Geospatial Data

Geospatial data analysis focuses on extracting insights from location-based information, aiming to improve understanding and prediction across diverse fields. Current research emphasizes developing advanced machine learning models, including graph neural networks, quantile neural networks, and large language models, to handle the complexities of various geospatial data types (points, lines, polygons, rasters) and address challenges like uncertainty quantification and bias mitigation. These advancements are crucial for enhancing applications in areas such as disaster management, urban planning, environmental monitoring, and precision agriculture, improving the accuracy and efficiency of spatial analysis and prediction.