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Cross Domain Early Crop Mapping using CropSTGAN
April 22, 2024, 4:43 a.m. | Yiqun Wang, Hui Huang, Radu State
cs.LG updates on arXiv.org arxiv.org
Abstract: Driven by abundant satellite imagery, machine learning-based approaches have recently been promoted to generate high-resolution crop cultivation maps to support many agricultural applications. One of the major challenges faced by these approaches is the limited availability of ground truth labels. In the absence of ground truth, existing work usually adopts the "direct transfer strategy" that trains a classifier using historical labels collected from other regions and then applies the trained model to the target region. …
abstract applications arxiv availability challenges cs.cv cs.lg domain eess.iv generate labels machine machine learning major mapping maps promoted resolution satellite support truth type work
More from arxiv.org / cs.LG updates on arXiv.org
The Perception-Robustness Tradeoff in Deterministic Image Restoration
1 day, 17 hours ago |
arxiv.org
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