DeepGlobe Land Cover Classification Challenge

Organized by jinghuang - Current server time: May 19, 2019, 6:43 a.m. UTC


May 1, 2018, 11:45 p.m. UTC


March 1, 2018, 6 p.m. UTC


Competition Ends
May 15, 2018, 11:59 p.m. UTC

DeepGlobe Land Cover Classification Challenge

Automatic categorization and segmentation of land cover is of great importance for sustainable development, autonomous agriculture, and urban planning. We would like to introduce the challenge of automatic classification of land cover types. This problem is defined as a multi-class segmentation task to detect areas of urban, agriculture, rangeland, forest, water, barren, and unknown. The evaluation will be based on the accuracy of the class labels.

For details about other DeepGlobe challenges and the workshop:

Please refer to the following paper if you participate in this challenge or use the dataset for your approach:

   author = {Demir, Ilke and Koperski, Krzysztof and Lindenbaum, David and Pang, Guan and Huang, Jing and Basu, Saikat and Hughes, Forest and Tuia, Devis and Raskar, Ramesh},
  title = {DeepGlobe 2018: A Challenge to Parse the Earth Through Satellite Images},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2018}


The problem is a multiclass classification problem. Each input is a satellite image. You must predict a mask for the input, i.e., a colored image of the same height and width as the input image,

There are 2 phases:

  • Phase 1: Development phase. We provide you with labeled training dataset and unlabeled validation dataset. You can submit your predictions on the validation data to CodaLab. You will receive feed-back on your performance on the validation set. The performance of your LAST submission will be displayed on the leaderboard.
  • Phase 2: Final phase. The unlabeled testing dataset will be released. You can submit your predictions on the testing dataset to CodaLab. Your performance on the test set will appear on the leaderboard when the organizers finish checking the submissions.

You only need to submit the prediction results (no code). However you need to submit your a short paper of 3 pages (+1 page for references) before May 1st to be eligible for the final phase. We will evaluate your methodology and your results in parallel. Paper submission is open at and please use the CVPR paper template.

The submissions are evaluated using the pixel-wise mean Intersection over Union (mIoU) metric.


Start: March 1, 2018, 6 p.m.

Description: Directly submit results on validation and/or test data; feed-back are provided on the validation set only. Do not forget to submit the short paper about your methodology before May 1st!


Start: May 1, 2018, 11:45 p.m.

Description: Please complete this phase using the Validation dataset. The results on the test set will be revealed when the organizers make them available.

Competition Ends

May 15, 2018, 11:59 p.m.

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# Username Score
1 Ali_iDST_Deep_Learning 0.5915
2 chenjun 0.5869
3 BingBing 0.5754