Pretrain 54
- Generic Knowledge Boosted Pre-training ForRemote Sensing Images
- Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
- S2MAE A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data
- RingMo A Remote Sensing Foundation Model With Masked Image Modeling
- Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
- One for All Toward Unified Foundation Models for Earth Vision
- SpectralGPT Spectral Foundation Model
- Generative ConvNet Foundation Model With Sparse Modeling and Low-Frequency Reconstruction for Remote Sensing Image Interpretation
- SwiMDiff Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image
- MTP Advancing Remote Sensing FoundationModel via Multi-Task Pretraining
- SkySense A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
- DINO-MC Self-supervised Contrastive Learning for Remote Sensing Imagery with Multi-sized Local Crops
- Scale-MAE A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
- Multi Modal Multi Objective Contrastive Learning for Sentinel 1-2 Imagery
- FoMo-Bench a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models
- EarthPT a foundation model for Earth Observation
- An Empirical Study of Remote Sensing Pretraining
- CtxMIM Context-Enhanced Masked Image Modeling for Remote Sensing Image Understanding
- Change-Aware Sampling and Contrastive Learning for Satellite Images
- CMID A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
- USat A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery
- TOV The Original Vision Model for Optical Remote Sensing Image Understanding via Self-Supervised Learning
- Foundation Models for Generalist Geospatial Artificial Intelligence
- Towards Geospatial Foundation Models via Continual Pretraining
- A billion-scale foundation model for remote sensing images
- A Self-Supervised Cross-Modal Remote Sensing Foundation Model with Multi-Domain Representation and Cross-Domain Fusion
- Predicting Gradient is Better Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
- Cross-Scale MAE A Tale of Multiscale Exploitation in Remote Sensing
- CROMA Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders
- SatlasPretrain A Large-Scale Dataset for Remote Sensing Image Understanding
- RingMo-lite A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework
- TOV The original vision model for optical remote sensing image understanding via self-supervised learning
- Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing
- DeCUR decoupling common & unique representations for multimodal self-supervision
- Rsprompter Learning to prompt for remote sensing instance segmentation based on visual foundation model
- Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
- Semantic segmentation of remote sensing images with self-supervised semantic-aware inpainting
- Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images
- Consecutive Pre-Training A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
- Self-supervised vision transformers for joint sar-optical representation learning
- Geographical Knowledge-Driven RepresentationLearning for Remote Sensing Images
- SatMAE Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
- Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks
- Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
- Advancing plain vision transformer toward remote sensing foundation model
- Multi-source remote sensing pretraining based on contrastive self-supervised learning
- Geography-aware self-supervised learning
- On Creating Benchmark Dataset for Aerial Image Interpretation Reviews, Guidances, and Million-AID
- Seasonal ContrastUnsupervised Pre-Training from Uncurated Remote Sensing Data
- Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding
- Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples
- Tile2Vec Unsupervised representation learning for spatially distributed data
- BIGEARTHNET A LARGE-SCALE BENCHMARK ARCHIVE FOR REMOTE SENSINGIMAGE UNDERSTANDING
- Functional Map of the World