Welcome to UB Geospatial Artificial Intelligence (GeoAI) Lab! We are a research group located within the Department of Geography at the University at Buffalo (UB). We are also an affiliated lab with the UB AI and Data Science Institute. We conduct basic research that integrates big geospatial data, spatial analysis, and AI methods to understand natural and social phenomena in geographic space, such as those related to resilient and sustainable communities, public health and environment, and ecosystems and biodiversity. Such basic research also inspires us to conduct research that improves GeoAI methods and spatial data infrastructures (SDIs), which can further support scientific discoveries. We are using our expertise to address some of the most challenging problems facing our society and to advance knowledge.

Our research currently focuses on four interrelated areas:
GeoAI for Resilient and Sustainable Communities: Our research in this area uses AI methods to discover knowledge that can help communities become more resilient in the face of natural disasters and to become more sustainable. Some of our studies include understanding and extracting location descriptions during natural disasters, understanding the perceptions of people toward their living environment and quality of life, and identifying urban areas of interest frequently visited and used by people. Our work also involves protecting vulnerable population groups (e.g., minorities, elderly, women, and children) and reducing inequality in communities.
GeoAI for Public Health and Environment: The places where we live, work, and spend time can directly affect our health outcomes. Our research in this area aims to assess and understand environmental impacts on public health. AI methods integrated with various types of big geo data (e.g., point-of-interest data, GPS locations, environmental sensors, and remote sensing images) provide novel approaches for improving our understanding of the places that have positive impacts on health (e.g., parks) and those that may have negative impacts (e.g., liquor stores). Such an improved understanding will enable more comprehensive answers to health-related questions from a spatial perspective.
GeoAI for Ecosystems and Biodiversity: Ecosystems and biodiversity provide critical resources for our humans to survive and prosper, which include oxygen, food, clean air and water, pest control, and many others. Our research in this area integrates AI methods, big geo data (e.g., remote sensing images and field observations), and spatial models to discover knowledge that advances our understanding of spatial ecological processes (e.g., vegetation dynamics) and informs decisions related to environmental protection and resource management. Our work currently focuses on the Cape Floristic Region of South Africa, which is a global biodiversity hotspot and a UNESCO World Heritage site.

GeoAI Methods and Spatial Data Infrastructures: Basic research in the above three areas inspires us to develop and improve GeoAI methods and to enhance spatial data infrastructures (SDIs). Improved GeoAI methods can help us better analyze geospatial data, and enhanced SDIs can facilitate the reuse of existing datasets, tools, and services. Our research in this area has developed methods for prioritizing disaster mapping tasks, enriching the metadata of geospatial resources, and building knowledge graphs from historical maps. We are also one of the participants that developed the five-star guide for achieving replicability and reproducibility in GIScience research.