activity
20182026
most citedTwo Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions

55 citations · 86 across the 10 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

SolarBench: A global solar energy nowcasting benchmark

Yuhao Nie, Stephen Campbell, Quentin Paletta +18

As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply…

cs.CV2026

Conformal Prediction Sets for Instance Segmentation

Kerri Lu, Dan M. Kluger, Stephen Bates +1

Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is…

cs.CV2023

Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions

Philippe Rufin, Sherrie Wang, Sá Nogueira Lisboa +3

Transfer learning allows for resource-efficient geographic transfer of pre-trained field delineation models. However, the scarcity of labeled data for complex and dynamic smallhold…

cs.CV2023

Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types

Jordi Laguarta Soler, Thomas Friedel, Sherrie Wang

Accurate crop type maps are an essential source of information for monitoring yield progress at scale, projecting global crop production, and planning effective policies. To date,…

cs.CV2022

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…

cs.CV2018

Tile2Vec: Unsupervised representation learning for spatially distributed data

Neal Jean, Sherrie Wang, Anshul Samar +3

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and com…