The team Sargassum Detector wins the Atos IT Challenge 2022 “To the Moon, to Mars, and to the Stars…”
We are delighted to announce that the 1st prize of this year’s Atos IT Challenge was won by the team Sargassum Detector, from the Université d’Orléans in France, represented by Clément Ortiz and Gauthier Ferec.
This app manages the large expanses of the Sargassum seaweed. It detects floating islands of algae with satellite images, then estimates on which coasts they will be stranded. The objective is to be able to warn local authorities in advance in order to promote a rapid collection of algae and a low impact on the coasts. The project also includes an app which allows users to participate in the collection of algae, by putting them in touch with partner associations or collection services. The team won €10,000.
Congratulations to the 3 winning teams for making it so far into the competition and to all the teams who have participated in the edition. It is an honor to highlight such talented minds every year!🌟
Discover the 3 Finalists
Sargassum Detector
Orléans University
What is Sargassum ? Sargassum is a type of seaweed, a brown algae, that lives on the ocean’s surface and floats in large masses. Typically found in the Sargasso Sea around Bermuda, it began showing up in large quantities in the Caribbean from 2011. What is the problem ? Since 2011, periodics invasions of sargassum…
Lumø
National University of Singapore
In the last few years, the world has seen an unprecedented damage from wildfires. A large portion of these damages can be attributed to climate change, which has proven to be a key player in escalating the risk and damage of wildfires. As trapped greenhouse gasses continue to push temperatures and increase the presence of…
RiverEye
Universidade de São Paulo - USP
Our proposal consists of a web platform to inspect and automatically detect vegetation areas that should be preserved alongside rivers using satellite imagery. Our proposal is based on state-of-the-art image classification, machine learning models, image pre and post-processing techniques to process the images with robust accuracy and still have an excellent performance. We propose a…
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