ii2030 Challenge: AI for Earth Observation
Identifying sustainable business models for open machine learning ready Earth observation repositories –
When Gedeon Jean first realized the power of Earth observation (EO) data to detect environmental changes, he was mesmerized. As a Machine Learning Research Engineer, he saw the potential of combining machine learning (ML) and EO to develop diverse predictive applications for Rwanda, his native country. Rwanda is increasingly experiencing natural disasters due to climate change, including landslides, floods, and earthquakes, which take a socio-economic toll on an already vulnerable population.
Gedeon decided to write a ML algorithm that could find patterns in remote sensing data to forewarn when flooding could occur. Flood prediction models map flood-prone areas and can improve the accuracy of early warning systems to minimize the destruction of natural habitats, food sources, infrastructure, and loss of human lives, which is associated with floods. But building the application proved to be trickier as he struggled to find high-quality labeled training data that were ML-ready, on which he could train his model. In addition, the lack of training data relevant to his area of interest is known to produce biased or incorrect results. Training data, the building block for ML algorithms, needs to capture the geographical diversity of real-world data to help the model identify patterns more accurately.
