Publications

Mar, 2026

Conference Paper

OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery

While the pretraining of Foundation Models (FMs) for remote sensing (RS) imagery is on the rise, models remain restricted to a few hundred million parameters. Scaling models to billions of parameters has been shown to yield unprecedented benefits including emergent abilities, but requires data scaling and computing resources typically not available outside industry R&D lab…

Sep, 2025

ORNL Report

LandScan Mosaic Gaza Version 9

Jul, 2025

ORNL Report

Energy Infrastructure Identification and Damage Assessment Utilizing Imagery From Airborne Systems: Data Requirements

Oct, 2024

ORNL Report

Enhancing Utility Pole Detection Performance: An Analysis and Benchmarking of YOLOv5 with MMYOLO and MMDetection Frameworks

Sep, 2024

Journal

A Science Gateway for the Repeatable Analysis of Machine Learning Predicted Gravity Anomalies

Apr, 2024

ORNL Report

AI-Driven High-Resolution Gravitational Map: Year 3 Technical Report

Dec, 2023

Journal

UAS Edge Computing of Energy Infrastructure Damage Assessment

Energy infrastructure assessments are needed within 72 hours of natural disasters, and previous data collection methods have proven too slow. We demonstrate a scalable end-to-end solution using a prototype unmanned aerial system that performs on-the-edge detection, classification (i.e., damaged or undamaged), and geo-location of utility poles. The prototype is suitable for…

Nov, 2023

Conference Paper

Few-shot Learning for Post-disaster Structure Damage Assessment

Automating post-disaster damage assessment with remote sensing data is critical for faster surveys of structures impacted by natural disasters. One significant obstacle to training state-of-the-art deep neural networks to support this automation is that large quantities of labelled data are often required. However, obtaining those labels is particularly unrealistic to supp…

Nov, 2023

ORNL Report

Post-Invasion Damage Assessment: Ukraine’s Crop Storage Infrastructure

Nov, 2023

Conference Paper

Scaling Automatic Vector Data Alignment to Satellite Imagery

Given the tremendous volume of accessible Earth Observation (EO) data, there is a need to develop scalable Geospatial Artificial Intelligence (GeoAI) solutions for time-sensitive applications. Scalability in this context refers to rapidly processing large-scale EO data using high performance computing resources. Accurate mapping of the built environment from remote sensing…

Nov, 2023

Conference Paper

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple app…