<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 2024.08.08 :fire::fire::fire:

2024.8.08: Update a survey paper.
2024.8.06: Update MA3E.
2024.8.01: Update OmniSat and MM-VSF.

Models
Datasets & Benchmarks
- Benchmarks for RSFMs
- (Large-scale) Pre-training Datasets
Others
- Relevant Projects
- Survey 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	link
-	Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding	CVPRW2021	Paper	link
GASSL	Geography-Aware Self-Supervised Learning	ICCV2021	GASSL	link
SeCo	Seasonal Contrast: Unsupervised Pre-Training From Uncurated Remote Sensing Data	ICCV2021	SeCo	link
DINO-MM	Self-supervised Vision Transformers for Joint SAR-optical Representation Learning	IGARSS2022	DINO-MM	link
SatMAE	SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery	NeurIPS2022	SatMAE	link
RS-BYOL	Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images	JSTARS2022	RS-BYOL	null
GeCo	Geographical Supervision Correction for Remote Sensing Representation Learning	TGRS2022	GeCo	null
RingMo	RingMo: A remote sensing foundation model with masked image modeling	TGRS2022	RingMo	Code
RVSA	Advancing plain vision transformer toward remote sensing foundation model	TGRS2022	RVSA	link
RSP	An Empirical Study of Remote Sensing Pretraining	TGRS2022	RSP	link
MATTER	Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks	CVPR2022	MATTER	null
CSPT	Consecutive Pre-Training: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain	RS2022	CSPT	link
-	Self-supervised Vision Transformers for Land-cover Segmentation and Classification	CVPRW2022	Paper	link
BFM	A billion-scale foundation model for remote sensing images	Arxiv2023	BFM	null
TOV	TOV: The original vision model for optical remote sensing image understanding via self-supervised learning	JSTARS2023	TOV	link
CMID	CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding	TGRS2023	CMID	link
RingMo-Sense	RingMo-Sense: Remote Sensing Foundation Model for Spatiotemporal Prediction via Spatiotemporal Evolution Disentangling	TGRS2023	RingMo-Sense	null
IaI-SimCLR	Multi-Modal Multi-Objective Contrastive Learning for Sentinel-1/2 Imagery	CVPRW2023	IaI-SimCLR	null
CACo	Change-Aware Sampling and Contrastive Learning for Satellite Images	CVPR2023	CACo	link
SatLas	SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding	ICCV2023	SatLas	link
GFM	Towards Geospatial Foundation Models via Continual Pretraining	ICCV2023	GFM	link
Scale-MAE	Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning	ICCV2023	Scale-MAE	link
DINO-MC	DINO-MC: Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops	Arxiv2023	DINO-MC	link
CROMA	CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders	NeurIPS2023	CROMA	link
Cross-Scale MAE	Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing	NeurIPS2023	Cross-Scale MAE	link
DeCUR	DeCUR: decoupling common & unique representations for multimodal self-supervision	Arxiv2023	DeCUR	link
Presto	Lightweight, Pre-trained Transformers for Remote Sensing Timeseries	Arxiv2023	Presto	link
CtxMIM	CtxMIM: Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding	Arxiv2023	CtxMIM	null
FG-MAE	Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing	Arxiv2023	FG-MAE	link
Prithvi	Foundation Models for Generalist Geospatial Artificial Intelligence	Arxiv2023	Prithvi	link
RingMo-lite	RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework	Arxiv2023	RingMo-lite	null
-	A Self-Supervised Cross-Modal Remote Sensing Foundation Model with Multi-Domain Representation and Cross-Domain Fusion	IGARSS2023	Paper	null
EarthPT	EarthPT: a foundation model for Earth Observation	NeurIPS2023 CCAI workshop	EarthPT	link
USat	USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery	Arxiv2023	USat	link
FoMo-Bench	FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models	Arxiv2023	FoMo-Bench	link
AIEarth	Analytical Insight of Earth: A Cloud-Platform of Intelligent Computing for Geospatial Big Data	Arxiv2023	AIEarth	link
-	Self-Supervised Learning for SAR ATR with a Knowledge-Guided Predictive Architecture	Arxiv2023	Paper	link
Clay	Clay Foundation Model	-	null	link
Hydro	Hydro--A Foundation Model for Water in Satellite Imagery	-	null	link
U-BARN	Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series	JSTARS2024	Paper	link
GeRSP	Generic Knowledge Boosted Pre-training For Remote Sensing Images	Arxiv2024	GeRSP	GeRSP
SwiMDiff	SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image	Arxiv2024	SwiMDiff	null
OFA-Net	One for All: Toward Unified Foundation Models for Earth Vision	Arxiv2024	OFA-Net	null
SMLFR	Generative ConvNet Foundation Model With Sparse Modeling and Low-Frequency Reconstruction for Remote Sensing Image Interpretation	TGRS2024	SMLFR	link
SpectralGPT	SpectralGPT: Spectral Foundation Model	TPAMI2024	SpectralGPT	link
S2MAE	S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data	CVPR2024	S2MAE	null
SatMAE++	Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery	CVPR2024	SatMAE++	link
msGFM	**Bridging Remote Sensors with Multisensor Geospatial Foundation