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
6 urban land cover classes, raster mask labels, 4-band RGB-IR aerial imagery (0.05m res.) & DSM, 38 image patches
| index | label | color |
|---|---|---|
| 1 | Impervious surfaces | 255, 255, 255 |
| 2 | Building | 0, 0, 255 |
| 3 | Low vegetation | 0, 255, 255 |
| 4 | Tree | 0, 255, 0 |
| 5 | Car | 255, 255, 0 |
| 6 | Clutter/background | 255, 0, 0 |
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
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.
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
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
| index | label | color |
|---|---|---|
| 1 | 水田 | 0,200,0 |
| 2 | 水浇田 | 150,250,0 |
| 3 | 旱耕地 | 150,200,150 |
| 4 | 园地 | 200,0,200 |
| 5 | 乔木林地 | 150,0,250 |
| 6 | 灌木林地 | 150,150,250 |
| 7 | 天然草地 | 250,200,0 |
| 8 | 人工草地 | 200,200,0 |
| 9 | 工业用地 | 200,0,0 |
| 10 | 城市住宅 | 250,0,150 |
| 11 | 村镇住宅 | 200,150,150 |
| 12 | 交通运输 | 250,150,150 |
| 13 | 河流 | 0,0,200 |
| 14 | 湖泊 | 0,150,200 |
| 15 | 坑塘 | 0,200,250 |
| 16 | 其他 | 0,0,0 |
5 argriculture categories
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
| index | label | gary |
|---|---|---|
| 1 | 植被 | 1 |
| 2 | 建筑 | 2 |
| 3 | 水体 | 3 |
| 4 | 道路 | 4 |
| 5 | 其他 | 0 |
- 初赛：10万高分光学影像和标注文件（一级分类（8类）），20万测试图片数据；
- 复赛：10万高分光学影像和标注文件（二级分类（17类）），30万测试图片数据；
| index | 一级标签 | gary(百位数字) |
|---|---|---|
| 1 | 水体 | 1 |
| 2 | 交通运输 | 2 |
| 3 | 建筑 | 3 |
| 4 | 耕地 | 4 |
| 5 | 草地 | 5 |
| 6 | 林地 | 6 |
| 7 | 裸土 | 7 |
| 8 | 其他 | 8 |
| index | 二级标签 | gary(十位及个位上的数字) |
|---|---|---|
| 1 | 水体 | 01 |
| 2 | 道路 | 02 |
| 3 | 建筑物 | 03 |
| 4 | 机场 | 04 |
| 5 | 火车站 | 05 |
| 6 | 光伏 | 06 |
| 7 | 停车场 | 07 |
| 8 | 操场 | 08 |
| 9 | 普通耕地 | 09 |
| 10 | 农业大棚 | 10 |
| 11 | 自然草地 | 11 |
| 12 | 绿地绿化 | 12 |
| 13 | 自然林 | 13 |
| 14 | 人工林 | 14 |
| 15 | 自然裸土 | 15 |
| 16 | 人为裸土 | 16 |
| 17 | 其他 | 17 |
训练集包含140,000张分辨率为2m/pixel，尺寸为256256的JPG图片，一共7个类别，对应gt 0-6
| index | label | gary |
|---|---|---|
| 1 | 建筑 | 0 |
| 2 | 耕地 | 1 |
| 3 | 林地 | 2 |
| 4 | 水体 | 3 |
| 5 | 道路 | 4 |
| 6 | 草地 | 5 |
| 7 | 其他 | 6 |
| 8 | 未标注区域 | 255 |
初赛训练集包含16017张分辨率为0.8m-2m/pixel，尺寸为256256的TIF图片，一共10个类别，对应gt 1-10
复赛训练集包含15904张分辨率为0.8m-2m/pixel，尺寸为256256的TIF图片，一共10个类别，对应gt 1-10
| index | label | gary |
|---|---|---|
| 1 | 耕地 | 1 |
| 2 | 林地 | 2 |
| 3 | 草地 | 3 |
| 4 | 道路 | 4 |
| 5 | 城镇建设用地 | 5 |
| 6 | 农村建设用地 | 6 |
| 7 | 工业用地 | 7 |
| 8 | 构筑物 | 8 |
| 9 | 水域 | 9 |
| 10 | 裸地 | 10 |
image_classification [pytorch]
image_seg [pytorch]
It is based on the codes of our Tianchi competition in 2021 (https://tianchi.aliyun.com/competition/entrance/531860/introduction).
In the competition, our team won the third place (please see Tianchi_README.md).
- 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]