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20212026
most citedHigh-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest

8 citations · 38 across the 20 of their papers we have counts for

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Showing 2021 · cs.CVShow all

5 papers · 2 filters

cs.CV2021★ 1 cited

Mapping industrial poultry operations at scale with deep learning and aerial imagery

Caleb Robinson, Ben Chugg, Brandon Anderson +2

Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountabilit…

cs.CV2021★ 2 cited

TorchGeo: Deep Learning With Geospatial Data

Adam J. Stewart, Caleb Robinson, Isaac A. Corley +3

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among…

cs.CV2021★ 4 cited

Detecting Cattle and Elk in the Wild from Space

Caleb Robinson, Anthony Ortiz, Lacey Hughey +2

Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Pri…

cs.CV2021★ 1 cited

Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery

Caleb Robinson, Anthony Ortiz, Juan M. Lavista Ferres +2

Longitudinal studies are vital to understanding dynamic changes of the planet, but labels (e.g., buildings, facilities, roads) are often available only for a single point in time.…

cs.CV2021★ 8 cited

High-resolution land cover change from low-resolution labels: Simple baselines for the 2021 IEEE GRSS Data Fusion Contest

Nikolay Malkin, Caleb Robinson, Nebojsa Jojic

We present simple algorithms for land cover change detection in the 2021 IEEE GRSS Data Fusion Contest. The task of the contest is to create high-resolution (1m / pixel) land cover…