Page 25 - Crowdsourcing AI and Machine Learning solutions for SDGs - ITU AI/ML Challenges 2024 Report
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Crowdsourcing AI and Machine Learning solutions for SDGs
Figure 12: 2 GNNet Workshop
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6.4 Smart Weather Station
This challenge aimed to develop a low-cost, low-power, smart weather station with no moving
parts based on tinyML. The team from CSEM developed "Aurora," a prototype that meets these
criteria and is currently undergoing further improvements. Aurora represents a significant step
forward in weather monitoring technology, providing reliable data collection while minimizing
maintenance and operational costs.
Figure 13: Aurora smart weather station
The ITU AI/ML Challenge continues to inspire innovative solutions that address real-world
problems. By showcasing these winning solutions, the challenge not only rewards the ingenuity
of participants but also contributes to the broader goals of technological advancement and
sustainable development.
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