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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 1910 |
| 2026-08-03 | 1913 |
| 2026-08-06 | 1913 |
Today
— stars today
This week
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Momentum
0.0
growth rate 0.00%/day
[](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models/graphs/commit-activity) [](https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) <img alt="GitHub watchers" src="https://img.shields.io/github/watchers/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models?style=social"> <img alt="GitHub stars" src="https://img.shields.io/github/stars/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models?style=social"> <img alt="GitHub forks" src="https://img.shields.io/github/forks/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models?style=social"> # <p align=center>`Awesome Remote Sensing Foundation Models`</p> :star2:**A collection of papers, datasets, benchmarks, code, and pre-trained weights for Remote Sensing Foundation Models (RSFMs).** ## 📢 Latest Updates :fire::fire::fire: Last Updated on 2026.05.06 :fire::fire::fire: ## Table of Contents - **Models** - [Remote Sensing Vision Foundation Models](#remote-sensing-vision-foundation-models) - [Remote Sensing Vision-Language Foundation Models](#remote-sensing-vision-language-foundation-models) - [Remote Sensing Generative Foundation Models](#remote-sensing-generative-foundation-models) - [Remote Sensing Vision-Location Foundation Models](#remote-sensing-vision-location-foundation-models) - [Remote Sensing Vision-Audio Foundation Models](#remote-sensing-vision-audio-foundation-models) - [Remote Sensing Agents](#remote-sensing-agents) - **Datasets & Benchmarks** - [Benchmarks for RSFMs](#benchmarks-for-rsfms) - [(Large-scale) Pre-training Datasets](#large-scale-pre-training-datasets) - [Embeddings data](#embeddings-data) - **Others** - [Relevant Projects](#relevant-projects) - [Survey/Commentary Papers](#surveycommentary-papers) ## Remote Sensing <ins>Vision</ins> Foundation Models |Abbreviation|Title|Publication|Paper|Code & Weights| |:---:|---|:---:|:---:|:---:| |**GeoKR**|**Geographical Knowledge-Driven Representation Learning for Remote Sensing Images**|TGRS2021|[GeoKR](https://ieeexplore.ieee.org/abstract/document/9559903)|[link](https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning)| |**-**|**Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding**|CVPRW2021|[Paper](https://openaccess.thecvf.com/content/CVPR2021W/EarthVision/html/Stojnic_Self-Supervised_Learning_of_Remote_Sensing_Scene_Representations_Using_Contrastive_Multiview_CVPRW_2021_paper.html)|[link](https://github.com/vladan-stojnic/CMC-RSSR)| |**GASSL**|**Geography-Aware Self-Supervised Learning**|ICCV2021|[GASSL](https://openaccess.thecvf.com/content/ICCV2021/html/Ayush_Geography-Aware_Self-Supervised_Learning_ICCV_2021_paper.html)|[link](https://github.com/sustainlab-group/geography-aware-ssl)| |**SeCo**|**Seasonal Contrast: Unsupervised Pre-Training From Uncurated Remote Sensing Data**|ICCV2021|[SeCo](https://openaccess.thecvf.com/content/ICCV2021/html/Manas_Seasonal_Contrast_Unsupervised_Pre-Training_From_Uncurated_Remote_Sensing_Data_ICCV_2021_paper.html)|[link](https://github.com/ServiceNow/seasonal-contrast)| |**RSP**|**An Empirical Study of Remote Sensing Pretraining**|TGRS2022|[RSP](https://ieeexplore.ieee.org/abstract/document/9782149)|[link](https://github.com/ViTAE-Transformer/Remote-Sensing-RVSA)| |**MATTER**|**Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks**|CVPR2022|[MATTER](https://openaccess.thecvf.com/content/CVPR2022/html/Akiva_Self-Supervised_Material_and_Texture_Representation_Learning_for_Remote_Sensing_Tasks_CVPR_2022_paper.html)|[link](https://github.com/periakiva/MATTER)| |**-**|**Self-supervised Vision Transformers for Land-cover Segmentation and Classification**|CVPRW2022|[Paper](https://openaccess.thecvf.com/content/CVPR2022W/EarthVision/html/
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4eabc28a99427842, llm:Repository name: 'Awesome-Remote-Sensing-Foundation-Models' suggests a curated 'awesome' list of foundation models for remote sensing (collection of resources). No topics or readme provided, classification inferred from name.
matched fp:4eabc28a99427842, llm:Repository name: 'Awesome-Remote-Sensing-Foundation-Models' suggests a curated 'awesome' list of foundation models for remote sensing (collection of resources). No topics or readme provided, classification inferred from name.
matched fp:4eabc28a99427842, llm:Repository name: 'Awesome-Remote-Sensing-Foundation-Models' suggests a curated 'awesome' list of foundation models for remote sensing (collection of resources). No topics or readme provided, classification inferred from name.
matched fp:4eabc28a99427842, llm:Repository name: 'Awesome-Remote-Sensing-Foundation-Models' suggests a curated 'awesome' list of foundation models for remote sensing (collection of resources). No topics or readme provided, classification inferred from name.