SpaceNet: Winning Implementations and New Imagery Release

Todd Stavish
Feb 24, 2017 · 3 min read

Recently, we announced the winners of the Rio de Janeiro building footprint extraction competition. In the announcement, we promised to open source the winning implementations and release satellite imagery over additional cities. As of today, the source code for the winning implementations can be found in the SpaceNetChallenge GitHub repository. Additionally, satellite imagery for four new cities is currently available via SpaceNet on AWS. Please continue to read for a summary of the implementation approaches and details on the new imagery.

Winning Implementations

The winning implementation was developed by Brazilian TopCoder wleite with a final footprint evaluation metric score of 0.255292. His implementation was custom and did not leverage deep learning frameworks. Generally, his approach was to use random forests with brute force polygon search. He followed a three step process:

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Figure 1: wleite implementation showing candidate polygons (in red)

The 2nd place entry was submitted by a Polish TopCoder Marek.cygan. His implementation produced a final score of 0.245420 by using the following workflow:

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Figure 2: marek.cygan implementation.

The 3rd place finalist was TopCoder qinhaifan from China with a final score of 0.227852. His entry is based on MNC (Multitask Network Cascade), an image segmentation approach presented at CVPR 2016. A TopCoder from Japan, Fugusuki, placed fourth with a score of 0.216199. His approach was developed using a convolutional neural network with the Keras framework and cluster code from the Object Detection on SpaceNet post. The fifth place finalist was TopCoder bic-user from Ukraine with a score of 0.168605. He also uses the Keras framework and references a StackExchange post as inspiration. His implementation performs a novel technique using scikit-image, scikit-learn, and shapely to post-process images by fitting rotated polygons.

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New Cities: Las Vegas, Paris, Shanghai, Khartoum

New Competition and Data

With regard to the new satellite imagery, we plan to host another building footprint extraction contest in the next 30 days. Please monitor TopCoder for the announcement as well. SpaceNet on AWS now contains imagery and building footprints for Las Vegas, Paris, Shanghai, and Khartoum. This release includes 3,800 km²of additional imagery with 181,619 footprints. We believe the new data is qualitatively better than the Rio release in a few important aspects:

Areas of Interest: Las Vegas, Paris, Shanghai, Kartoum

Conclusions

The contest implementations revealed that automated footprint extraction remains a challenging problem. We learned that pre- and post-processing techniques are as important as the choice of machine learning framework. We look forward to seeing how the introduction of greater geographic diversity with varying building standards and construction materials will impact footprint extraction quality.

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Thanks to Patrick Hagerty

Todd Stavish

Written by

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

Todd Stavish

Written by

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

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