| Summary: | Urban flood risk mitigation requires fine-scale near-real-time precipitation observations that are challenging to obtain from traditional monitoring networks. Novel data and computational techniques offer a valuable potential source of information. This study explores an unprecedented, device-independent, artificial intelligence-based system for opportunistic rainfall monitoring through deep learning models that detect rainfall presence and estimate quasi-instantaneous intensity from single pictures. Preliminary results demonstrate the models’ ability to detect a significant meteorological state corroborating the potential of non-dedicated sensors for hydrometeorological monitoring in urban areas and data-scarce regions. Future research will involve further experiments and crowdsourcing, to improve accuracy and promote public resilience.
|
|---|