├── DatasetPaper.ipynb ├── README.md ├── Scatter.jpg └── Schmitt_IGARSS2021.png /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Satellite Benchmark Datasets 2 | 3 | Annotated datasets have become one of the most crucial preconditions for the development and evaluation of machine learning-based methods designed for the automated interpretation of remote sensing data. In this study, we have reviewed the historic development of such datasets, discussed their features based on a few selected examples, and addressed open issues for future developments. 4 | 5 | To the best of our knowledge, and to the best of our findings, until May 2021, the following graph displays more than 90% of the benchmark datasets in remote sensing and photogrammetry. This graph includes all the available datasets which are acquired using airborne/spaceborne imaging/radar sensors. (It DOES NOT include POINT CLOUD datasets). 6 | This list is going to be more complete in the future and we are still working on it. 7 | 8 | ## Contributors 9 | This study is a collaboration between [Michael Schmitt](https://schmitt-muc.github.io/), [Ronny Hänsch](http://www.rhaensch.de/) and me. 10 | 11 | ## Datasets 12 | **181** benchmark satellite datasets have been reviewed and their statistics is provided in the following table. The table includes a link to the datasets webpages, their volume, publication date, their specidied task and some other stats. The figures in the paper summarize these stats. 13 | 14 | *(For the high resolution scatter image please refer to the paper.)* 15 | ![ScatterTimeLine](https://user-images.githubusercontent.com/53389122/125167119-2ff6d300-e1b4-11eb-86b6-98e3636916f7.jpg) 16 | 17 | Also, the following drawing simplifies the concept behind our "Size Measure" and "Volume Measure". 18 | 19 |

20 | 21 | ### Paper 22 | UPDATE (09 August 2023): Our review paper with a team of scientists around the world is published in IEEE GRSM. You can find it in [this link](https://ieeexplore.ieee.org/abstract/document/10213439). 23 | You can find more information at our [IGARSS-2021 paper](https://arxiv.org/abs/2105.11726). 24 | 25 | ⭐⭐⭐ Our new paper which is an extended version of the above paper is published as a review paper in IEEE Geoscience and Remote Sensing Magazine (GRSM). You can find it in [this link](https://ieeexplore.ieee.org/document/10213439).⭐⭐⭐ 26 | 27 | ***NOTICE: You can find each dataset's link on the most right column. Scroll the table to right!*** 28 | ***NOTICE: Finish the table. You'll find more AWESOME stuff!*** 29 | 30 | ### Datasets table 31 | 32 | |Sample Image | Dataset Name | Year of publication | Number of images | Size of images | Size Measure | Task | Number of classes | Volume (MB) | link | 33 | |------------------|--------------------------------------------------------------------------------|---------------------|------------------|----------------|--------------|--------------|-------------------|-------------|-------------------------------------------------------------------------------------------------------------------------------------| 34 | | ![image](https://user-images.githubusercontent.com/53389122/124712486-9d84d400-df14-11eb-86cd-79fff9ab9207.png) | AID | 2017 | 10000 | 600 | 3600000000 | Class | 30 | 2440 | https://captain-whu.github.io/AID/ | 35 | | ![image](https://user-images.githubusercontent.com/53389122/124763114-db9beb00-df48-11eb-9c78-f35c79912297.png) | AID++ | 2018 | 400000 | | | Class | 46 | | | 36 | | ![image](https://user-images.githubusercontent.com/53389122/124713159-6f53c400-df15-11eb-8ebf-00e0400f42a0.png) | Oil Storage Tanks | 2019 | 10000 | 512 | 2621440000 | OD | | 3000 | https://www.kaggle.com/towardsentropy/oil-storage-tanks | 37 | | ![image](https://user-images.githubusercontent.com/53389122/124763189-f2424200-df48-11eb-9e43-e194a5e296cc.png) | BigEarthNet | 2019 | 590326 | 120 | 8500694400 | Class | | 121000 | http://bigearth.net/ | 38 | | ![image](https://user-images.githubusercontent.com/53389122/125168930-26be3400-e1bd-11eb-9e3e-22d88fd77cd4.png) | Brazilian Coffee Scene | 2015 | 2876 | 64 | 11780096 | Class | 2 | 4.50 | http://www.patreo.dcc.ufmg.br/2017/11/12/brazilian-coffee-scenes-dataset/ | 39 | | ![image](https://user-images.githubusercontent.com/53389122/125168991-70a71a00-e1bd-11eb-8823-d6b1c58f5fb0.png) | BrazilDAM | 2020 | 769 | 384 | 113393664 | Class | 2 | 57000 | http://www.patreo.dcc.ufmg.br/2020/01/27/brazildam-dataset/ | 40 | | ![Bridge_Dataset](https://user-images.githubusercontent.com/53389122/125169800-114b0900-e1c1-11eb-9c71-07db9d10c03d.png) | Bridges Dataset | 2019 | 500 | 3822 | 68232000000 | OD | 1 | 1450 | http://www.patreo.dcc.ufmg.br/2019/07/10/bridge-dataset/ | 41 | | ![img_dataset-1024x588](https://user-images.githubusercontent.com/53389122/125169809-190aad80-e1c1-11eb-9b30-1f338d783088.png) | Brazilian Cerrado-Savanna Scenes | 2016 | 1311 | 64 | 5369856 | Class | 4 | 11 | http://www.patreo.dcc.ufmg.br/2017/11/12/brazilian-cerrado-savanna-scenes-dataset/ | 42 | | ![pools](https://user-images.githubusercontent.com/53389122/125169826-2b84e700-e1c1-11eb-9dbf-7c7478bd2cfc.jpeg) | BH-Pools + BH-WaterTanks | 2020 | 350 | 3000 | 3150000000 | SemSeg | 1 | 1900 | http://www.patreo.dcc.ufmg.br/2020/07/29/bh-pools-watertanks-datasets/ | 43 | | ![AiRound](https://user-images.githubusercontent.com/53389122/127604930-8aec75d0-7b63-466b-ba8a-a289cb920a30.jpg) | AiRound | 2020 | 11753 | 300 | 1057770000 | Class | 11 | 33000 | http://www.patreo.dcc.ufmg.br/2020/07/22/multi-view-datasets/ | 44 | | ![CV-BrCT](https://user-images.githubusercontent.com/53389122/127604944-648db894-7535-445a-a3e7-0b5623b43ce7.gif) | CV-BrCT (Cross-View Brazilian Construction Type) | 2020 | 24000 | 500 | 6000000000 | Class | 9 | 9200 | http://www.patreo.dcc.ufmg.br/2020/07/22/multi-view-datasets/ | 45 | | ![EuroSAT](https://user-images.githubusercontent.com/53389122/127604962-5755ea9d-d039-439e-934a-a9e3f264d157.jpg) | EuroSAT | 2018 | 27000 | 64 | 110592000 | Class | 10 | 1920 | https://github.com/phelber/EuroSAT# | 46 | | ![resisc45-3 0 0](https://user-images.githubusercontent.com/53389122/127605075-ad46c958-b924-4fe0-a89d-179b34a8de07.png) | NWPU-RESISC45 | 2016 | 31500 | 256 | 2064384000 | Class | 45 | 404.7 | https://github.com/tensorflow/datasets/blob/master/docs/catalog/resisc45.md | 47 | || NWPU-VHR10 | 2014 | 800 | 1000 | 800000000 | OD | 10 | 73 | https://github.com/chaozhong2010/VHR-10_dataset_coco | 48 | || SSDD (RadarSat-2, TerraSAR-X, S-1) | 2017 | 1160 | 500 | 290000000 | OD | 1 | | | 49 | || Dataset for Ship Classification (DSCR) | 2019 | 1951 | | | Class | | | https://github.com/DYH666/DSCR | 50 | || SAR Ship Detection (GF-2, S-1) | 2019 | 43819 | 256 | 2871721984 | OD | 1 | 407 | https://github.com/CAESAR-Radi/SAR-Ship-Dataset | 51 | || AIR-SARShip-2.0 (GF-3) | 2020 | 300 | 1000 | 300000000 | OD | 1 | 224 | http://radars.ie.ac.cn/web/data/getData?dataType=SARDataset | 52 | | ![LSDD](https://user-images.githubusercontent.com/53389122/127605092-b581dc3b-b1e4-45cb-a560-6d5c2ea981d4.JPG) | LS-SSDD (Large Scale) | 2020 | 15 | 20000 | 5760000000 | OD | 1 | 7800 | https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN | 53 | || HRSID (Ship Detection, S-1, TerraSAR-X) | 2020 | 5604 | 800 | 3586560000 | OD | 1 | 581 | https://github.com/chaozhong2010/HRSID | 54 | || High Resolution Semantic Change Detection (HRSCD) | 2019 | 582 | 10000 | 58200000000 | CD | | 5000 | https://ieee-dataport.org/open-access/hrscd-high-resolution-semantic-change-detection-dataset ; https://rcdaudt.github.io/hrscd/ | 55 | || HRSC2016 (Ship Detection) | 2017 | 1061 | 1100 | 816970000 | OD | 26 | | http://www.escience.cn/people/liuzikun/DataSet.html%E2%80%9D | 56 | || Remote Sensing Object Detection (RSOD) | 2017 | 946 | 1000 | 946000000 | OD | 4 | 309 | https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset- | 57 | || PatternNet | 2018 | 30400 | 256 | 1992294400 | Class | 38 | 1300 | https://sites.google.com/view/zhouwx/dataset | 58 | || Bijie Landslide | 2020 | 2773 | 200 | 110920000 | Class | 1 | 513 | http://gpcv.whu.edu.cn/data/Bijie_pages.html | 59 | || RSC11 | 2016 | 1232 | 512 | 322961408 | Class | 11 | | | 60 | || RSD46-WHU | 2017 | 117000 | 256 | 7667712000 | Class | 46 | | https://pan.baidu.com/s/1mMDKUu02V0s8rXstewv26A | 61 | || RSI-CB256 | 2017 | 24000 | 256 | 1572864000 | Class | 35 | 2240 | https://1drv.ms/u/s!Am218i8VSQEBaTyXDc-zA56zPv4 | 62 | || RSI-CB128 | 2017 | 36000 | 128 | 589824000 | Class | 45 | 879 | https://1drv.ms/u/s!Auv9HKTH1GC9jBbv-XzBFyMegqlL | 63 | || RSSCN7 | 2015 | 2800 | 400 | 448000000 | Class | 7 | 86 | https://figshare.com/articles/dataset/RSSCN7_Image_dataset/7006946 | 64 | || SATIN | 2023 | 775632 | 28-10494 | | Class | >250 | 56600 | https://satinbenchmark.github.io/ | 65 | || SAT-4 | 2015 | 500000 | 28 | 392000000 | Class | 4 | 1150 | http://csc.lsu.edu/~saikat/deepsat/ | 66 | || SAT-6 | 2015 | 405000 | 28 | 317520000 | Class | 6 | 1150 | http://csc.lsu.edu/~saikat/deepsat/ | 67 | || SemCity Toulouse | 2020 | 16 | 3500 | 196000000 | SemSeg | | 5200 | http://rs.ipb.uni-bonn.de/data/ | 68 | | ![image](https://user-images.githubusercontent.com/53389122/124713429-cce81080-df15-11eb-8dfc-00d5069e8680.png) | SEN12MS | 2019 | 180662 | 256 | 11839864832 | Class/SemSeg | | 510000 | https://mediatum.ub.tum.de/1474000 | 69 | || SEN12MS-CR | 2020 | 122218 | 256 | 8009678848 | CR | | 272000 | https://mediatum.ub.tum.de/1554803 | 70 | || SIRI-WHU (Google + USGS) | 2016 | 2400 | 200 | 96000000 | Class | 12 | 700 | http://www.lmars.whu.edu.cn/prof_web/zhongyanfei/e-code.html | 71 | || SZTAKI AirChange | 2012 | 13 | 800 | 7920640 | OD | 2 | 42 | http://web.eee.sztaki.hu/remotesensing/airchange_benchmark.html | 72 | || Things And Stuff (TAS) | 2008 | 30 | 792 | 18817920 | OD | 1 | 11 | http://ai.stanford.edu/~gaheitz/Research/TAS/ | 73 | || UC Merced | 2010 | 2100 | 256 | 137625600 | Class | 21 | 317 | http://weegee.vision.ucmerced.edu/datasets/landuse.html | 74 | || WHU-RS19 | 2012 | 1013 | 600 | 364680000 | Class | 19 | | http://captain.whu.edu.cn/datasets/WHU-RS19.zip | 75 | || SpaceNet-1 (Building Detection v1) | 2016 | 9735 | 650 | 4113037500 | SemSeg | 1 | 31000 | https://spacenet.ai/spacenet-buildings-dataset-v1/ | 76 | || SpaceNet-2 (Building Detection v2) | 2017 | 24586 | 650 | 10387585000 | SemSeg | 1 | 182200 | https://spacenet.ai/spacenet-buildings-dataset-v2/ | 77 | || SpaceNet-3 (Road Network Detection) | 2017 | 3711 | 1300 | 6271590000 | SemSeg | 1 | 182200 | https://spacenet.ai/spacenet-roads-dataset/ | 78 | || SpaceNet-4 (Multi-View Overhead Imagery) | 2018 | 60000 | 900 | 48600000000 | OD | | 186000 | https://spacenet.ai/off-nadir-building-detection/ | 79 | || SpaceNet-5 (Road Network Extraction and Route Travel Time Est.) | 2019 | 2369 | 1300 | 4003610000 | SemSeg | 1 | 84103 | https://spacenet.ai/sn5-challenge/ | 80 | || SpaceNet-6 (Multi-Sensor All Weather Mapping) | 2020 | 3401 | 900 | 2754810000 | SemSeg | 1 | 368 | https://spacenet.ai/sn6-challenge/ | 81 | || SpaceNet-7 (Multi-Temporal Urban Development) | 2020 | 1525 | 1024 | 1599078400 | SemSeg | 1 | 20582 | https://spacenet.ai/sn7-challenge/ | 82 | || Functional Map of the World | 2018 | 523846 | | 1.084E+12 | Class | 63 | 3500000 | https://github.com/fMoW/dataset | 83 | || xView | 2018 | 1413 | 3000 | 56000000000 | Class | 60 | 20000 | https://challenge.xviewdataset.org/welcome | 84 | || xView2 | 2018 | 22068 | 1024 | 23139975168 | CD | 4 | 51000 | https://xview2.org/ | 85 | || LandCoverNet v1.0 | 2020 | 1980 | 256 | 129761280 | Class | 7 | 81900 | https://registry.mlhub.earth/10.34911/rdnt.d2ce8i/ | 86 | || Agriculture-Vision | 2020 | 21061 | 512 | 5521014784 | OD | 6 | 4392.50 | https://www.agriculture-vision.com/dataset#h.p_C89EwHgTp3-L | 87 | || INRIA Aerial Image Labeling | 2017 | 360 | 1500 | 810000000 | OD | | 19510 | https://project.inria.fr/aerialimagelabeling/ | 88 | || DeepGlobe (Road Detection) | 2018 | 8570 | 1024 | 8986296320 | Class | 1 | 3840 | https://www.kaggle.com/balraj98/deepglobe-road-extraction-dataset | 89 | || DeepGlobe (Building Detection) | 2018 | 24586 | 650 | 10387585000 | Class/SemSeg | 1 | | https://competitions.codalab.org/competitions/18544 | 90 | || DeepGlobe (LandCover Classification) | 2018 | 1146 | 2448 | 6867638784 | SemSeg | 7 | 2750 | https://competitions.codalab.org/competitions/18544 | 91 | || Slovenia Land Cover | 2019 | 940 | 500 | 235000000 | Class | 10 | 190000 | http://eo-learn.sentinel-hub.com/ ; http://eo-learn.sentinel-hub.com.s3.eu-central-1.amazonaws.com/eopatches_slovenia_2017_full.zip | 92 | || AIST Building Change Detection | 2017 | 16950 | 160 | 356100096 | CD | 1 | 18200 | https://github.com/gistairc/ABCDdataset | 93 | || Onera Satellite Change Detection | 2018 | 24 | 600 | 8640000 | CD | 2 | 489 | https://rcdaudt.github.io/oscd/ | 94 | || DSTL Feature Detection (3Band) | 2016 | 450 | 3391 | 5174496450 | OD | 10 | 12870 | https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/data?select=three_band.zip | 95 | || DSTL Feature Detection (16Band) | 2016 | 1350 | 3391 | 15523489350 | OD | 10 | 7300 | https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/data?select=three_band.zip | 96 | || Kaggle Planet Forest | 2017 | 150000 | 256 | 9830400000 | Class | 12 | 33000 | https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data | 97 | || DOTA v1.0 | 2018 | 2806 | 4000 | 44896000000 | OD | 15 | 18000 | https://captain-whu.github.io/DOTA/index.html | 98 | || DOTA v1.5 | 2019 | 2806 | 4000 | 44896000000 | OD | 16 | 18000 | https://captain-whu.github.io/DOTA/index.html | 99 | || DOTA v2.0 | 2020 | 11268 | 4000 | 1.80288E+11 | OD | 18 | 34280 | https://captain-whu.github.io/DOTA/index.html | 100 | || iSAID | 2020 | 2806 | 4000 | 44896000000 | SemSeg | 15 | 6544 | https://captain-whu.github.io/iSAID/index.html | 101 | || DLR-SkyScapes | 2019 | 16 | 4680 | 336420864 | SemSeg | 31 | | https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-12760/22294_read-58694 | 102 | || VIVID - PETS2005 | | 9283 | 640 | 2851737600 | OD | 1 | | | 103 | || RarePlanes | 2020 | 713348 | 512 | 1.87E+11 | OD | 110 | 318000 | https://www.cosmiqworks.org/rareplanes-public-user-guide/ | 104 | || Kaggle Airbus Ship Detection | 2018 | 192556 | 768 | 1.13574E+11 | OD | | 30000 | https://www.kaggle.com/c/airbus-ship-detection | 105 | || DLR-ACD | 2019 | 33 | 4458 | 636542478 | OD | 1 | | https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-12760/22294_read-58354 | 106 | || STGAN Cloud Removal | 2019 | 217190 | 256 | 14233763840 | SemSeg | 1 | 1500 | https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/BSETKZ | 107 | || WHU Building Dataset | 2018 | 25577 | 512 | 6704857088 | OD | 1 | 25000 | http://gpcv.whu.edu.cn/data/building_dataset.html | 108 | || MtS-WH | 2019 | 190 | 150 | 4275000 | CD | 1 | 459 | http://sigma.whu.edu.cn/newspage.php?q=2019_03_26_ENG | 109 | || Synthetic & Real Dataset | 2018 | 16000 | 256 | 1048576000 | CD | | 2700 | https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9/edit | 110 | || CloudCast | 2020 | 70080 | 1229 | 99502387200 | Other | | 328000 | https://vision.eng.au.dk/cloudcast-dataset/ | 111 | || SECOND | 2020 | 4662 | 512 | 1222115328 | CD | 30 | 2200 | http://www.captain-whu.com/project/SCD/ | 112 | || LEVIR-CD | 2020 | 637 | 1024 | 667942912 | CD | 1 | 2700 | https://justchenhao.github.io/LEVIR/ | 113 | || COWC | 2016 | 388435 | 256 | 25456476160 | OD | 1 | 64000 | https://gdo152.llnl.gov/cowc/ | 114 | || Hurricane Wind Speed | 2021 | 114634 | 366 | 15355912104 | Other | | 1500 | https://www.drivendata.org/competitions/72/predict-wind-speeds/page/274/ | 115 | || Proba-V Super Resolution | 2018 | 1160 | 384 | 171048960 | SR | | 692 | https://kelvins.esa.int/proba-v-super-resolution/data/ | 116 | || SEN1-2 | 2018 | 282384 | 256 | 18506317824 | SemSeg | | 43700 | https://mediatum.ub.tum.de/1520883?show_id=1436631 | 117 | || So2Sat LCZ42 | 2019 | 400673 | 32 | 410289152 | Class | | 51800 | https://mediatum.ub.tum.de/1454690 | 118 | || Indian Pines | 2015 | 1 | 145 | 21025 | Class | 16 | 6 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 119 | || DFC21-MSD | 2021 | | | | | | | | 120 | || DFC21-DSE | 2021 | | | | | | | | 121 | || DFC20 | 2020 | | | | | | | | 122 | || DFC19 | 2019 | | | | | | | | 123 | || DFC18 (Multi-sensor land use land cover classification) | 2018 | | | | | 20 | | | 124 | || DFC17 (Local Climate Zones Classification) | 2017 | | | | | | | | 125 | || DFC 2007 | 2007 | 1 | 787 | 619369 | SemSeg | | 19 | http://www.grss-ieee.org/community/technical-committees/data-fusion/ | 126 | || Salinas | 2015 | 1 | 365 | 111104 | Class | 16 | 27 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 127 | || Botswana | 2015 | 1 | 875 | 375000 | Class | 14 | 79 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 128 | || SanFrancisco | 2015 | 1 | | | Class | | | | 129 | || Kennedy Space Center | 2015 | 1 | 550 | 311100 | Class | 13 | 57 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 130 | || Pavia Center | 2015 | 1 | 1096 | 1201216 | Class | 9 | 124 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 131 | || Pavia University | 2015 | 1 | 610 | 372100 | Class | 9 | 33 | http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes | 132 | || ISPRS 2D - Potsdam | 2011 | 38 | 6000 | 1368000000 | SemSeg | 6 | 16000 | https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-potsdam/ | 133 | || ISPRS 2D - Vaihingen | 2011 | 33 | 2200 | 156750000 | SemSeg | 6 | 17000 | https://www2.isprs.org/commissions/comm2/wg4/benchmark/2d-sem-label-vaihingen/ | 134 | || GTA Birds Eye View (Surrounding Vehicle Awareness) | 2017 | 1000000 | 1920 | 1920000000 | Other | | 350000 | https://github.com/ndrplz/surround_vehicles_awareness | 135 | || Semantic Drone Dataset | 2019 | 400 | 5000 | 9600000000 | SemSeg | 20 | 4000 | https://www.tugraz.at/index.php?id=22387 | 136 | || Kaggle Cloud Detection | 2019 | 9244 | 1750 | 27177360000 | Class | 4 | 6000 | https://www.kaggle.com/c/understanding_cloud_organization/data | 137 | || Vehicle Detection in Aerial Imagery (VEDAI) | 2015 | 1250 | 1024 | 1310720000 | Class | 9 | 3990 | https://downloads.greyc.fr/vedai/ | 138 | || WAMI DIRSIG | | | | | | | | | 139 | || SEN-12-FLOOD | | | | | | | | https://ieee-dataport.org/open-access/sen12-flood-sar-and-multispectral-dataset-flood-detection | 140 | || WHU Cloud Dataset | 2020 | 859 | 512 | 225181696 | OD/SemSeg | 1 | 3650 | http://gpcv.whu.edu.cn/data/WHU_Cloud_Dataset.html | 141 | || WHU MVS/Stereo Dataset | 2020 | 1776 | 5376 | 51328843776 | Other | | 98000 | http://gpcv.whu.edu.cn/data/WHU_MVS_Stereo_dataset.html | 142 | || WHU Multi-View Dataset | 2020 | 28400 | 768 | 8375500800 | Other | | 12600 | http://gpcv.whu.edu.cn/data/WHU_MVS_Stereo_dataset.html | 143 | || WHU Stereo Dataset | 2020 | 21868 | 768 | 6449135616 | Other | | 8600 | http://gpcv.whu.edu.cn/data/WHU_MVS_Stereo_dataset.html | 144 | || Aerial Maritime Drone Dataset | | 508 | | | | | | https://public.roboflow.com/object-detection/aerial-maritime | 145 | || ERA (Event Recognition in Aerial videos) | 2020 | 343680 | 640 | 1.40771E+11 | Other | | 6300 | https://lcmou.github.io/ERA_Dataset/ | 146 | || AU-AIR | 2020 | 32823 | 1920 | 68061772800 | OD | 8 | 2200 | https://bozcani.github.io/auairdataset | 147 | || BIRDSAI | 2020 | 162000 | 640 | 49766400000 | OD | | 43200 | https://sites.google.com/view/elizabethbondi/dataset | 148 | || 38-Cloud | 2018 | 17601 | 384 | 2595373056 | OD/SemSeg | | 16000 | https://www.kaggle.com/sorour/38cloud-cloud-segmentation-in-satellite-images | 149 | || 95-Cloud | 2020 | 34701 | 384 | 5116870656 | OD/SemSeg | | 18000 | https://www.kaggle.com/sorour/95cloud-cloud-segmentation-on-satellite-images/version/1 | 150 | || MTARSI (Muti-type Aircraft of Remote Sensing Images) | 2019 | 9385 | 256 | 615055360 | OD | 1 | 480 | https://zenodo.figshare.com/articles/dataset/Muti-type_Aircraft_of_Remote_Sensing_Images_MTARSI/11587569 | 151 | || FGSD (Fine-grained Ship Detection Dataset) | 2020 | 4736 | 930 | 4096166400 | OD | 43 | | ckyan@bupt.edu.cn | 152 | || SIMD (Satellite Imagery Multi-vehicles Dataset) | 2020 | 5000 | 1024 | 3932160000 | OD | 15 | | https://vision.seecs.edu.pk/simd-project/ | 153 | || FAIR1M (FinegrAined object recognItion in high-Resolution) | 2020 | 15000 | 5000 | | OD | 37 | | | 154 | || Humpback Whale Identification Challenge | 2018 | 25460 | 1050 | 16039800000 | OD/Class | | 700 | https://www.kaggle.com/c/whale-categorization-playground/rules | 155 | || NOAA Fisheries Steller Sea Lion Population Count | 2017 | 950 | 4900 | 15361500000 | OD/Class | 4 | 96000 | https://www.kaggle.com/c/noaa-fisheries-steller-sea-lion-population-count/data | 156 | || OVERHEAD MNIST | 2020 | 1000 | 28 | 784000 | Class | 9 | 17.50 | https://github.com/reveondivad/ov-mnist | 157 | || RSOC (Remote Sensing Object Counting) | 2020 | 3057 | 2500 | 3621481392 | OD/Class | 4 | | | 158 | || Olive tree | | 10 | 4000 | 120000000 | | | | | 159 | || PlanesNet | 2017 | 32000 | 20 | 12800000 | OD | | 390 | https://www.kaggle.com/rhammell/planesnet | 160 | || Iceberg Detection (Statoil/C-CORE Iceberg Classifier Challenge) | 2018 | 10028 | 75 | 56407500 | OD | | 290 | https://www.kaggle.com/c/statoil-iceberg-classifier-challenge/data?select=train.json.7z | 161 | || NaSC-TG2 | 2021 | 20000 | 256 | 1310720000 | Class | 10 | | http://www.msadc.cn/jszc_xzq/ | 162 | || SynthAer | 2018 | 765 | 1280 | 705024000 | SemSeg | 8 | 1000 | https://figshare.com/articles/dataset/SynthAer_-_a_synthetic_dataset_of_semantically_annotated_aerial_images/7083242/1 | 163 | || Synthinel-1 | 2020 | 2108 | 572 | 689703872 | SemSeg | 1 | 1000 | https://drive.google.com/open?id=1T2fO-VLfyQoQdy5C4at_uHkP0KBRZkit | 164 | || VALID | 2020 | 6690 | 1024 | 7014973440 | SemSeg/OD | 30 | 15700 | https://sites.google.com/view/valid-dataset | 165 | || Aerial Image Segmentation Dataset | 2013 | 80 | 512 | 20971520 | SemSeg | | 7 | http://jiangyeyuan.com/ASD/Aerial%20Image%20Segmentation%20Dataset.html | 166 | || Aeroscapes | 2018 | 3269 | 1280 | 3012710400 | SemSeg | 11 | 752 | https://drive.google.com/file/d/1W7yQtrGUnPQ1fB2dPb5wPjrLrlQi395g/view?usp=sharing | 167 | || MLRSNet | 2020 | 109161 | 256 | 7153975296 | Class | 46 | 1254 | https://md-datasets-cache-zipfiles-prod.s3.eu-west-1.amazonaws.com/7j9bv9vwsx-1.zip | 168 | || Kaggle Satellite buildings semantic segmentation | 2020 | 6038 | 256 | 395706368 | SemSeg | 1 | 878 | https://www.kaggle.com/hyyyrwang/buildings-dataset | 169 | || Kaggle Satellite Images of Water Bodies | 2020 | 2841 | 1000 | 2841000000 | SemSeg | 1 | 274 | https://www.kaggle.com/franciscoescobar/satellite-images-of-water-bodies | 170 | || Kaggle Massachusetts Buildings Dataset | 2020 | 151 | 1500 | 339750000 | SemSeg | 1 | 3000 | https://www.kaggle.com/balraj98/massachusetts-buildings-dataset | 171 | || Kaggle Semantic segmentation of aerial imagery | 2020 | 72 | 800 | 46080000 | SemSeg | 6 | 32 | https://www.kaggle.com/humansintheloop/semantic-segmentation-of-aerial-imagery?select=Semantic+segmentation+dataset | 172 | || USTC_SmokeRS | 2019 | 6225 | 256 | 407961600 | Class | 6 | 795 | http://complex.ustc.edu.cn/2019/0802/c18202a389656/page.htm | 173 | || ALSAT-2B | 2021 | 5518 | 256 | 192114688 | SR | | 70 | https://github.com/achrafdjerida/Alsat-2B | 174 | || ITCVD (Vehicle Detection) | 2018 | 173 | 5616 | 3637550592 | OD | 1 | 1300 | https://eostore.itc.utwente.nl:5001/fsdownload/zZYfgbB2X/ITCVD | 175 | || Zurich Summer Dataset | 2015 | 20 | 1000 | 20000000 | SemSeg | | 132 | https://sites.google.com/site/michelevolpiresearch/data/zurich-dataset | 176 | || ISPRS Aerial Image Segmentation Dataset | 2017 | 21 | 2500 | 131250000 | SemSeg | | 23800 | https://zenodo.org/record/1154821#.YIsmuY4zYdV | 177 | || EvLab-SS Dataset | 2017 | 60 | 4500 | 1215000000 | SemSeg | 11 | | http://earthvisionlab.whu.edu.cn/zm/SemanticSegmentation | 178 | || Gaofen Image Dataset (GID) | 2018 | 150 | 7200 | 7344000000 | Class | 15 | 47000 | https://x-ytong.github.io/project/GID.html | 179 | || CrowdAI Mapping Challenge | 2018 | 401755 | 300 | 36157950000 | SemSeg | 1 | 5335 | https://www.aicrowd.com/challenges/mapping-challenge#datasets | 180 | || Aerial Imagery for Roof Segmentation (AIRS) | 2019 | 1047 | 10000 | 1.047E+11 | SemSeg | 1 | 17600 | https://www.airs-dataset.com/ | 181 | || Massachusetts Roads Dataset | 2013 | 1171 | 1500 | 2634750000 | OD | | 7552 | https://www.cs.toronto.edu/~vmnih/data/ | 182 | || built-structure-count dataset | 2019 | 5364 | 512 | 1406140416 | OD | | 2000 | http://im.itu.edu.pk/deepcount/ | 183 | || OpenSARShip | | 11346 | | | | | | | 184 | || MAritime SATellite Imagery dataset (MASATI) | 2018 | 7389 | 512 | 1936982016 | Class | 7 | 2300 | https://www.iuii.ua.es/datasets/masati/ | 185 | || VisDrone | 2020 | 275437 | 1400 | 4.24173E+11 | OD | | 85000 | http://aiskyeye.com/ | 186 | || SatStereo | 2019 | 144 | | | | | 127000 | https://engineering.purdue.edu/RVL/Database/SatStereo/index.html | 187 | || Satellite Pose Estimation | 2020 | 15300 | 1920 | 35251200000 | Other | | 4600 | https://kelvins.esa.int/ | 188 | || Kaggle Satellite Images of Hurricane Damage | 2019 | 16000 | 128 | 262144000 | Class | | 64 | https://www.kaggle.com/kmader/satellite-images-of-hurricane-damage | 189 | || MidAir | 2019 | 420000 | 1024 | 4.40402E+11 | Other | | 1000000 | https://midair.ulg.ac.be/ | 190 | || QXS-SAROPT | 2021 | 40000 | 256 | 2621440000 | Other | | 2700 | https://github.com/yaoxu008/QXS-SAROPT | 191 | || oriEnted object detection using Aerial imaGery in real-worLd scEnarios (EAGLE) | 2020 | 8820 | 936 | 7727166720 | OD | | | | 192 | || Parking Lot Database (PKLot) | 2015 | 12417 | 1280 | 11443507200 | OD | 1 | 4600 | http://web.inf.ufpr.br/vri/databases/parking-lot-database/ | 193 | || Car Parking Lot Dataset (CARPK) | 2016 | 1448 | 1280 | 1334476800 | OD | 1 | 2000 | https://lafi.github.io/LPN/ | 194 | || Kaggle Find a Car Park | 2019 | 3262 | 1296 | 4109180544 | Class | 2 | 2750 | https://www.kaggle.com/daggysheep/find-a-car-park | 195 | || LEarning, VIsion and Remote (LEVIR) | 2016 | 21952 | 800 | 10536960000 | OD | 3 | | | 196 | || AeroRIT | 2019 | 1 | 3900 | 7842675 | SemSeg | | 1800 | https://drive.google.com/drive/folders/1yCMqa9uDC_CEGtbnxeWEQCTb-odC2r4c | 197 | || RIT-18 | 2018 | 1 | 9300 | 52080000 | SemSeg | | 1500 | https://github.com/rmkemker/RIT-18 | 198 | || UAVid | 2020 | 420 | 4000 | 3628800000 | SemSeg | | 5880 | https://uavid.nl/ | 199 | || Aerial Change Detection in Video Games (AICD) | 2018 | 1000 | 800 | 480000000 | CD | | 1700 | https://www.kaggle.com/kmader/aerial-change-detection-in-video-games | 200 | || Sentinel-2 Cloud Mask Catalogue | 2020 | 513 | 1022 | 535820292 | SemSeg/Class | | 15380 | https://zenodo.org/record/4172871#.YIvbA44zbIV | 201 | || LandCoverAI | 2020 | 41 | 9000 | 2979420000 | SemSeg | | 1400 | https://landcover.ai.linuxpolska.com/ | 202 | || Sentinel-2 Cloud Detection (ALCD) | 2019 | 38 | 1830 | 127258200 | OD | | 234 | https://zenodo.org/record/1460961#.YIvbAI4zbIV | 203 | || SPARCS | 2016 | 80 | 1000 | 80000000 | Other | | 1400 | https://www.usgs.gov/core-science-systems/nli/landsat/spatial-procedures-automated-removal-cloud-and-shadow-sparcs | 204 | || Sentinel-2 Multitemporal Cities Pairs | 2020 | 1520 | 600 | 547200000 | CD | | 10600 | https://zenodo.org/record/4280482#.YIvbH44zbIV | 205 | || Hi-UCD | 2020 | 1293 | 1024 | 1355808768 | CD | | | | 206 | || GTA-V SID | 2020 | 121 | 500 | 30250000 | SemSeg | | 100 | https://github.com/jiupinjia/gtav-sattellite-imagery-dataset | 207 | || transmission towers and power lines (TTPLA) | 2020 | 1100 | 3840 | 9123840000 | SemSeg | | 4200 | https://github.com/r3ab/ttpla_dataset | 208 | || AerialLanes18 | 2018 | 20 | 5616 | 420526080 | SemSeg | | | | 209 | || WHU-Hi | 2020 | 3 | 1217 | 1035251 | SemSeg | | 817 | http://rsidea.whu.edu.cn/resource_WHUHi_sharing.htm | 210 | || MOR-UAV | 2020 | 10948 | 1080 | 12769747200 | SemSeg | | | https://visionintelligence.github.io/Datasets.html | 211 | || Sentinel-2 Cloud Detection (WHUS2-CD+) | 2021 | 36 | 10980 | 4340174000 | CD | | 27800 | https://github.com/Neooolee/WHUS2-CD 212 | || CDD (season-varying) | 2018 | 10000 | 256 | | CD | | 2700 |https://drive.google.com/file/d/1GX656JqqOyBi_Ef0w65kDGVto-nHrNs9 213 | 214 | 215 | ## Contribution 216 | Any contribution in expanding this list are welcomed. You can introduce your own benchmark datasets, or other published ones to be added to this list. 217 | 218 | 219 | ## Citation 220 | In case you use this information in your studies, please consider citing 221 | ``` 222 | @article{schmitt2021there, 223 | title={There is no data like more data--current status of machine learning datasets in remote sensing}, 224 | author={Schmitt, Michael and Ahmadi, Seyed Ali and H{\"a}nsch, Ronny}, 225 | journal={arXiv preprint arXiv:2105.11726}, 226 | year={2021} 227 | } 228 | ``` 229 | 230 | # References and related links (Awesome of Awesomes) 231 | Here I acknowledge some useful lists and pages which can enrich your mind about Earth Observation and make us closer. 232 | 233 | 🛰️ List of [satellite image training datasets](https://awesomeopensource.com/project/chrieke/awesome-satellite-imagery-datasets) with annotations for computer vision and deep learning 234 | 235 | :star2: WOW! Take a look at Robin's awesome page. Almost everything for [deep learning in remote sensing](https://github.com/robmarkcole/satellite-image-deep-learning). 236 | 237 | A curated list of awesome tools, tutorials, code, helpful projects, links, stuff about [Earth Observation and Geospatial stuff](https://github.com/acgeospatial/awesome-earthobservation-code)! 238 | 239 | A curated list of awesome [tools, tutorials and APIs](https://github.com/Fernerkundung/awesome-sentinel) related to data from the Copernicus Sentinel Satellites. 240 | 241 | [Remote Sensing](https://github.com/attibalazs/awesome-remote-sensing) is very exciting. 242 | 243 | Long list of [geospatial analysis tools](https://github.com/sacridini/Awesome-Geospatial). 244 | 245 | [List](https://github.com/chrieke/awesome-geospatial-companies) of 500+ geospatial companies & interactive map. 246 | 247 | List of datasets, codes, and contests related to [remote sensing change detection](https://github.com/wenhwu/awesome-remote-sensing-change-detection). 248 | 249 | [Zhang Bin's list of data](https://zhangbin0917.github.io/2018/06/12/%E9%81%A5%E6%84%9F%E6%95%B0%E6%8D%AE%E9%9B%86/). 250 | 251 | The Top 112 [Super Resolution](https://awesomeopensource.com/projects/super-resolution) Open Source Projects. 252 | 253 | Super Resolution Methods [1](https://github.com/oneTaken/Awesome-SuperResolution), [2](https://github.com/idealo/image-super-resolution), [3](https://github.com/ChaofWang/Awesome-Super-Resolution), [4](https://github.com/ptkin/Awesome-Super-Resolution), [5](https://github.com/MIVRC/Image-Super-Resolution-Guide) 254 | 255 | [Kaggle datasets](https://www.kaggle.com/datasets) 256 | 257 | 258 | 259 | 260 | -------------------------------------------------------------------------------- /Scatter.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Seyed-Ali-Ahmadi/Awesome_Satellite_Benchmark_Datasets/8e3c77ddf5312684db58ee9e090dfc4f70568e3a/Scatter.jpg -------------------------------------------------------------------------------- /Schmitt_IGARSS2021.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Seyed-Ali-Ahmadi/Awesome_Satellite_Benchmark_Datasets/8e3c77ddf5312684db58ee9e090dfc4f70568e3a/Schmitt_IGARSS2021.png --------------------------------------------------------------------------------