Towards Smart Airports: Use of Segmentation and Classification Algorithms to Map Airdromes Features using High-Spatial Resolution LiDAR and Photogrammetry

This study introduces and utilizes the TTI-HydroMet dataset, including rainfall data from 23 rain gauge stations, stage observations from a hydrological gauge, and quantitative precipitation estimates at high temporal and spatial resolution. The paper's main message is that radar-based Machine Learning approaches can be a viable alternative for short-term stage forecasting in regions lacking rain gauge networks, supported by a reproducible case study.