2 citations · 7 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
Where are the Whales: A Human-in-the-loop Detection Method for Identifying Whales in High-resolution Satellite Imagery
Caleb Robinson, Kimberly T. Goetz, Christin B. Khan +4
Effective monitoring of whale populations is critical for conservation, but traditional survey methods are expensive and difficult to scale. While prior work has shown that whales…
Machine Learning for Sustainable Rice Production: Region-Scale Monitoring of Water-Saving Practices in Punjab, India
Ando Shah, Rajveer Singh, Akram Zaytar +6
Rice cultivation supplies half the world's population with staple food, while also being a major driver of freshwater depletion--consuming roughly a quarter of global freshwater--a…
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…
Core-Set Selection for Data-efficient Land Cover Segmentation
Keiller Nogueira, Akram Zaytar, Wanli Ma +9
The increasing accessibility of remotely sensed data and their potential to support large-scale decision-making have driven the development of deep learning models for many Earth O…