When it comes to Earth observations in AI for disaster risk reduction, is it feast or famine? A topical review

Abstract

Earth observations (EOs) have successfully been used to train artificial intelligence (AI)-based models in the field of disaster risk reduction (DRR) contributing to tools such as disaster early warning systems. Given the number of in situ and remote (e.g. radiosonde/satellite) monitoring devices, there is a common perception that there are no limits to the availability of EO for immediate use in such AI-based models. However, a mere fraction of EO is actually being used in this way. This topical review draws on use cases, workshop presentations, literature, and consultation with experts from key institutes to explore reasons for this discrepancy. Specifically, it evaluates the types of EO needed to train AI-based models for DRR applications and identifies the main characteristics, possible challenges, and innovative solutions for EO. Finally, it suggests ways to make EO more user ready and to facilitate its uptake in AI for DRR and beyond.

Keywords

• Earth observation
• remote sensing
• disaster risk reduction
• artificial intelligence
• machine learning