MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images

arXiv:2312.12735v2 Announce Type: replace
Abstract: Semantic segmentation of remote sensing images plays a vital role in a wide range of Earth Observation (EO) applications, such as land use land cover mapping, environment monitoring, and sustainable development. Driven by rapid developments in Artificial Intelligence (AI), deep learning (DL) has emerged as the mainstream tool for semantic segmentation and has achieved many breakthroughs in the field of remote sensing. However, the existing DL-based methods mainly focus on unimodal visual data while ignoring …

abstract applications artificial artificial intelligence arxiv collaborative cs.cv deep learning development earth earth observation environment images intelligence language mapping metadata monitoring observation representation representation learning role segmentation semantic sensing sustainable sustainable development type vision vital