Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience
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. #disasters
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.
Quick Facts
Participants
No. of Entries
Prize
Winner
qubvel
1st Place
Overview
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. UTCSubmissions close. |
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Semantic Segmentation TrackEvaluated on predicted labels, final rankings displayed on the private leaderboard. |
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Responsible AI TrackEvaluated from provided submissions, three winners selected by a panel of judges. |
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GFDRR employees are not eligible for prizes.
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Image courtesy of OpenStreetMap.
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Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience: Rules and Terms of Data Use
DrivenData Competition Rules
GUIDELINES
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Open licensing of winners
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Documentation for winning solutions
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Submission limits
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External data
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FULL RULES
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Registration
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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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Data use and code sharing
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