Cross Domain Early Crop Mapping using CropSTGAN

arXiv:2401.07398v2 Announce Type: replace-cross
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