Labeled Data Scarcity is a challenge in machine learning and remote sensing characterized by the insufficient availability of ground-truth annotated samples required to train and validate supervised land cover classification models effectively.
322
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2017–2026
273
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Publications
322
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Lihe Yang, Bingyi Kang, Zilong Huang et al. · 2024 · 889 citations
Learning From Synthetic Data for Crowd Counting in the Wild
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453
Peking University
Beijing, China
9 h-index
18 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
14 publications
667 citations
Chinese Academy of Sciences
Beijing, China
8 h-index
24 publications
621 citations
Tsinghua University
Beijing, China
7 h-index
23 publications
1.4K citations
Xidian University
Xi'an, China
6 h-index
26 publications
765 citations
University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
13 publications
924 citations
Georgia Institute of Technology
Atlanta, United States
6 h-index
15 publications
1.4K citations
University of Hong Kong
Pok Fu Lam, Hong Kong
6 h-index
15 publications
3.8K citations
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Repository · 31 publications · 1K citations
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- IEEE Geoscience and Remote Sensing Letters
GRSL · 11 publications · 1.4K citations
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8 publications · 269 citations
8
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- IEEE Transactions on Geoscience and Remote Sensing
8 publications · 530 citations
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- Proceedings of the AAAI Conference on Artificial Intelligence
Conference · 8 publications · 328 citations
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