Modeling population dynamics is of great importance for disaster response and recovery, and detection of buildings and urban areas are key to achieve so. We would like to pose the challenge of automatically detecting buildings from satellite images. This problem is formulated as a binary segmentation problem to localize all building polygons in each area. The evaluation will be based on the overlap of detected polygons with the ground truth.
The problem is a object segmentation problem. Each input is a satellite image. You must predict a set of polygons that describe the buildings in the 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 IoU (Intersection over Union) metric.
In SpaceNet Challenge, the metric for ranking entries is based on the Jaccard Index, also called the Intersection-over-Union (IoU). For more information read the full article on The DownlinQ.
and see below
The evaluation metric for this competition is an F1 score with the matching algorithm inspired by Algorithm 2 in the ILSVRC paper applied to the detection of building footprints. For each building there is a geospatially defined polygon label to represent the footprint of the building. A SpaceNet entry will generate polygons to represent proposed building footprints. Each proposed building footprint is either a “true positive” or a “false positive”.
There is at most one “true positive” per labeled polygon. The measure of proximity between labeled polygons and proposed polygons is the Jaccard similarity or the “Intersection over Union (IoU)”, defined as:
The value of IoU is between 0 and 1, where closer polygons have higher IoU values.
The F1 score is the harmonic mean of precision and recall, combining the accuracy in the precision measure and the completeness in the recall measure. For this competition, the number of true positives and false positives are aggregated over all of the test imagery and the F1 score is computed from the aggregated counts.
For example, suppose there are N polygon labels for building footprints that are considered ground truth and suppose there are M proposed polygons by an entry in the SpaceNet competition. Let tp denote the number of true positives of the M proposed polygons. The F1 score is calculated as follows:
The F1 score is between 0 and 1, where larger numbers are better scores.
To jump start the thought process and the implementation, you can take a look at some open source solutions below:
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: April 30, 2018, 6 p.m.
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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