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MapAI:

Precision in Building Segmentation


Important Dates

  • Development dataset release: 21st of September, 2022
  • Participant's submission of results: 25th of November, 2022
  • Evaluation results for participants: 5th of December, 2022
  • Methods description paper submission: 15th of December, 2022

Introduction


MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificial Intelligence Research at the University of Agder (CAIR), the Norwegian Mapping Authority, AI:Hub, Norkart, and The Danish Agency for Data Supply and Infrastructure.

We propose two different tasks to segment buildings, where the first task can only utilize aerial images, while the second must use laser data (LiDAR) with or without aerial images.

Award


The prizes will be 1200 euros for first place, 500 euros for second place, and 300 Euros for third place.


  1. 1200 Euro
  2. 500 Euro
  3. 300 Euro


The winning team will be announced during the Northern Lights Deep Learning conference hosted on the 10-12. January 2023 at UiT The Arctic University of Norway.


We encourage you to register for the Northern Lights Deep Learning conference if you are based in the Nordic region.

The competition prizes are sponsored by the AI:Hub and The Norwegian Mapping Authority.

Registration

Register for the competition throughthis Github repository. More details can be found in the README.md.

The participants will be invited to submit to the following two tasks:

Task 1: Aerial Image Segmentation Task

The aerial image segmentation task aims to solve the segmentation of buildings only using aerial images. Segmentation using only aerial images is helpful for several scenarios, including disaster recovery in remote sensing images where laser data is unavailable. We ask the participants to develop machine learning models for generating accurate segmentation masks of buildings solely using aerial images.

Task 2: Laser Data Segmentation Task

The laser data segmentation task aims to solve the segmentation of buildings using laser data. Segmentation using laser data is helpful for urban planning or change detection scenarios, where precision is essential. We ask the participants to develop machine learning models for generating accurate segmentation masks of buildings using laser data with or without aerial images.


To compete for the prize money, both tasks are mandatory. However, submissions for only one sub-task are allowed but will not be eligible for winning any prizes.


Submission guidelines


For more details surrounding the competition, please visit NORA's competition page: