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.
As urban populations grow, more people are exposed to the benefits and hazards of city life. One challenge for cities is managing the risk of disasters in a constantly changing built environment. Buildings, roads, and critical infrastructure need to be mapped frequently, accurately, and in enough detail to represent assets important to every community. Knowing where and how assets are vulnerable to damage or disruption by natural hazards is key to disaster risk management (DRM).
The Global Facility for Disaster Reduction and Recovery (GFDRR) is a global partnership that provides knowledge, funding, and technical assistance towards achieving the vision of a world where resilient societies manage and adapt to ever-changing disaster and climate risk, and where the human and economic impact of disasters is reduced. Recognizing the significant potential of machine learning and geospatial technologies for DRM, GFDRR Labs is engaging the expert community through initiatives like this challenge to advance the creation of global public goods for improving disaster resilience.
The goal of this challenge is to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities.
Better performing and responsibly used AI systems can provide more accurate, faster, and lower-cost approaches to assessing risk and protecting lives and property.
There are 2 competition tracks:
- Semantic Segmentation track: Build models to identify building footprints from aerial imagery across diverse African cities.
- Responsible AI track: Apply an ethical lens to the design and use of AI systems for DRM. Segmentation track participants must also submit at least once to the Responsible AI track to qualify for the $12,000 in cash prizes.
March 16, 2020, 11:59 p.m. UTC
Semantic Segmentation Track
Evaluated on predicted labels, final rankings displayed on the private leaderboard.
Responsible AI Track
Evaluated from provided submissions, three winners selected by a panel of judges.
GFDRR employees are not eligible for prizes.
Image courtesy of OpenStreetMap.
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
Privately sharing code or data outside of teams is not permitted.
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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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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Prizes and conditions
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RECEIVING PRIZES. After verification of eligibility, if prizes are awarded, each Prize winner will receive the prize in the form of a check or electronic transfer made out to the Prize winner (if an individual, or to the individual Team members if a Team). Allow 30 days from final confirmation for Prize delivery. Any winners who are U.S. citizens will receive an IRS 1099 form in the amount of their prize at the appropriate time. Prize winners are responsible for any taxes, fees or other liability resulting from their receipt of a Prize.
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Data use and code sharing
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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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