISPRS Int. J. Geo-Inf. 2026, 15(4), 156; https://doi.org/10.3390/ijgi15040156 - 3 Apr 2026

Viewed by 490

Abstract

Under rapid urbanization, street crime poses growing challenges to urban safety. Existing studies often treat gray and green spaces as independent variables, limiting the understanding of nonlinear crime patterns and spatiotemporal heterogeneity. Using day–night street crime data from Shanghai between 2010 and 2020, [...] Read more.

Under rapid urbanization, street crime poses growing challenges to urban safety. Existing studies often treat gray and green spaces as independent variables, limiting the understanding of nonlinear crime patterns and spatiotemporal heterogeneity. Using day–night street crime data from Shanghai between 2010 and 2020, this study applies an interpretable machine learning framework combining CatBoost and SHAP to examine how the coupling of gray–green spatial structures influences street crime. Gray–green spatial morphology is quantified using both MSPA- and Fragstats-based indicators, which are integrated into composite coupling indices. The results indicate that gray–green structural coupling exhibits significant nonlinear and threshold-dependent effects on street crime. Compared with conventional Fragstats metrics, MSPA-based structural indicators demonstrate stronger explanatory power. Theft-specific analysis further indicates that gray-space core–edge structures exhibit higher crime risk at night, with this effect becoming more pronounced in the later period. Across both study periods and day–night contexts, green branch areas (G_BRANCH) consistently show stable inhibitory effects, with the strongest suppression occurring when G_BRANCH values range between 0 and 1.6 and interact with gray core–edge structures (B_CORE and B_EDGE). These findings provide quantitative evidence that gray–green spatial structures function through coupled, nonlinear interactions and offer targeted spatial planning implications for crime prevention in high-density cities. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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28 pages, 7008 KB

Open AccessArticle

Multimodal Deep Learning Framework for Profiling Socio-Economic Indicators and Public Health Determinants in Urban Environments

by Esaie Dufitimana, Jean Pierre Bizimana, Ernest Uwayezu, Paterne Gahungu and Emmy Mugisha

Urban Sci. 2026, 10(4), 177; https://doi.org/10.3390/urbansci10040177 - 25 Mar 2026

Viewed by 504

Abstract

Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, [...] Read more.

Urbanization significantly enhances socio-economic conditions, health, and well-being for many by improving access to services, education, and economic opportunities. However, socio-economic and public health disparities are also being exacerbated by urbanization. The reliable data required to monitor these conditions are often unavailable, outdated, or inconsistent. This study introduces a multimodal deep learning framework that integrates satellite imagery with street network datasets to predict urban socio-economic indicators and public health determinants at the sector level as a political administrative unit of public health planning in Rwanda. We extracted latent visual and topological embeddings of the urban built environment, using a Convolutional Neural Network (CNN) and Graph Neural Network (GNN). These embeddings were fused through an attentional mechanism to train a multi-task regression model that simultaneously predicts multiple socio-economic indicators and public health determinants. This framework was applied to the City of Kigali in Rwanda. Overall, the multimodal fusion model achieved the best average performance across targets, with an average correlation of 0.68 and MAE of 1.26 for socio-economic indicators, and 0.68 and 1.46 for public health determinants, demonstrating the benefit of integrating visual and topological information. The learned fused embedding space arranges socio-economic indicators and public health determinant deciles along a continuous morphological gradient from sparsely built rural settings to dense urban settings, demonstrating that the urban form encodes latent signals that capture socio-economic indicators and health determinants. Moreover, the study reveals a strong relationship between socio-economic indicators and the public health index, with education, cooking materials, and floor materials exhibiting a correlation above 0.96. This work demonstrates the utility of an integrated framework for socio-economic indicator profiling and public health planning in data-scarce urban contexts, offering a scalable approach for monitoring the indicators of Sustainable Development Goals in rapidly changing urban environments. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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29 pages, 11795 KB

Open AccessArticle

Empirical Evaluation of a CNN-ResNet-RF Hybrid Model for Occupancy Rate Prediction in Passive Ultra-Low-Energy Buildings

by Yiwen Liu, Yibing Xue, Chunlu Liu and Runyu Wang

Urban Sci. 2026, 10(3), 150; https://doi.org/10.3390/urbansci10030150 - 11 Mar 2026

Viewed by 341

Abstract

Accurate occupancy information is critical for optimizing energy efficiency in buildings. Hybrid machine learning models have demonstrated great potential in previous studies; however, their application in passive ultra-low-energy buildings remains underexplored. This study conducts an empirical evaluation of real-time occupancy rate prediction using [...] Read more.

Accurate occupancy information is critical for optimizing energy efficiency in buildings. Hybrid machine learning models have demonstrated great potential in previous studies; however, their application in passive ultra-low-energy buildings remains underexplored. This study conducts an empirical evaluation of real-time occupancy rate prediction using a CNN-ResNet-RF hybrid model based on multi-source environmental and behavioral data from a passive ultra-low-energy educational building. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Residual Networks (ResNet) to enhance deep feature representation, and Random Forests (RF) for ensemble-based generalization. Indoor CO₂ concentration exhibits the strongest linear correlation with occupancy rate (r = 0.54), indicating a meaningful association with occupancy dynamics. The model demonstrates strong predictive performance on the test set, with a coefficient of determination (R²) of 0.964, a root mean square error (RMSE) of 0.054, and a residual prediction deviation (RPD) exceeding 5. Compared with baseline models such as CNN, RF, and CNN-RF, the proposed framework exhibits generally lower prediction errors and improved stability. Further lightweight compression experiments reveal that the structured compact CNN-ResNet-RF-25 variant achieves even better accuracy (R² = 0.9748, RMSE = 0.0449, RPD = 6.327) while substantially reducing model complexity, demonstrating strong deployment potential in resource-constrained environments. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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29 pages, 14318 KB

Open AccessArticle

A High-Resolution Remote Sensing Building Extraction Network Integrating Multi-Scale Sequence Modeling and Spatial Adaptive Enhancement

by Chang Zuo and Xiaoji Lan

ISPRS Int. J. Geo-Inf. 2026, 15(3), 96; https://doi.org/10.3390/ijgi15030096 - 26 Feb 2026

Viewed by 706

Abstract

Building extraction from high-resolution remote sensing imagery holds significant value for urban planning, disaster assessment, and geospatial analysis. However, current semantic segmentation models still face limitations when handling complex scenarios characterized by diverse building morphologies, significant scale variations, and blurred boundaries. To address [...] Read more.

Building extraction from high-resolution remote sensing imagery holds significant value for urban planning, disaster assessment, and geospatial analysis. However, current semantic segmentation models still face limitations when handling complex scenarios characterized by diverse building morphologies, significant scale variations, and blurred boundaries. To address the challenges of insufficient long-range dependency modeling, suboptimal multi-scale feature representation, and weak spatial adaptability, this paper proposes a building extraction network that integrates multi-scale sequence modeling with spatial adaptive enhancement. Adopting UPerNet (equipped with ConvNeXt-Tiny) as the baseline framework, the proposed method introduces a dedicated PyramidSSM-based neck (PyramidSSMNeck) as the primary design for multi-scale feature alignment and fusion, and further integrates three enhancement components (S6 (SSM-based), LSKNet, and SAFM) that provide additional improvements mainly reflected in boundary delineation. Specifically, PyramidSSMNeck performs structured cross-scale feature projection, alignment, and aggregation to strengthen multi-scale representation; S6 enhances long-range contextual modeling, LSKNet adaptively adjusts spatial receptive fields to accommodate scale variations, and SAFM modulates feature responses with spatial cues to refine boundaries and fine details—forming a unified framework in which PyramidSSMNeck primarily drives multi-scale alignment and fusion, while S6, LSKNet, and SAFM further enhance long-range context modeling and spatial adaptivity, mainly benefiting boundary preservation and fine-detail integrity. Experiments were conducted on the WHU Building, INRIA, and a self-constructed Ganzhou urban dataset, and the results indicate that the proposed method achieved IoU scores of 91.29%, 81.96%, and 88.18% across the three datasets, outperforming the baseline UPerNet (ConvNeXt-Tiny) by 2.37%, 0.88%, and 3.68%, respectively, with F1-scores consistently exceeding 90%. Importantly, ablation results indicate that the majority of region-level gains (IoU/F1) come from PyramidSSMNeck, whereas the additional modules contribute more prominently to boundary quality, yielding a Boundary IoU increase from 63.29% to 65.63% (+2.34) from the neck-only setting to the full model. Visualization results further support the method’s advantages in boundary preservation and detail integrity, and additional cross-domain transfer experiments (zero-shot and few-shot from WHU to Ganzhou) suggest improved robustness under domain shift. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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37 pages, 8011 KB

Open AccessArticle

TopoFarm: A Topology-Annotated Panoptic Dataset for Unauthorized Farmland Excavation Scene Representation

by Shunxi Yin, Wanzeng Liu, Jun Chen, Jiaxin Ren and Jiadong Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(3), 93; https://doi.org/10.3390/ijgi15030093 - 25 Feb 2026

Cited by 1 | Viewed by 646

Abstract

Unauthorized farmland excavation is a prominent manifestation of farmland non-agriculturalization, and its effective monitoring depends on structured representations of objects and their spatial interactions in complex scenes. However, the existing computer vision research mainly focuses on object-level recognition or scene-level classification, while lacking [...] Read more.

Unauthorized farmland excavation is a prominent manifestation of farmland non-agriculturalization, and its effective monitoring depends on structured representations of objects and their spatial interactions in complex scenes. However, the existing computer vision research mainly focuses on object-level recognition or scene-level classification, while lacking datasets that explicitly model topological relationships in farmland excavation scenarios. To address this limitation, this paper presents TopoFarm, a topology-annotated panoptic dataset for unauthorized farmland excavation scenes. TopoFarm provides fine-grained panoptic segmentation annotations together with pairwise object contact relationship labels, enabling joint object–relation modeling and topology-aware scene representation. To improve annotation reliability under complex conditions, a human-in-the-loop hybrid intelligence framework, termed HITPA, is introduced to integrate automatic panoptic segmentation, depth-aware topological reasoning, and expert-guided refinement, achieving high annotation quality with controlled manual effort. Based on TopoFarm, systematic benchmark experiments are conducted for panoptic segmentation and topological relationship reasoning, along with a hierarchical evaluation protocol to analyze the impact of object-level representation quality on relational inference. The results demonstrate that TopoFarm poses substantial challenges for both tasks and highlight the strong dependence of topological reasoning on object accuracy and global scene context. Overall, TopoFarm provides a new data foundation and evaluation benchmark for topology-aware perception in farmland monitoring applications. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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20 pages, 3481 KB

Open AccessArticle

Construction of a Driving Route Inference Model Integrating Road Network Topology and Traffic Dynamics

by Yuxia Bian, Jinbao Liu, Xiaolong Su and Yuanjie Tang

ISPRS Int. J. Geo-Inf. 2026, 15(2), 84; https://doi.org/10.3390/ijgi15020084 - 16 Feb 2026

Viewed by 443

Abstract

The deployment volume of urban surveillance cameras has reached hundreds of thousands or even millions with the advancement of intelligent transportation systems (ITSs), indicating an enormous scale. However, the number of small-field-of-view surveillance cameras in large-scale traffic areas is insufficient to achieve full [...] Read more.

The deployment volume of urban surveillance cameras has reached hundreds of thousands or even millions with the advancement of intelligent transportation systems (ITSs), indicating an enormous scale. However, the number of small-field-of-view surveillance cameras in large-scale traffic areas is insufficient to achieve full coverage of urban traffic zones. In the fields of ITSs, this study proposes a traffic information-based driving route inference method to clarify target vehicles’ paths in zones with monitoring blind spots and enhance the collaborative capability between surveillance cameras and traffic networks. First, this study maps traffic roads containing monitoring blind spots and their topologies into Bayesian network (BN) structures. The influencing factors of the target vehicle path can be analyzed, extracted, and quantified by the known data in a traffic network. A weight analysis method is utilized to estimate the weight coefficients of the influencing factors on the basis of the traditional BN model, thereby realizing the driving routes based on traffic networks. This study conducted experiments in Xinbei District, Changzhou City, and Jiangsu Province, China. Experimental results verify that the proposed method can accurately infer and reconstruct driving routes with monitoring blind zones. This method can provide theoretical support for analyzing driving directions at complex traffic intersections and enabling driving route inference in traffic network areas with monitoring blind spots. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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20 pages, 3415 KB

Open AccessArticle

Enhancing Observation Point Analysis for Atmospheric State Estimation Using Self-Supervised Graph Neural Networks

by Guangyu Xu, Feng Bao, Siyu Lu, Chunlai Wu, Yuxin Liu and Wenfeng Zheng

Atmosphere 2026, 17(2), 163; https://doi.org/10.3390/atmos17020163 - 1 Feb 2026

Cited by 3 | Viewed by 505

Abstract

Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, [...] Read more.

Atmospheric state estimation is an important part of weather forecasting, and its accuracy determines the accuracy of the forecasting results. Traditional methods for atmospheric state estimation mainly rely on assimilation systems, using physical models and dynamic equations to predict the atmospheric state. However, these methods have certain limitations when dealing with large-scale meteorological data and complex meteorological phenomena. In order to solve the above problems, this study first integrates and processes data from multiple datasets including ground, upper-air, satellite, and atmospheric state, representing these data as graph structures. Secondly, a graph neural network-based network model is constructed, which is pre-trained using self-supervised methods and fine-tuned for specific tasks. Finally, gradient-based interpretability analysis is used to evaluate the importance of observed nodes. The experimental results show that both the atmospheric state estimation model and the interpretable analysis method proposed in this paper are superior to some existing representative models and methods. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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27 pages, 2982 KB

Open AccessArticle

Combining Machine Learning and MCR Model to Construct Urban Ventilation Corridors

by Zhiyuan Chen, Rongxiang Chen, Zixi Chen, Zekun Lu, Wenjuan Wu and Shunhe Chen

Appl. Sci. 2026, 16(3), 1428; https://doi.org/10.3390/app16031428 - 30 Jan 2026

Viewed by 623

Abstract

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing [...] Read more.

The heat island effect and air stagnation issues caused by high-density built-up areas are becoming increasingly severe. Optimising urban ventilation structures and establishing ventilation corridors have become key approaches to improving the urban thermal environment and enhancing liveability. However, traditional methods for constructing ventilation corridors often rely on empirical weighting or linear models, which struggle to accurately reveal the resistance coefficients of resistance indicators and fail to reflect the threshold at which indicators transition between positive and negative impacts. Consequently, this study employs Shanghai, China, as a case study, integrating machine learning models with the minimum cost path (MCR) model. Key variables were screened through multiple linear regression and variance inflation factor (VIF) analysis. Subsequently, machine learning models were compared to select the optimal model, with parameter optimisation conducted using Optuna, followed by computational implementation. The results indicate that built environment factors (such as building height, shape complexity, and road density) exert a significantly greater influence on ventilation potential than natural green space factors. By introducing the SHAP method, the positive and negative effects of each indicator on the ventilation environment and their threshold relationships were revealed. Negative indicators were converted into ventilation resistance factors to construct a resistance surface. Building upon this, cold and heat sources were identified using LST, NPP, and population density data. The MCR model was then employed to calculate the minimum resistance paths from cold to heat sources, forming an urban ventilation corridor network. The results indicate that primary corridors align with prevailing wind directions, following urban rivers and low-density green spaces. This study reveals the nonlinear effects of building and green space elements on ventilation systems, proposing machine learning-based optimisation strategies for ventilation corridors. It provides quantitative decision support for mitigating the urban heat island effect and enhancing city livability. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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17 pages, 7868 KB

Open AccessArticle

An Improved Geospatial Object Detection Framework for Complex Urban and Environmental Remote Sensing Scenes

by Yueying Zhu, Aidong Chen, Xiang Li, Yu Pan, Yanwei Yuan, Ning Yang, Wenwen Chen, Jiawang Huang, Jun Cai and Hui Fu

Appl. Sci. 2026, 16(3), 1288; https://doi.org/10.3390/app16031288 - 27 Jan 2026

Viewed by 449

Abstract

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. [...] Read more.

The development of Geospatial Artificial Intelligence (GeoAI), combining deep learning and remote sensing imagery, is of great interest for automated spatial inference and decision-making support. In this paper, a GeoAI-based efficient object detection framework named RS-YOLO is introduced by adopting the YOLOv11 architecture. The model integrates Dynamic Convolution for adaptive receptive field adjustment, Selective Kernel Attention for multi-path feature aggregation, and the MPDIoU loss function for geometry-aware localization. The proposed approach outperforms in experimental results on the TGRS-HRRSD dataset of 13 scenes from different geospatial scenarios, giving an 89.0% mAP and an 87 F1-score. Beyond algorithmic advancement, RS-YOLO provides a GeoAI-based analytical tool for applications such as urban infrastructure monitoring, land use management, and transportation facility recognition, enabling spatially informed and sustainable decision-making in complex remote sensing environments. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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30 pages, 16514 KB

Open AccessArticle

BD-GNN: Integrating Spatial and Administrative Boundaries in Property Valuation Using Graph Neural Networks

by Jetana Somkamnueng and Kitsana Waiyamai

ISPRS Int. J. Geo-Inf. 2026, 15(2), 52; https://doi.org/10.3390/ijgi15020052 - 23 Jan 2026

Viewed by 842

Abstract

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware [...] Read more.

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware Dual-Path Graph Neural Network (BD-GNN), a heterophily-oriented GNN specifically designed for continuous regression tasks. The model uses a dual and adaptive message passing design, separating inter- and intra-boundary pathways and combining them through a learnable gating parameter

α

. This allows it to capture boundary effects while preserving spatial continuity. Experiments conducted on three structurally contrasting housing datasets, namely Bangkok, King County (USA), and Singapore, demonstrate consistent performance improvements over strong baselines. The proposed BD-GNN reduces MAPE by 7.9%, 4.4%, and 4.5% and increases

R^{2}

by 3.2%, 0.7%, and 5.0% for the respective datasets. Beyond predictive performance,

α

provides a clear picture of how spatial and administrative factors interact across urban scales. GNN Explainer provides local interpretability by showing which neighbors and features shape each prediction. BD-GNN bridges predictive accuracy and structural insight, offering a practical, interpretable framework for applications such as property valuation, taxation, mortgage risk assessment, and urban planning. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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19 pages, 2460 KB

Open AccessArticle

GeoAI in Temperature Correction for Rice Heat Stress Monitoring with Geostationary Meteorological Satellites

by Han Luo, Binyang Yang, Lei He, Yuxia Li, Dan Tang and Huanping Wu

ISPRS Int. J. Geo-Inf. 2026, 15(1), 31; https://doi.org/10.3390/ijgi15010031 - 8 Jan 2026

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Abstract

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms [...] Read more.

To address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LST_max) and its diurnal range (LST_{max_min}), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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22 pages, 5307 KB

Open AccessArticle

Proposed Application of a Tree-Based Model for a Priority Scenario Restoration Plan for a Water Distribution Network

by Samantha Louise N. Jarder and Lessandro Estelito O. Garciano

Water 2026, 18(1), 131; https://doi.org/10.3390/w18010131 - 5 Jan 2026

Viewed by 826

Abstract

Hazard impacts are increasing in complexity as the world population grows. No universal strategies are available to minimize or eliminate the impacts of all scenarios. In this paper, a priority scenario-based strategy methodology is proposed using a Decision Tree (DT) machine learning tool. [...] Read more.

Hazard impacts are increasing in complexity as the world population grows. No universal strategies are available to minimize or eliminate the impacts of all scenarios. In this paper, a priority scenario-based strategy methodology is proposed using a Decision Tree (DT) machine learning tool. This approach identifies the parameters and combinations that contribute to high impact and loss from a hazard event conditioned on a priority scenario. The method is applied to a local water distribution network under seismic hazards. The priority scenarios in this study are vulnerability (VPS), damage (DPS), and cost (CPS). Each priority scenario identifies different affected areas. Some areas were repeatedly affected in different priority scenarios, showing an overlap of effects and making them a high crucial priority. Based on the analysis, a priority-based map was generated, highlighting areas that should be given priority for restoration or protection. The DTs were compared with other ML tools and Tree-based models to ascertain the best tool that determines the affected parameters. Competition tests compared the results from the ML tools and showed acceptable predictions; however, the DT was demonstrated to be the most ideal tool for this proposed method, showing an r² of 0.6745, 0.9259, and 0.7343 for VPS, DPS, and CPS, respectively. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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27 pages, 14285 KB

Open AccessEditor’s ChoiceArticle

Modeling and Explaining Perceived Fear of Crime from Street View Imagery Using a GeoAI Framework

by Somang Kim, Jaeyeon Choi and Youngok Kang

ISPRS Int. J. Geo-Inf. 2026, 15(1), 18; https://doi.org/10.3390/ijgi15010018 - 31 Dec 2025

Viewed by 1254

Abstract

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, [...] Read more.

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

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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24 pages, 4415 KB

Open AccessArticle

Revealing the Impact of the Built Environment on the Temporal Heterogeneity of Urban Vitality Using Ensemble Machine Learning

by Xuyang Chen, Junyan Yang, Jingjing Mai, Ao Cui and Xinyue Gu

Land 2025, 14(11), 2182; https://doi.org/10.3390/land14112182 - 3 Nov 2025

Viewed by 893

Abstract

The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an [...] Read more.

The multidimensional urban built environment (BE) in high-density cities has been shown to be closely related to the urban vitality (UV) of residents’ travelling. However, existing research lacks consideration of the differences in this relationship over a week, so this paper proposes an ensemble machine learning approach that simultaneously considers different time periods of the week. This study reveals the impacts of four dimensions of BE variables on UV at different time periods at the scale of the community life circle. The four well-performing base models are integrated to reveal the mechanism of differential effects of BE variables on UV under different time periods in the old city of Nanjing through Shapley addition explanation. The findings reveal that (1) the seven most important built environment variables existed in different time periods of the week: floor area ratio, service POI density, remote sensing ecological index, POI mixability, average building height, fractional vegetation cover, and maximum building area; (2) The nonlinear and threshold effects of the built environment factors differed across time periods of the week; (3) There is a dominant interaction between built environment variables at different time periods of the week. This study can provide guidance for the refined management of complex urban systems. Full article

(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)

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