#Remote Sensing

This repository delivers a detailed set of resources including papers, datasets, benchmarks, code, and pre-trained weights dedicated to Remote Sensing Foundation Models (RSFMs). It systematically categorizes models into types like vision, vision-language, and generative, offering valuable developments such as PANGAEA, TEOChat, and SAR-JEPA. Designed for a neutral exploration, it aids in navigating through model types and associated projects, maintaining up-to-date information on significant research progress in journals like ICCV and NeurIPS. This collection serves professionals seeking an enhanced understanding of RSFMs through focuses on geographical knowledge, self-supervised learning, and multimodal fusion.

The RS-Mamba project features an innovative Recurrent State Space Model designed for dense prediction in large remote sensing images. By utilizing a state space model for the first time in this context, RS-Mamba achieves an effective global receptive field with linear complexity, setting new standards in semantic segmentation and change detection. This model is structured to optimally map spatial features across various directions, ensuring efficiency and power even with straightforward training methods. Explore the code and documentation to enhance remote sensing projects.

The platform presents a comprehensive overview of the application of multimodal large language models (MLLMs) in remote sensing. It includes carefully curated resources like model architectures, training processes, datasets, and evaluation benchmarks. The site continuously updates to reflect the latest in the field, emphasizing intelligent agents, instruction tuning, and the vision-language interface, while documenting contributions from numerous researchers to provide the latest on remote sensing MLLMs.