GeoAI Challenge

Everything happens somewhere – applying machine learning to geospatial analysis

The ITU GeoAI Challenge aims to provide a platform for collaboratively addressing real-world geospatial problems by applying artificial intelligence (AI) / machine learning (ML) to advance the United Nations Sustainable Development Goals (SDGs).

ITU provides a state-of-the-art, free-of-charge compute platform to participants of the Challenge who do not have adequate access to compute in their respective institutions. The compute platform will provide participants with access to:

  • Free GPUs and CPUs
  • Hosted Jupyter notebook server
  • Python kernel
  • Pre-installed machine learning packages, e.g. PyTorch and Tensorflow

Webinars, roundtables and hands-on sessions accompany the GeoAI Challenge.

Winners receive cash prizes and certificates.

The ITU GeoAI Challenge aims to provide a platform for collaboratively addressing real-world geospatial problems by applying artificial intelligence (AI) / machine learning (ML) to advance the United Nations Sustainable Development Goals (SDGs).

ITU provides a state-of-the-art, free-of-charge compute platform to participants of the Challenge who do not have adequate access to compute in their respective institutions. The compute platform will provide participants with access to:

  • Free GPUs and CPUs
  • Hosted Jupyter notebook server
  • Python kernel
  • Pre-installed machine learning packages, e.g. PyTorch and Tensorflow

Webinars, roundtables and hands-on sessions accompany the GeoAI Challenge.

Winners receive cash prizes and certificates.

The GeoAI Challenge features three problem statements

School mapping with big data

This challenge aims to explore ways to enhance current algorithms and the use of additional datasets and incorporate them to the current algorithms to enhance its scalability and accuracy.

Curated by UNICEF (United Nations International Children’s Emergency Fund)

Cropland mapping with satellite imagery

Develop accurate, cost-effective classification model for cropland extent and crop intensity maps using machine learning and artificial intelligence techniques.

Curated by FAO (Food and Agriculture Organization of the United Nations)

Location Mention Recognition (LMR)

This challenge aims at automatically extracting toponyms (places or location names) from the given text.

Curated by QCRI (Qatar Computing Research Institute), QU (Qatar University), and Qen Labs Inc.

GeoAI Challenge Timeline

February – June 2022

July – October 2022

November – December 2022

Curation Phase

Competition Phase

Evaluation Phase

GeoAI Challenge Timeline

Now – March 2022

Curation Phase

April – July 2022

Competition Phase

August – October 2022

Evaluation phase

Everything happens somewhere

Sky

GEOAI-DataSources-SKY-01

Ground

GEOAI-DataSources-GROUND-01
GEOAI-DataSources-GROUND-UserGen-03

Water + below surface level

GEOAI-DataSources-BELOWGROUND-01
Related sessions
28 June 2022
15:30 - 17:30 CEST, Geneva | 09:30-11:30 EST, New York | 21:30-23:30 CST, Beijing
Andrea Manara (ITU), Zhongxin Chen (FAO), Do-Hyung Kim (UNICEF)...
12 July 2022
16:00 - 17:30 CEST, Geneva | 22:00-23:30 CST, Beijing | 10:00-11:30 EST, New York
Andrea Manara (ITU), Maria Antonia Brovelli (Politecnico di Milano), Begüm Demir (Technische Universität Berlin)
21 September 2022
16:00 - 17:30 CEST, Geneva | 10:00-11:30 EDT, New York | 22:00-23:30 CST, Beijing
Andrea Manara (ITU), Maria Antonia Brovelli (Politecnico di Milano), Zhongxin Chen (FAO)...
21 November 2022
16:00 - 17:00 CET Geneva | 10:00-11:00 EST, New York | 22:00-23:00 CST, Beijing
Maria Antonia Brovelli (Politecnico di Milano), Devis Tuia (Ecole Polytechnique Fédérale de Lausanne)

Benefits

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Crowdsourcing multiple solutions for high-impact problems that could improve real production AI/ML systems

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Increasing awareness about the problem domain and your work either in research or business

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Access to a growing and highly skilled talent pool of AI researchers, students, and professionals interested in the same problems as your group for further collaborations or hiring

Hosts of problem statements

unicef
FAO
UN-Open-GIS-Initiative
QCRI-logo
qen-labs-logo-250px
QU-logo

Technical partners

AIIA-300x
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Sponsorship Inquiries​