Multimodal Geospatial

Multimodal geospatial research focuses on developing intelligent systems that can understand and reason with diverse geospatial data types, such as images, text, and vector data, to solve complex problems. Current efforts concentrate on building foundation models, leveraging techniques like self-supervised learning and multimodal fusion, often adapting existing vision-language models to the geospatial domain or designing novel architectures for handling the unique challenges of geospatial data heterogeneity. This field is significant for advancing applications in areas like infrastructure digitalization, anomaly detection in geographic datasets, and improving the accuracy and efficiency of geospatial analysis across various domains.