Geospatial insights: Explainable AI (XAI) in remote sensing and GIS
The growing adoption of explainable AI (XAI) in remote sensing and geographic information systems (GIS), often referred to as GeoXAI, is transforming how users interact with AI-driven geospatial models. GeoXAI enables better understanding, trust, and validation of outcomes from complex “black-box” models, including deep learning in remote sensing. By enhancing transparency and interpretability, it provides clear insights into how predictions—such as classification and detection from Earth observation data—are generated. Widely used XAI techniques in geospatial platforms such as SHAP, LIME, heatmaps, visualizations, and surrogate models provide insights into the spatial and temporal factors influencing model outputs, helping improve accountability, fairness, bias detection, and risk assessment. As demand for transparent GeoAI solutions grows, stakeholders increasingly seek location-specific explanations that support reliable decision-making and build confidence in AI-generated geographic insights before operational deployment.
