Use-inspired research on addressing disaster-related societal challenges: Our research in this area uses GeoAI and GIS methods, along with geospatial big data, to advance knowledge in human-environment interactions and to help communities become more resilient in the face of natural disasters. There is an increase in the frequency and intensity of natural disasters. Meanwhile, geospatial big data, such as remote sensing images, weather and climate data, anonymized mobile phone location data, and social media data, capture many aspects of the human-environment system under a disaster. Our research has leveraged GeoAI methods to study such human-environment interactions in disaster events, such as Hurricane Harvey, Texas Winter Storm, and California Wildfires.

Methodological research on GeoAI and GIS methods: Our research in this area aims to improve existing GeoAI and GIS methods or to develop new methods and tools when necessary. These methods and tools can increase our ability to analyze geospatial data and enable us to better address disaster-related societal challenges. Examples include geo-knowledge-guided large language models (LLMs) for disaster-related location description extraction, a Python based geographical random forest model (PyGRF), and a geospatial data annotation tool called GALLOC.