I'm Isaac and I work on GeoAI — computer vision and foundation models for earth observation — from research through production. Currently Director of AI/ML Research at Taylor Geospatial, where I'm building the team and the models behind our earth observation work. Previously I built and shipped the RasterFlow platform at Wherobots for global-scale geospatial inference, and served as PI on the IARPA SMART program at BlackSky.
I maintain open-source projects — TorchGeo (4k+ stars), SMP (11k+ stars), and FTW — and publish country-scale and global-scale prediction maps as open data. I write a weekly blog with Caleb Robinson at geospatialml.com covering ML experiments for geospatial applications. Ph.D. in Electrical Engineering from UTSA.
News
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Projects
Selected Publications
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Talks & Podcasts
Jul 2026
EO-AI Symposium, TUM
TorchGeo-Bench: An Evaluation Harness for Geospatial AI and Foundation Models
Invited talk introducing TorchGeo-Bench, our new evaluation harness for geospatial AI and foundation models, at the EO-AI Symposium 2026 in Munich (organized by Prof. Xiaoxiang Zhu's lab at TUM and the University of Queensland). Slides linked below.
Jun 2026
Satellite-Image-Deep-Learning Podcast
Mapping The World at Taylor Geospatial
Joined Robin Cole's podcast with Jennifer Marcus to discuss Fields of The World, openly licensed field boundary data, and geospatial foundation model benchmarking at Taylor Geospatial.
Watch recordingMay 2026
Great Data Products Podcast
Fields of the World: Mapping Every Field on Earth
Joined Jed Sundwall's Great Data Products podcast with Jennifer Marcus to discuss the global release of Fields of The World, the fiboa schema, and what it takes to sustain open geospatial data products.
Watch recordingApr 2026
UT Austin
Cloud Native GeoAI
Guest lecture at UT Austin on Cloud Native GeoAI, with slides and recording linked below.
Watch recordingApr 2026
Clark University
Geospatial AI and Deep Learning with PyTorch
Guest lecture for Lyndon Estes' class at Clark University, with slides linked below.
Oct 2025
Spatial Stack Podcast
Beyond the Hype: Embeddings, Foundation Models, and the Future of Earth Observation
Joined Matt Forrest's Spatial Stack podcast with Chris Ren to discuss the current state of Geospatial Foundation Models and Embeddings.
Aug 2025
Satellite-Image-Deep-Learning Podcast
Chained Models for High-Res Aerial Solar Fault Detection
Joined Robin Cole's Satellite-Image-Deep-Learning podcast to discuss our CVPR PBVS paper: Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale.
Education
University of Texas at San Antonio
Ph.D. in Electrical Engineering
Advisor: Paul Rad
University of Texas at San Antonio
M.S. in Electrical Engineering
Advisor: Yufei Huang
Texas A&M University - Kingsville
B.S. in Electrical Engineering, Minor in Mathematics
Experience
Director of AI/ML Research — Taylor Geospatial
Building the AI/ML research team and shipping geospatial foundation models and earth observation pipelines from prototype through production.
Senior Machine Learning Engineer — Wherobots
Built and scaled geospatial vision models powering the Wherobots spatial analytics platform. Shipped country-scale field boundary predictions and open-sourced prediction maps for 5 countries.
Senior Machine Learning Scientist — Zeitview (formerly DroneBase)
Researched and deployed computer vision, vision-language, and 3D reconstruction models at scale for renewable energy inspection — solar farms, wind turbines, rooftops, and telecom infrastructure.
Ph.D. Research Intern — Microsoft Research
Advisor: Simone Fobi Nsutezo & Anthony Ortiz
Researched multimodal pretraining methods for large-scale geospatial vision-language datasets.
Senior Machine Learning Engineer — BlackSky
Served as PI on the IARPA SMART program. Built and deployed models powering the Spectra AI platform's satellite image analytics.
Senior Data Scientist — HouseCanary
Built computer vision models extracting features from property images for HouseCanary's automated valuation model and property recommender.
Senior Data Scientist — Booz Allen Hamilton
Prototyped deep learning methods for detecting image steganography and adversarially generated domains.
Research Engineer — Southwest Research Institute (SwRI)
Advisor: Kenneth Holladay
Shipped software updates for the A-10 Warthog and researched ML methods for engine stall detection and MIL-STD-1553 bus exploitation.
Research Intern — Oak Ridge National Laboratory (ORNL)
Advisor: Paul Ewing
Collected and annotated a seismic dataset of human and vehicle activity and trained ML models to detect it.