6 citations · 10 across the 8 of their papers we have counts for
8 papers
Fields of the Planet: Field Boundary Mapping Beyond 10m
Isaac Corley, Caleb Robinson, Jennifer Marcus +1
Field-boundary maps support crop monitoring, irrigation planning, and yield estimation, but many smallholder parcels span only a few 10 m Sentinel-2 pixels. We introduce Fields of…
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
Kelsey Doerksen, Hannah Kerner
Current Earth observation benchmarks focus on measuring performance on diverse tasks and applications, typically measuring generalization in-distribution. But when models are deplo…
The first global agricultural field boundary map at 10m resolution
Caleb Robinson, Gedeon Muhawenayo, Subash Khanal +9
The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available…
PRUE: A Practical Recipe for Field Boundary Segmentation at Scale
Gedeon Muhawenayo, Caleb Robinson, Subash Khanal +10
Large-scale maps of field boundaries are essential for agricultural monitoring tasks. Existing deep learning approaches for satellite-based field mapping are sensitive to illuminat…
Fields of The World: A Field Guide for Extracting Agricultural Field Boundaries
Isaac Corley, Hannah Kerner, Caleb Robinson +1
Field boundary maps are a building block for agricultural data products and support crop monitoring, yield estimation, and disease estimation. This tutorial presents the Fields of…
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…
Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels
Hannah Kerner, Saketh Sundar, Mathan Satish
The goal of field boundary delineation is to predict the polygonal boundaries and interiors of individual crop fields in overhead remotely sensed images (e.g., from satellites or d…