Can you map building footprints from drone imagery? This semantic segmentation challenge leverages computer vision and data from OpenStreetMap to support disaster risk management efforts in cities across Africa.
This challenge created public goods that advance our state of practice in applying ML for understanding risk in urban Africa, including new ML performance benchmarks and in-depth explorations of how we responsibly create and deploy AI systems for disaster risk management.
As urban populations grow, more people are exposed to the benefits and hazards of city life. To manage the risk of natural disasters in this dynamic built environment, buildings need to be mapped frequently and in enough detail to help communities prepare and respond.
ML algorithms are becoming critical to scaling these mapping efforts by learning to use aerial imagery to automatically create building footprints. To power these models, this competition featured high-resolution drone imagery from 12 African cities and regions covering more than 700,000 buildings. This imagery was paired with building footprints annotated with the help of local OpenStreetMap communities.
Throughout the challenge, participants competed to build computer vision models that could most accurately map buildings on the ground. In parallel, the novel Responsible AI track gave participants an opportunity to engage with the ethical implications of applying ML in disaster risk contexts.
The top model achieved an impressive 0.86 Jaccard score (i.e. intersection over union) on the private test set! That translates to segmenting buildings with 92% precision (ground truth in predictions) and 93% recall (predictions in ground truth)!
This result represents a dramatic improvement over what relatively low-effort models would have produced before the challenge (0.5915 and 0.6235 Jaccard scores). What's more, the challenge datasets had enough diversity in locations and sensors to make the open-source winning solutions useful for a range of urban mapping projects across Africa.
Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience: Rules and Terms of Data Use
DrivenData Competition Rules
One account per participant
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Private sharing of code
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Public dissemination of entries
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Open licensing of winners
Winning solutions need to be made available under The MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT) in order to be eligible for recognition and prize money if offered.
Documentation for winning solutions
Winning solutions must be documented using the provided Winning Model Documentation Template in order to be eligible for recognition and prize money if offered.
The number of submissions per day is restricted to a fixed value on a per-competition basis. Any attempt to circumvent this limit will result in disqualification.
External data is not allowed unless otherwise noted explicitly on these competition pages. Participants agree to make no attempt to use additional data or data sources not provided on these competition pages.
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USER SUBMISSIONS MUST IN THEIR ENTIRETY COMPLY WITH ALL APPLICABLE FEDERAL, STATE, LOCAL, AND INTERNATIONAL LAWS AND REGULATIONS. WITHOUT LIMITING THE FOREGOING, IN ORDER TO BE ELIGIBLE, YOU WARRANT THAT (UNLESS OTHERWISE SPECIFIED IN THE PROBLEM STATEMENT) YOUR SUBMISSION FOR THIS COMPETITION:
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- Is suitable for a general audience.
- Does not contain any claims that are not and cannot be substantiated or that would be false and/or misleading to a reasonable consumer.
Selection of winners
This Competition is a challenge of skill and the final results are determined by evaluating a combination of quantitative and qualitative factors, as more fully described on the Competition Website. For that portion of the Competition evaluated quantitatively, the results will be determined solely by leaderboard ranking on the private leaderboard based on an objective statistical evaluation metric published on the Competition Website prior to commencement of the Competition (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the objective statistical evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data. For that portion of the Competition evaluated qualitatively, the results will be determined by a panel of judges, as more fully described on the Competition Website. As more fully described on the Competition Website, the Competition may have a quantitative winner, a qualitative winner, a combined quantitative and qualitative winner, or all three.
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- disqualify your Submission(s); or
- require that you remediate within one week all issues identified in your Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).
A tie between two or more valid and identically ranked submissions will be resolved in favor of the tied submission that was submitted first.
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Prizes and conditions
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As a condition of receipt of the prize, winner must deliver the algorithm's code and documentation to DrivenData. The source code must contain a description of resources required to build and run the algorithm. The accompanying documentation should be consistent with DrivenData's Winning Model Documentation Template, which will be provided to winners within 30 days of the end of the Competition Period.
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- represents that he/she/it has the unrestricted right to grant that license.
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- represents that he/she/it has the unrestricted right to grant that license.
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Except where prohibited by law, entry constitutes permission to use winners' names, hometowns and likenesses for online posting, and/or any advertising and publicity without additional compensation. Winners may also be required to sign and return a release of liability, declaration of eligibility and, where lawful, a publicity consent agreement, as conditions of receiving a prize. Failure to comply with the aforementioned conditions shall be grounds for forfeiture of a prize.
Data use and code sharing
DATA. "Data" means the Data or Datasets linked from the Competition Website for the purpose of use by Participants in the Competition. For the avoidance of doubt, Data is deemed for the purpose of these Competition Rules to include any prototype or executable code provided to Participants by DrivenData or via the Competition Website. Participants must use the Data only as permitted by these Competition Rules and any associated data use rules specified on the Competition Website.
Unless otherwise permitted by the terms of the Competition Website, Participants must use the Data solely for the purpose and duration of the Competition, including but not limited to reading and learning from the Data, analyzing the Data, modifying the Data and generally preparing your Submission and any underlying models and participating in forum discussions on the Competition Website. Participants agree to use suitable measures to prevent persons who have not formally agreed to these Competition Rules from gaining access to the Data and agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Data to any party not participating in the Competition. Participants agree to notify DrivenData immediately upon learning of any possible unauthorized transmission or unauthorized access of the Data and agree to work with DrivenData to rectify any unauthorized transmission. Participants agree that participation in the Competition shall not be construed as having or being granted a license (expressly, by implication, estoppel, or otherwise) under, or any right of ownership in, any of the Data.
EXTERNAL DATA. Unless otherwise expressly stated on the Competition Website, Participants must not use data other than the Data to develop and test their models and Submissions. Competition Sponsor reserves the right in its sole discretion to disqualify any Participant who Competition Sponsor discovers has undertaken or attempted to undertake the use of data other than the Data, or who uses the Data other than as permitted according to the Competition Website and in these Competition Rules, in the course of the Competition.
CODE SHARING. Participants are prohibited from privately sharing source or executable code developed in connection with or based upon the Data, and any such sharing is a breach of these Competition Rules and may result in disqualification. Participants are permitted to publicly share source or executable code developed in connection with or based upon the Data, or otherwise relevant to the Competition, provided that such sharing does not violate the intellectual property rights of any third party. By so sharing, the sharing Participant is thereby deemed to have licensed the shared code under the MIT License (an open source software license commonly described at http://opensource.org/licenses/MIT).
OPEN-SOURCE CODE. A Submission will be ineligible to win a prize if it was developed using code containing or depending on software licensed under an open source license:
- other than an Open Source Initiative-approved license (see http://opensource.org/licenses); or
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