On Cloud N: Cloud Cover Detection Challenge
Clouds obscure important ground-level features in satellite images, complicating their use in downstream applications. Build algorithms for cloud cover detection using a new cloud dataset and Microsoft's Planetary Computer! Competition hosted by Microsoft AI for Earth. #science
Clouds obscure important ground-level features in satellite images, complicating their use in downstream applications. Build algorithms for cloud cover detection using a new cloud dataset and Microsoft's Planetary Computer!
To obtain adequate analytical results from multispectral satellite imagery, it is essential to precisely detect clouds and mask them out from any Earth surface analysis, such as land cover classification. The models developed in this competition result in higher quality input data for those analytics.
— Hamed Alemohammad, Executive Director & Chief Data Scientist, Radiant Earth Foundation
Satellite imagery is critical for a wide variety of applications from disaster management and recovery, to agriculture, to military intelligence. Sentinel-2 multispectral imagery in particular has been used in applications like tracking erupting volcanos, mapping deforestation, and monitoring wildfires.
A major obstacle for all of these use cases is the presence of clouds, which introduce noise and inaccuracy in image-based models. As a result, clouds usually have to be identified and removed to most effectively use these satellite data sources.
The goal of this challenge was to most accurately detect cloud cover in multispectral satellite imagery from the Sentinel-2 mission. Algorithms submitted by participants were run on test imagery to produce cloud masks, which were compared against human cloud annotations. The availability of labeled data has been a major obstacle for cloud detection efforts, and this challenge featured a unique set of human-verified labels spanning imagery and cloud conditions across three continents.
Over the course of the competition, participants tested thousands of solutions and were able to significantly advance methods for cloud detection. The winning approach achieved a Jaccard score (intersection over union) of ~90%, detecting 91% of all cloudy pixels (recall) while keeping 94% of the pixels it labeled as clouds correct (precision). This represents a dramatic improvement over the existing thresholding methods provided with Sentinel-2 (65%) and the PyTorch Lightning benchmark (82%).
For example, the top models made gains on two key obstacles identified in the field: distinguishing clouds from other bright objects and detecting thin clouds.
Clouds vs. bright objects: Example chip from Buenos Aires, Argentina with a few very brightly illuminated plots of land (left). 1st place correctly selects only a small cloud in the top left corner (center).
Thin clouds: Example chip from Kikwit, Democratic Republic of the Congo that is completely covered by a thin layer of clouds (left). 1st place detects the thin layer and correctly predicts full cloud coverage (center).
See the results announcement for more information on the winning approaches. All of the prize-winning solutions from this competition, along with the dataset assembled for the challenge, are linked below and made available on for anyone to use and learn from.
On Cloud N: Cloud Cover Detection Challenge: 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.
Winner License Type: Open Source License
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 Winning Model Documentation Template provided to top-ranking participants 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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Data use and code sharing
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Disclaimer of Warranties
You understand that we cannot and do not guarantee or warrant that the Data or other files available for downloading from the internet or the Competition Website will be free of viruses or other destructive code. You are responsible for implementing sufficient procedures and checkpoints to satisfy your particular requirements for anti-virus protection and accuracy of data input and output, and for maintaining a means external to our site for any reconstruction of any lost data. RELEASED PARTIES WILL NOT BE LIABLE FOR ANY LOSS OR DAMAGE CAUSED BY A DISTRIBUTED DENIAL-OF-SERVICE ATTACK, VIRUSES OR OTHER TECHNOLOGICALLY HARMFUL MATERIAL THAT MAY INFECT YOUR COMPUTER EQUIPMENT, COMPUTER PROGRAMS, DATA OR OTHER PROPRIETARY MATERIAL DUE TO YOUR USE OF THE WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE OR TO YOUR DOWNLOADING OF ANY MATERIAL POSTED ON IT, OR ON ANY WEBSITE LINKED TO IT.
YOUR USE OF THE COMPETITION WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE AND RECEIPT OF ANY PRIZES IS AT YOUR OWN RISK. THE WEBSITE, ITS CONTENT AND ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE ARE PROVIDED ON AN “AS IS” AND “AS AVAILABLE” BASIS, WITHOUT ANY WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, BEYOND ANY NON-EXCLUDABLE GUARANTEES (DEFINED BELOW). NEITHER THE RELEASED PARTIES NOR ANY PERSON ASSOCIATED WITH RELEASED PARTIES MAKES ANY WARRANTY OR REPRESENTATION WITH RESPECT TO THE COMPLETENESS, SECURITY, RELIABILITY, QUALITY, ACCURACY OR AVAILABILITY OF THE COMPETITION WEBSITE. WITHOUT LIMITING THE FOREGOING, NEITHER RELEASED PARTIES NOR ANYONE ASSOCIATED WITH THE RELEASED PARTIES REPRESENTS OR WARRANTS THAT THE COMPETITION WEBSITE, ITS CONTENT OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL BE ACCURATE, RELIABLE, ERROR-FREE OR UNINTERRUPTED, THAT DEFECTS WILL BE CORRECTED, THAT OUR SITE OR THE SERVER THAT MAKES IT AVAILABLE ARE FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS OR THAT THE COMPETITION WEBSITE OR ANY SERVICES OR ITEMS OBTAINED THROUGH THE WEBSITE WILL OTHERWISE MEET YOUR NEEDS OR EXPECTATIONS.
NOTHING IN THESE COMPETITION RULES LIMITS, EXCLUDES, OR MODIFIES OR PURPORTS TO LIMIT, EXCLUDE, OR MODIFY ANY STATUTORY CONSUMER GUARANTEE OR ANY IMPLIED CONDITION OR WARRANTY, THE EXCLUSION OF WHICH FROM THESE COMPETITION RULES WOULD CONTRAVENE ANY STATUTE OR CAUSE ANY PART OF THESE TERMS TO BE VOID (“NON-EXCLUDABLE GUARANTEES”). SUBJECT TO THE LIMITATIONS IN THE PRECEDING SENTENCE AND TO THE MAXIMUM EXTENT PERMITTED BY ANY MANDATORY PROVISIONS OF APPLICABLE LAW, THE RELEASED PARTIES HEREBY DISCLAIM ALL WARRANTIES OF ANY KIND, WHETHER EXPRESS OR IMPLIED, STATUTORY OR OTHERWISE, INCLUDING BUT NOT LIMITED TO ANY WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND FITNESS FOR PARTICULAR PURPOSE.
THE FOREGOING DOES NOT AFFECT ANY WARRANTIES WHICH CANNOT BE EXCLUDED OR LIMITED UNDER APPLICABLE LAW.
Governing Law and Jurisdiction
DrivenData shall be the sole interpreter of these Competition Rules.
All matters relating to the Competition Rules or the Competition and any dispute or claim arising therefrom or related thereto (in each case, including non-contractual disputes or claims), shall be governed by and construed in accordance with the internal laws of the Commonwealth of Massachusetts without giving effect to any choice or conflict of law provision or rule (whether of the Commonwealth of Massachusetts or any other jurisdiction).
The invalidity or unenforceability of any provision of these Competition Rules shall not affect the validity or enforceability of any other provision. In the event that any provision is determined to be invalid or otherwise unenforceable or illegal, these Competition Rules shall otherwise remain in effect and be construed in accordance with their terms as if the invalid or illegal provision was not contained herein.