7 citations · 16 across the 24 of their papers we have counts for
9 papers · 1 filter
Sims: An Interactive Tool for Geospatial Matching and Clustering
Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson +6
Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid disc…
Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps
Girmaw Abebe Tadesse, Caleb Robinson, Charles Mwangi +7
In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insi…
PGRID: Power Grid Reconstruction in Informal Developments Using High-Resolution Aerial Imagery
Simone Fobi Nsutezo, Amrita Gupta, Duncan Kebut +5
As of 2023, a record 117 million people have been displaced worldwide, more than double the number from a decade ago [22]. Of these, 32 million are refugees under the UNHCR mandate…
Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation
Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson +4
Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data…
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation
Hannah Kerner, Snehal Chaudhari, Aninda Ghosh +9
Crop field boundaries are foundational datasets for agricultural monitoring and assessments but are expensive to collect manually. Machine learning (ML) methods for automatically e…
Analyzing Decades-Long Environmental Changes in Namibia Using Archival Aerial Photography and Deep Learning
Girmaw Abebe Tadesse, Caleb Robinson, Gilles Quentin Hacheme +5
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterh…
Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery
Akram Zaytar, Caleb Robinson, Gilles Q. Hacheme +6
Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery…
Weak Labeling for Cropland Mapping in Africa
Gilles Quentin Hacheme, Akram Zaytar, Girmaw Abebe Tadesse +4
Cropland mapping can play a vital role in addressing environmental, agricultural, and food security challenges. However, in the context of Africa, practical applications are often…
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…