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20232026
most citedFields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary Segmentation

2 citations · 7 across the 24 of their papers we have counts for

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Showing 2024 · cs.CVShow all

7 papers · 2 filters

cs.CV2024

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…

cs.CV2024★ 2 cited

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…

cs.CV2024

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…

cs.CV2024★ 2 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…