2 citations · 2 across the 4 of their papers we have counts for
6 papers
TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
Tammy Glazer, Gilles Q. Hacheme, Akram Zaytar +9
We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building f…
Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse +5
Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot k…
GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models
Gilles Quentin Hacheme, Girmaw Abebe Tadesse, Caleb Robinson +3
Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce…
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…
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…