RemoteCLIP: A Vision Language Foundation Model for Remote Sensing

General-purpose foundation models have led to recent breakthroughs in artificial intelligence. In remote sensing, self-supervised learning (SSL) and Masked Image Modeling (MIM) have been adopted to build foundation models. However, these models primarily learn low-level features and require annotated data for fine-tuning. Moreover, they are inapplicable for retrieval and zero-shot applications due to the lack of language understanding. To address these limitations, we propose RemoteCLIP, the first vision-language foundation model for remote sensing that aims to learn robust visual features with rich semantics and aligned text embeddings for seamless downstream application. To address the scarcity of…

• NN
  National Natural Science Foundation of China

  Awards: 32371877, 62372155
• CA
  Chinese Aeronautical Establishment

  Award: 2022Z071108001
• QP
  Qinglan Project of Jiangsu Province of China
• WC
  Water Conservancy Science and Technology Project of Jiangsu Province

  Award: 2021063