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SSL4EO-S12
SSL4EO-S12: a large-scale dataset for self-supervised learning in Earth observationM3R-CR
Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and BenchmarkDOFA
Code for Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing ModalitiesSSL4EO-Review
Codes for the benchmark experiments in the paper "Self-supervised Learning in Remote Sensing: A Review"UniDA
Code for TGRS paper "Universal Domain Adaptation for Remote Sensing Image Scene Classification"augsburg_Multimodal_Data_Set_MDaS
So2Sat-LCZ42
Zhu et al., So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification, IEEE Geoscience and Remote Sensing Magazine, in press, 2020.DINO-MM
Self-supervised vision transformers for joint SAR-optical representation learningChatEarthNet
So2Sat-LCZ-Classification-Demo
FGMAE
Feature guided masked Autoencoder for self-supervised learning in remote sensingDeCUR
DeCUR: decoupling common and unique representations for multimodal self-supervised learning.ST-FSOD
ML4Earth-Hackathon-2022
ML4Earth-Hackathon-2023
UCDFormer
Code for TGRS paper "UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation"HTC-DC-Net
Official Implementation of submitted TGRS paper "HTC-DC Net: A Network for Monocular Height Estimation from Single Remote Sensing Images"rrsis
So2Sat-POP-DL
AIO2
Official implementation of TGRS paper - AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image SegmentationSo2Sat-POP
So2Sat-BuildingType
A large-scale dataset for building type classification using social media and aerial datasoftcon
Multi-label guided soft contrastive learning for efficient Earth observation pretrainingA-Relation-Augmented-Fully-Convolutional-Network-for-Semantic-Segmentation-in-Aerial-Scenes
Mou et al., A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes, CVPR 2019, accepted.PFST
Official implementation of TGRS paper: "Pseudo Features Guided Self-training for Domain Adaptive Semantic Segmentation of Satellite Images"SARptical
Wang et al., “Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring,” IEEE Transactions on Geoscience and Remote Sensing, 55(1), 14–26, 2017.Deep-Recurrent-Neural-Networks-for-Hyperspectral-Image-Classification
Mou et al., Deep Recurrent Neural Networks for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 55(7), 3639-3655, 2017.VQA-easy2hard
Code for paper "From easy to hard: Learning language-guided curriculum for visual question answering on remote sensing data"AdaptMatch
This is official code of "AdaptMatch: Adaptive Matching for Semisupervised Binary Segmentation of Remote Sensing Images," IEEE-TGRS, 2023Floodcast
Busy-parking-lot-dataset---vehicle-detection-in-UAV-video
L. Mou and X. X. Zhu, "Vehicle instance segmentation from aerial image and video using a multi-task learning residual fully convolutional network," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 11, pp. 6699-6711, 2018.Benchmark-of-streetview-image-of-individual-building-instance
Kang et al., Building instance classification using street view images, ISPRS Journal of Photogrammetry and Remote Sensing, 145, 44-59, 2018.ERA-DATASET
A Dataset and Deep Learning Benchmark for Event Recognition in Aerial VideosLULC_MSI_QCNN
Land Cover Classification from Sentinel-2 Images with Quantum-Classical Convolutional Neural Networks (FQCNN and MQCNN)Cross-cityDTL
ForestFormer
BiomassUQ
Data used in the paper "Biomass Estimation and Uncertainty Quantification from Tree Height".UTB_codes
Nationwide urban tree canopy mapping and coverage assessment in BrazilSEN12MSCR
land_consumption
SEQNN
Hybrid Quantum Deep Learning with Superpixel Encoding for Earth Observation Data ClassificationPeFNN
RS-DWL
This is official Pytorch implementation of "Decouple and Weight Semi-supervised Semantic Segmentation of Remote Sensing Images, " ISPRS, 2024.Love Open Source and this site? Check out how you can help us