Explainable AI (XAI)

Explainable AI encompasses methods and techniques that make the outputs of artificial intelligence systems understandable to humans. As AI models grow more complex, particularly deep neural networks, their decision-making processes become increasingly opaque, earning them the label of "black boxes." XAI addresses this by providing explanations of model predictions, revealing which features or input regions were most influential, and enabling users to understand, trust, and appropriately rely on AI outputs. Transparency is especially critical when AI informs consequential decisions in areas like urban planningUrban PlanningUrban Planning is the systematic process of designing and managing the development of cities and communities. It inte..., environmental policy, and disaster response. Explainability Methods for Geospatial AI ModelsSeveral XAI techniques are commonly applied to geospatial models. Saliency maps and Grad-CAM highlight which regions of a satellite image most influenced the model's prediction, showing whether a land cover classifier focused on relevant spectral features or spurious artifacts. SHAP (SHapley Additive exPlanations) values quantify each feature's contribution to individual predictions, revealing which spatial variables drive site selectionSite SelectionSite selection is the analytical process of evaluating and choosing optimal physical locations for new stores, facili... scores or property valuations. LIME (Local Interpretable Model-agnostic Explanations) approximates model behavior locally with interpretable models. Attention weight visualization in TransformerTransformerThe Transformer is an attention-based neural network architecture that processes entire sequences in parallel, enabli...-based models shows which parts of the input the model attended to for each prediction. Feature importance from tree-based models ranks spatial, spectral, and demographic features by their predictive contribution. Importance for Geospatial Decision-MakingExplainability is not merely a technical consideration but a governance requirement for geospatial AI applications that affect communities and environments. Urban planners need to understand why an AI recommends specific zoningZoningZoning is a land use planning tool that divides geographic areas into zones with specific permitted uses, building st... changes. Environmental regulators must verify that deforestation detection is based on valid spectral evidence rather than model artifacts. Site selection stakeholders need transparent reasoning behind location recommendations. Explainable AI builds the trust necessary for operational deployment of geospatial models and helps identify potential biases in training data or model behavior.