Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model
The algorithm uses a U-Net neural network with heteroscedastic regression to predict elevation corrections and quantify uncertainty for vertical ground surface displacements in agricultural areas. This method filters out noise and vegetation influence by retaining only low-uncertainty correction segments, providing a reliable way to assess subsidence risks.