zhu-xlab/FGMAE

Stars
15
Rank 1,371,379 (Top 28 %)
Language
Jupyter Notebook
License
Apache License 2.0
Created over 1 year ago
Updated about 1 year ago

zhu-xlab/FGMAE

zhu-xlab

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Feature guided masked Autoencoder for self-supervised learning in remote sensing

SSL4EO-S12

SSL4EO-S12: a large-scale dataset for self-supervised learning in Earth observation

M3R-CR

Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

DOFA

Code for Neural Plasticity-Inspired Foundation Model for Observing the Earth Crossing Modalities

Jupyter Notebook

SSL4EO-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 learning

ChatEarthNet

So2Sat-LCZ-Classification-Demo

DeCUR

DeCUR: decoupling common and unique representations for multimodal self-supervised learning.

ST-FSOD

ML4Earth-Hackathon-2022

Jupyter Notebook

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 Segmentation

So2Sat-POP

So2Sat-BuildingType

A large-scale dataset for building type classification using social media and aerial data

softcon

Multi-label guided soft contrastive learning for efficient Earth observation pretraining

A-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, 2023

Floodcast

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 Videos

SEN12MS

Schmitt et al., SEN12MS – A Curated Dataset of Georeferenced Multi-spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 153–160, 2019.

LULC_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 Brazil

SEN12MSCR

land_consumption

SEQNN

Hybrid Quantum Deep Learning with Superpixel Encoding for Earth Observation Data Classification

PeFNN

RS-DWL

This is official Pytorch implementation of "Decouple and Weight Semi-supervised Semantic Segmentation of Remote Sensing Images, " ISPRS, 2024.