Geospatial Machine Learning

Geospatial machine learning (GeoML) applies machine learning techniques to analyze geospatial data, aiming to improve the accuracy, efficiency, and scalability of spatial analysis. Current research focuses on enhancing model robustness in challenging environments (e.g., space-based applications), developing explainable AI methods for improved model transparency and reliability, and addressing the scarcity of labeled data through automated labeling and efficient sampling strategies. GeoML's impact spans diverse fields, from improving remote sensing image analysis for applications like land cover classification and object detection to informing public health initiatives by identifying spatial correlations between environmental factors and disease prevalence.