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.
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:
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 https://cmt3.research.microsoft.com/DeepGlobe2018 and please use the CVPR paper template.
The submissions are evaluated using the mean average precision metric. This metric computes the balanced accuracy (that is the average of the per class accuracies). The metric is re-scaled linearly between 0 and 1, 0 corresponding to a random guess and 1 to perfect predictions.
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, midnight
Description: The last submission from the previous phase is automatically cloned and used to compute the final score. The results on the test set will be revealed when the organizers make them available.
May 15, 2018, 11:59 p.m.
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