SatImNet (Satellite Image Net) collection: a compilation of seven open-source training sets targeting various EO applications.

The structuring and homogenization of the SatImNet data is described in the paper https://arxiv.org/abs/2006.10623

- DOTA: A Large-scale Dataset for Object DeTection in Aerial Images, used to develop and evaluate object detectors in aerial images [1];
- xView: contains proprietary images (DigitalGlobe's WorldView-3) from complex scenes around the world, annotated using bounding boxes [2];
- Airbus-ship: combines Airbus proprietary data with highly-trained analysts to support the maritime industry and monitoring services [3];
- Clouds-s2-taiwan: contains Sentinel-2 True Colour Images (TCI) and corresponding cloud masks [4], covering the area of Taiwan;
- Inria Aerial Image Labeling: comprises aerial ortho-rectified colour imagery with a spatial resolution of 0.3 m and ground truth data for two semantic classes (building and no building) [5];
- BigEarthNet-v1.0: a large-scale Sentinel-2 benchmark archive consisting of Sentinel-2 image patches (L2A), annotated by the multiple land-cover classes that were provided from the CORINE Land Cover database of the year 2018 [6];
- EuroSAT: consists of numerous Sentinel-2 (L1C) patches provided in two editions, one with 13 spectral bands and another one with the basic RGB bands; all the image patches refer to 10 classes and are used to address the challenge of land use and land cover classification [7].


[1] Xia, G.S.; Bai, X.; Ding, J.; Zhu, Z.; Belongie, S.; Luo, J.; Datcu, M.; Pelillo, M.; Zhang, L. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
[2] Lam, D.; Kuzma, R.; McGee, K.; Dooley, S.; Laielli, M.; Klaric, M.; Bulatov, Y.; McCord, B. xView: Objects in Context in Overhead Imagery, 2018, [arXiv:cs.CV/1802.07856].
[3] Airbus-Kaggle. Airbus Ship Detection Challenge, 2018. accessed 28 February 2020, https://www.kaggle.com/c/airbus-ship-detection.
[4] Liu, C.C.; Zhang, Y.C.; Chen, P.Y.; Lai, C.C.; Chen, Y.H.; Cheng, J.H.; Ko, M.H. Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation. Remote Sensing 2019, 11. doi:10.3390/rs11020119.
[5] Maggiori, E.; Tarabalka, Y.; Charpiat, G.; Alliez, P. Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. IEEE International Symposium on Geoscience and Remote Sensing (IGARSS), 2017.
[6] Sumbul, G.; Charfuelan, M.; Demir, B.; Markl, V. Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 5901–5904. doi:10.1109/IGARSS.2019.8900532.
[7] Helber, P.; Bischke, B.; Dengel, A.; Borth, D. EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 2217–2226.


License:
- DOTA: -
- xView: The dataset is licensed under the Attribution-Noncommercial-ShareAlike 4.0 International (CCBY-NC-SA 4.0) license.
- Airbus-ship: -
- Clouds-s2-taiwan: The dataset is licensed under the Creative Commons Attribution (CC BY) license.
- Inria Aerial Image Labeling: Consent has been given by Yuliya Tarabalka on 15/11/2020.
- BigEarthNet-v1.0: The dataset can be obtained from http://bigearth.net/ and is licensed under the Community Data License Agreement – Permissive, Version 1.0 at http://bigearth.net/downloads/documents/License.pdf
- EuroSAT: -