8 citations · 38 across the 20 of their papers we have counts for
13 papers · 1 filter
HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment
Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo +8
When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after image…
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing
Isaac Corley, Simone Fobi Nsutezo, Anthony Ortiz +4
Remote sensing imagery is dense with objects and contextual visual information. There is a recent trend to combine paired satellite images and text captions for pretraining perform…
A Change Detection Reality Check
Isaac Corley, Caleb Robinson, Anthony Ortiz
In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the…
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
Caleb Robinson, Isaac Corley, Anthony Ortiz +3
Fully understanding a complex high-resolution satellite or aerial imagery scene often requires spatial reasoning over a broad relevant context. The human object recognition system…
Rapid building damage assessment workflow: An implementation for the 2023 Rolling Fork, Mississippi tornado event
Caleb Robinson, Simone Fobi Nsutezo, Anthony Ortiz +7
Rapid and accurate building damage assessments from high-resolution satellite imagery following a natural disaster is essential to inform and optimize first responder efforts. Howe…
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Isaac Corley, Caleb Robinson, Rahul Dodhia +2
Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets contain…
Mask Conditional Synthetic Satellite Imagery
Van Anh Le, Varshini Reddy, Zixi Chen +5
In this paper we propose a mask-conditional synthetic image generation model for creating synthetic satellite imagery datasets. Given a dataset of real high-resolution images and a…
Fast building segmentation from satellite imagery and few local labels
Caleb Robinson, Anthony Ortiz, Hogeun Park +5
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. H…
Mapping industrial poultry operations at scale with deep learning and aerial imagery
Caleb Robinson, Ben Chugg, Brandon Anderson +2
Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountabilit…
TorchGeo: Deep Learning With Geospatial Data
Adam J. Stewart, Caleb Robinson, Isaac A. Corley +3
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among…
Detecting Cattle and Elk in the Wild from Space
Caleb Robinson, Anthony Ortiz, Lacey Hughey +2
Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Pri…
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
Caleb Robinson, Anthony Ortiz, Juan M. Lavista Ferres +2
Longitudinal studies are vital to understanding dynamic changes of the planet, but labels (e.g., buildings, facilities, roads) are often available only for a single point in time.…
High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest
Nikolay Malkin, Caleb Robinson, Nebojsa Jojic
We present simple algorithms for land cover change detection in the 2021 IEEE GRSS Data Fusion Contest. The task of the contest is to create high-resolution (1m / pixel) land cover…