Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels

arXiv:2403.02746v1 Announce Type: cross
Abstract: Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. However, it is still a non-trivial task hindered by complex ground details, various landforms, and the scarcity of accurate training labels over a wide-span geographic area. In this paper, we propose an efficient, weakly supervised framework (Paraformer), a.k.a. Low-to-High Network (L2HNet) V2, to guide large-scale HR land-cover mapping with easy-access historical land-cover data of low …

abstract arxiv challenges cs.cv cs.lg earth guidance humanity labels low mapping maps scale surface survey training type vital