Measuring urban waterlogging depths from video images based on reference objects

Camera surveillance systems can record urban waterlogging processes. Objects with regular shapes and fixed sizes captured by the camera can be utilized to calculate urban waterlogging depths based on geometric principles. In this study, we propose a machine learning-based method to measure urban waterlogging depths using wheels and traffic buckets captured in video images as reference objects. This method is validated through laboratory experiments and observed data. The results demonstrate that: (1) the urban waterlogging depths calculated using urban reference objects show high consistency with the observed water level data; (2) in the laboratory scenario, the probability of error within 3 cm for measurements based on the hub, tire, and traffic bucket are 99.07%, 99.38%, and 81.55%, respectively; (3) in the real-world scenario, the probability of error within 3 cm for measurements based on car hubs and pickup truck hubs are 97.30% and 95.14%, respectively. In conclusion, urban waterlogging depths can be accurately measured using reference objects with regular shapes. The proposed method can help obtain waterlogging data with higher temporal and spatial resolution at lower economic costs, which is of great significance for urban flood control.