NSF I-GUIDE
The National Science Foundation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) aims to transform geospatial data-intensive sciences through integration of AI and cyberGIS, reproducible data-intensive analytics and modeling, FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and innovative education and workforce development programs. This transformation catalyzes new convergence science necessary to drive advances across many fields ranging from computer, data and information sciences to atmospheric sciences, ecology, economics, environmental science and engineering, human-environment and geographical sciences, hydrology and water sciences, industrial engineering, sociology, and statistics. Through synergistic advances of these fields I-GUIDE is empowering diverse communities to produce data-intensive solutions to society’s resilience and sustainability challenges.
Introduction to Mapping and Visualizing Social Data with R
GeoAI for Burn Area Analysis and Fire Risk Mapping in the Pantanal Wetlands
Harnessing OpenStreetMap Data for Roof Material and Shape Prediction
I-GUIDE Platform: Registering and Getting DOI for your Datasets
Geospatial Simulation of Fire Ecology with DeepEarth
Bridging High-Performance Computing and Geospatial Workflows: OGC Standards Implementation for ...
Information Session for the I-GUIDE Community Champions
TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
Building Spatial Workflows with Open Data in R
The Geography of Human Flourishing
Mapping Wildfire Risk to Transportation Infrastructure with Spatial AI
Intro to the I-GUIDE Platform
Team 5: Predicting and Mapping Floods through Geospatial Data Fusion and Machine Learning
Team 4: Neutralizing Onerous Heat Effects on Active Transportation (NO-HEAT)
Team 3: Geospatial Simulation of Fire Ecology with DeepEarth
Team 2: GeoAI for Burn Area Analysis and Fire Risk Mapping in the Pantanal Wetlands
Team 1: Bridging the Risk Perception Gaps around Hazards
Developing New Methods & Data for Understanding Large, Complex Wildland Fires & Their Impacts
GeoMapCLIP, a Fine-Tuned GeoCLIP to Geolocate Satellite Imagery
The Science of Urban Networks
A look inside OldInsuranceMaps.net, a crowdsourcing platform for georeferencing historical fire...
Introduction to the I-GUIDE Platform
Open-Source Spatiotemporal Geovisual Analytics with CyberGIS-Vis: A COVID-19 Case Study
Panel: AI for Sustainability through Geospatial Innovation and Partnership
Embracing Large Language Models for Geospatial Applications: A Retrieval and Structuring Approach
Assessing Dam Risk: Understanding Vulnerabilities of High-Risk Dams in Utah
Machine Learning Meets Satellite Data: Variance Analysis of Brightness Temperature
The Known Unknowns: Embracing Meta-Uncertainty in Human–Natural Systems Modeling