55 citations · 196 across the 12 of their papers we have counts for
10 papers · 1 filter
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision
Sherrie Wang, Francois Waldner, David B. Lobell
Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learn…
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
Chenxi Lin, Liheng Zhong, Xiao-Peng Song +3
Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower…
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Jihyeon Lee, Dylan Grosz, Burak Uzkent +4
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thu…
Efficient Poverty Mapping using Deep Reinforcement Learning
Kumar Ayush, Burak Uzkent, Kumar Tanmay +3
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure meas…
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Kumar Ayush, Burak Uzkent, Marshall Burke +2
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scar…
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert +6
Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources…