GeoXAI: Explainable AI for Geospatial Data

• GeoXAI is a field that fuses explainable AI methods with spatial analytics, enabling transparent interpretation of models applied to data like satellite imagery and coordinate-based features.
• It integrates model-agnostic techniques like SHAP and LIME with specialized spatial methods such as Grad-CAM to address local and global spatial heterogeneity.
• GeoXAI enhances decision-making in hazard prediction, urban planning, and environmental monitoring by providing quantifiable insights into nonlinearity and spatial interactions.

Geospatial Explainable AI (GeoXAI) refers to the development and deployment of explainability techniques for artificial intelligence models operating on geospatial data—data with explicit spatial or spatiotemporal structure, such as imagery, tabular features with coordinates, trajectories, and physically distributed fields. GeoXAI enables interpretation of machine learning systems that analyze complex geographic phenomena by providing transparent, trustworthy, and scientifically meaningful explanations of model behavior at local and global spatial scales. This domain integrates general XAI methods (e.g., SHAP, LIME, saliency maps) with specialized methodologies for spatial heterogeneity, spatial interaction, and geospatial context, resulting in actionable insights for environmental modeling, mobility analysis, hazard prediction, urban analytics, and remote sensing.

1. Conceptual Foundations and Taxonomy

GeoXAI is defined as the intersection of XAI and GeoAI, targeting the interpretability of predictive models—ranging from deep neural networks to tree ensembles—applied to spatially encoded data (Jalali et al., 2023). Its objectives are threefold: (i) enhancing transparency for scientific and operational applications, (ii) providing domain-relevant insight into spatial processes and relationships, and (iii) enabling actionability for decision-makers.

Taxonomically, GeoXAI methods can be classified by both their model-specificity and their explanation scope:

• Model-specific vs. Model-agnostic: Techniques such as Grad-CAM and Layer-wise Relevance Propagation (LRP) are tailored for CNNs, while SHAP and LIME are universal across black-box models (Taskin et al., 2023).
• Local vs. Global Explanations: Saliency maps and local Shapley values offer fine-grained pixel- or instance-level attribution; spatial aggregation or smoothing yields global insights over regions.
• Data Type: Imagery (satellite, hyperspectral), tabular geospatial features, spatiotemporal trajectories, and point-clouds require distinct treatment to preserve spatial context.
• Key Challenges: geo-semantic reference models, handling geographic scale, topology, spatial autocorrelation, scale-adaptive explanations, and spatial privacy (Jalali et al., 2023).

2. Algorithmic Methodologies and Mathematical Formalisms

GeoXAI leverages a repertoire of XAI tools, frequently adapted for spatial context:

• Saliency and Attribution Methods: Saliency maps based on gradient backpropagation, occlusion, or perturbation reveal sensitive regions in image or spatial features (Hsu et al., 2023, Mamalakis et al., 2022, Taskin et al., 2023). For CNNs, attribution formulas include Integrated Gradients, Input×Gradient, SmoothGrad, and LRP rules, each with limitations regarding gradient shattering, sign-sensitivity, and input-shift invariance (Mamalakis et al., 2022).
• SHAP and GeoShapley Frameworks: Shapley value-based decomposition provides local and interaction-based attributions for tabular and structured data. GeoShapley extends the classical definition:

$$\Phi_j = \sum_{S \subseteq \mathcal P\setminus\{j\}} \frac{|S|!\,(p-|S|-1)!}{p!}[f(S\cup\{j\})-f(S)]$$

and incorporates intrinsic location effects $\Phi_{\rm GEO}$, as well as spatially conditioned interactions $\Phi_{\rm GEO, j}$, capturing both nonlinear and spatially heterogeneous effects (Lu et al., 17 Dec 2025, Li, 1 May 2025).

• Counterfactual Explanations: Minimal input modifications are computed to flip model decisions, with formulations designed for spatiotemporal constraints and trajectory feasibility (Jalali et al., 2023, Taskin et al., 2023).
• Local Spatial Weighting and Ensemble Models: GeoXAI frameworks are augmented by geographically weighted kernels (binary, Gaussian, adaptive bandwidth selection), allowing local fitting and explanation at each location, as in XGeoML (Liu, 5 Mar 2024).

3. Capturing Spatial Heterogeneity and Nonlinearity

The core strength of GeoXAI lies in its ability to parse nonlinear relationships and spatially varying effects:

• Spatial Heterogeneity: By treating spatial coordinates (latitude/longitude or grid indices) as explicit features or as "players" in Shapley games, attribution maps can reveal intrinsic spatial baselines as well as modulated interactions—e.g., crash risk in Miami vs. rural Florida (Lu et al., 17 Dec 2025).
• Nonlinearity: Tree-based methods and neural nets model arbitrary nonlinearities. GeoShapley and spatial local SHAP expose thresholds, inflections, and plateaus in the relationship between predictors (e.g., intersection density, education) and outcomes (Lu et al., 17 Dec 2025, Li, 1 May 2025).
• Partial Dependence and Interactions: GeoXAI employs partial dependence plots, interaction Shapley terms, and percentile-based binning to characterize complex effect structures (Liu, 5 Mar 2024).

4. Integrated GeoXAI Workflows and Empirical Examples

End-to-end GeoXAI pipelines are now established in multiple domains:

• Tabular Geospatial Analysis: In traffic crash density modeling, the pipeline spans data ingestion (crashes, roadway, census/ACS features), AutoML model selection, spatially decomposed GeoShapley explanation, and policy translation via quantitative thresholds (Lu et al., 17 Dec 2025).
• Remote Sensing and Environmental Monitoring: Attribution methods such as Grad-CAM, LRP, Deep SHAP, and surrogate models (LIME, distillation) are used to interpret land-cover mapping, change detection, and hazard prediction from satellite or climate data (Taskin et al., 2023, Vu et al., 17 Nov 2025).
• Deep Spatiotemporal Surrogates: Hydrologic connectivity is diagnosed via LSTM-based emulation and XAI attribution (Expected Gradients/SHAP), where local feature contributions are spatially aggregated into watershed indicators that predict threshold dynamics in runoff (Ye et al., 2 Sep 2025).
• Transformer and Graph-Based GeoAI: Recent models such as GeoAggregator employ local attention augmented with spatial biases, optimized neighbor search, ensembling, and GeoShapley for high-dimensional tabular data (Deng et al., 23 Jul 2025).

5. Benchmarking, Comparative Evaluation, and Practical Guidance

Empirical benchmarking is central to GeoXAI validation:

• Method Performance: SHAP, LIME, and GeoShapley are systematically compared to MGWR and GWR, with metrics on $R^2$, partial-dependence curve fidelity, spatial smoothness, and interpretability (Liu, 5 Mar 2024, Li, 1 May 2025).
• Algorithmic Trade-offs: Gradient-based methods in deep networks are subject to gradient shattering and baseline sensitivity, necessitating cautious multi-method interpretation (Mamalakis et al., 2022).
• Policy Implications: GeoXAI directly informs targeted interventions—traffic calming, adaptive speed enforcement, equity-driven resource allocation—by revealing spatially explicit risk components (Lu et al., 17 Dec 2025).

Table: GeoXAI Methods and Applications

Methodological Class	Core Technique	Example Application
Attribution (Image)	Grad-CAM, LRP, Integrated Grads	Land cover classification, wildfire forecasting
SHAP/GeoShapley	Tree/Kernel SHAP, spatial extension	Traffic crash density, voting behavior, tabular prediction
Local Weighted ML	XGeoML, local ensemble fitting	Synthetic grid regression, spatial heterogeneity
Counterfactual	Trajectory perturbation, spatial mapping	Vessel/vehicle mobility, what-if scenario analysis
Deep Surrogate	LSTM, Transformer, CNN + XAI	Hydrologic connectivity, environmental time series

6. Stability, Robustness, and Modularity in Explanations

Recent works advance explanation robustness and spatial coherence:

• SX-GeoTree: Enhances regression trees with joint optimization over MSE reduction, spatial autocorrelation (Moran’s I), and explanation modularity on consensus similarity graphs combining GWR and SHAP distances (Kang et al., 25 Nov 2025). This yields spatially smooth attributions and reduced residual clustering.
• Modularity Maximization: Recodes Lipschitz continuity of local explanations as a community preservation problem in the network of domain and explanation similarities, enabling scalable enforcement without per-sample search (Kang et al., 25 Nov 2025).

7. Open Challenges and Future Directions

GeoXAI faces unresolved issues:

• Scaling and Computational Constraints: Efficient Shapley computation for large, high-dimensional spatial data requires ongoing methodological innovation, including model-specific algorithms (TreeSHAP), approximations (Monte Carlo/Kernel SHAP), and spatial sampling (Li, 1 May 2025, Liu, 5 Mar 2024).
• Multi-scale and Spatio-Temporal XAI: Current methods rarely address temporal dynamics or hierarchical spatial scales, limiting utility in EO, mobility, and process forecasting (Taskin et al., 2023, Jalali et al., 2023).
• Physics-ML Integration and Benchmarking: There is significant need for physically-constrained explanations, as well as standardized GeoXAI datasets and evaluation protocols for faithfulness, stability, and geospatial relevance (Taskin et al., 2023).
• Causal and Interventional GeoXAI: Next-generation frameworks aim to dissociate direct and mediated spatial effects, incorporate causal inference, and clarify intervention pathways for policy (Li, 1 May 2025).

GeoXAI thus represents an evolving synthesis of XAI and spatial analytical science, providing foundational algorithms, interpretability protocols, and empirical evidence for robust, domain-aware machine learning in geographic contexts. Its trajectory is characterized by rapid methodological development, impactful applications, and growing emphasis on explanation fidelity, scalability, and spatial reasoning.