6 citations · 15 across the 24 of their papers we have counts for
15 papers · 1 filter
Anaximander: Interactively Running Geospatial Deep Learning Models on Any Compute Backend
Satej S. Soman, Akram Zaytar, Girmaw A. Tadesse +5
Applying deep learning models to satellite imagery from within geographic information systems (GIS) remains high-friction for remote sensing practitioners. Models arrive in incompa…
WATCH: Wide-Area Archaeological Site Tracking for Change Detection
Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali +7
Monitoring archaeological sites at scale is vital for protecting cultural heritage, yet pinpointing when disturbances occur remains difficult because visual cues are subtle and gro…
GeoAI Agency Primitives
Akram Zaytar, Rohan Sawahn, Caleb Robinson +5
We present ongoing research on agency primitives for GeoAI assistants -- core capabilities that connect Foundation models to the artifact-centric, human-in-the-loop workflows where…
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…
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…
BON: An extended public domain dataset for human activity recognition
Girmaw Abebe Tadesse, Oliver Bent, Komminist Weldemariam +3
Body-worn first-person vision (FPV) camera enables to extract a rich source of information on the environment from the subject's viewpoint. However, the research progress in wearab…
Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning
Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas +2
Recent advances in deep learning have led to breakthroughs in the development of automated skin disease classification. As we observe an increasing interest in these models in the…
Pattern Detection in the Activation Space for Identifying Synthesized Content
Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse +3
Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize…