In this project, we will open source some baseline codes for the remote sensing analysis task, such as semantic segmentation, scene classification, object detection, and image captioning, We will also collect some public datasets that can be used for remote sensing image research and analysis.
- Public Remote Sensing Dataset
- Baseline Codes (Semantic Segmentation/Scene Classification/Object Detection/Image Captioning)
- OpenSoure Codes
- Compitions About Remote Sensing
- ISPRS Potsdam 2D Semantic Labeling Contest
6 urban land cover classes, raster mask labels, 4-band RGB-IR aerial imagery (0.05m res.) & DSM, 38 image patches - DSTL Satellite Imagery Feature Detection Challenge
10 land cover categories from crops to vehicle small, 57 1x1km images, 3/16-band Worldview 3 imagery (0.3m-7.5m res.), Kaggle kernels - Slovenia Land Cover Classification
10 land cover classes, temporal stack of hyperspectral Sentinel-2 imagery (R,G,B,NIR,SWIR1,SWIR2; 10 m res.) for year 2017 with cloud masks, Official Slovenian land use land cover layer as ground truth. - SEN12MS
180,748 corresponding image triplets containing Sentinel-1 (VV&VH), Sentinel-2 (all bands, cloud-free), and MODIS-derived land cover maps (IGBP, LCCS, 17 classes, 500m res.). All data upsampled to 10m res., georeferenced, covering all continents and meterological seasons, Paper: Schmitt et al. 2018 - IEEE Data Fusion Contest 2018
20 land cover categories by fusing three data sources: Multispectral LiDAR, Hyperspectral (1m), RGB imagery (0.05m res.) - Segmentation Data of Sparse Representation and Intelligent Analysis of 2019 Remote Sensing Image competition (website has been closed)
16 land cover classes,4-band RGB-IR aerial imagery (4m res.) 8 patches of 7200x6800 for train and 2 patches of 7200x6800 for val and 10 patches of 7200x6800 for test - 2019 年县域农业大脑AI挑战赛
5 argriculture categories - CCF 卫星影像的AI分类与识别比赛 BDCI 2017
5 land cover classes(greenland, building, waterbody, road and other), 5 rgb images(R,G,B; 1 m res.) for train and val, 3 rgb images for test
- cuilunan/Unet-of-remote-sensing-image [tensorflow]
- Epsilon123/Semantic-Segmentation-of-Remote-Sensing-Images [keras]
- YudeWang/UNet-Satellite-Image-Segmentation [tensorflow]
- rmkemker/EarthMapper [tensorflow]
- TachibanaYoshino/Remote-sensing-image-semantic-segmentation [Keras]
- lcylmhlcy/Semantic-segmentation [pytorch]
- liushuo2018/ERN [caffe]
- Walkerlikesfish/HSNRS [caffe]
- 1044197988/Semantic-segmentation-of-remote-sensing-images [keras]
- fuweifu-vtoo/Semantic-segmentation [pytorch]
- reachsumit/deep-unet-for-satellite-image-segmentation [keras]
- lehaifeng/SCAttNet [tensorflow]
- NexGenMap/dl-semantic-segmentation [tensorflow ]
- yiskw713/boundary_loss_for_remote_sensing [pytorch]
- zetrun-liu/FCNs-for-road-extraction-keras [keras]
- susurrant/rs-img-classification [tensorflow]
- AI-Chen/Deeplab-v3-Plus-pytorch- [pytorch]
- mohuazheliu/ResUnet-a [tensorflow]
- zlkanata/DeepGlobe-Road-Extraction-Challenge [pytorch]
- DeepVoltaire/Dstl-Satellite-Imagery-Feature-Detection [keras]
- weihancug/SENet_ResNeXt_Remote_Sensing_Scene_Classification [pytorch]
- BiQiWHU/DenseNet40-for-HRRSISC [tensorflow]
- weihancug/SSGF-for-HRRS-scene-classification [caffe]
- Arafat123-iit/A-System-for-Effecient-Remote-Sensing-Image-Scene-Classification- [keras]
- Aaromxj/SF-CNN [Caffe]
- Aaron-Lst/ARCNet [pytorch]
- Wanke15/Feature_extraction-SVM-classification-Remote-sensing [caffe]
- williamzhao95/Pay-More-Attention [Mxnet]
- henanjun/SccovNet [matlab]
- clw5180/remote_sensing_object_detection_2019 [pytorch]
- jiangruoqiao/RICNN_GongCheng_CVPR2015 [tensorflow]
- R-Stefano/Remote-Sensing-Analysis [tensorflow]
- WenchaoliuMUC/Detection-of-Multiclass-Objects-in-Optical-Remote-Sensing-Images [pytorch]
- weihancug/Remote-Sensing-Object-Detection-with-Oriented-Bouding-Box [pytorch]
- Pilot-Zhang/ssd.pytorch [pytorch]