NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science
NORA introduces an autonomous research agent designed to support end-to-end spatial data science workflows.
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At the University of Tennessee, Knoxville, the GRIND Lab develops responsible geospatial AI to understand how people, places, and natural systems interact under environmental change and disaster risk. Our work connects GeoAI, remote sensing, spatial data science, resilience, equity, and health to build more trustworthy tools for science and society.
Fair, explainable, and trustworthy geospatial artificial intelligence.
Understanding hazards, adaptation, recovery, and nature-human dynamics.
Studying who is exposed, who is protected, and how impacts differ across communities.
We use geospatial data and AI to study people, places, and the environment.
Designing fair, explainable, and trustworthy geospatial AI models that account for spatial heterogeneity and social equity in predictions.
Leveraging multi-source satellite imagery (SAR, optical, LiDAR) and deep learning for land cover mapping, change detection, and disaster monitoring.
Developing predictive and post-event models for wildfire, flood, hurricane, and compound hazards, with a focus on community resilience and recovery.
Studying how human activities and natural processes interact across spatial and temporal scales to generate and amplify socio-environmental risks.
Advancing methods for spatial statistics, graph neural networks, and spatio-temporal modeling to extract knowledge from complex geospatial datasets.
Examining how environmental hazards and climate change disproportionately affect marginalized communities, and developing equitable GeoAI solutions.
Dr. Bing Zhou leads the GRIND Lab, developing responsible GeoAI to understand disaster risk, nature–human dynamics, and community resilience.
Recent publications and research updates from the GRIND Lab.
NORA introduces an autonomous research agent designed to support end-to-end spatial data science workflows.
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Dr. Zhou chaired a GeoAI and Deep Learning Symposium session focused on GeoAI for disaster resilience at the 2026 AAG Annual Meeting.
Read MoreSelected work in disaster intelligence, multimodal GeoAI, and human–environment research.
An AI-supported disaster intelligence platform integrating geospatial evidence, hazard information, and intelligent decision support for disaster monitoring and emergency response.
Explore HazardWeave
A geo-contextual multimodal framework that generates geographically coherent landscape imagery from environmental soundscapes using Diffusion Transformers.
View SounDiTWe are actively recruiting PhD students, postdocs, and undergraduate researchers with a passion for geospatial AI, remote sensing, and disaster risk science.