Advancing Physically Informed Autoencoders for DTM Generation

Abstract

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to digital terrain models (DTMs). DTMs are used in various fields, including environmental management, where they provide crucial topographical data to accurately model water flow and identify flood-prone areas. However, current DL-based methods require intensive data processing, limiting their efficiency and real-time use. To address these challenges, we have developed an innovative method that incorporates a physically informed autoencoder, embedding physical constraints to refine the extraction process. Our approach utilizes a normalized DSM (nDSM), which is updated by the autoencoder to enable DTM generation by defining the DTM as the difference between the DSM input and the updated nDSM. This approach reduces sensitivity to topographical variations, improving the model’s generalizability. Furthermore, our framework innovates by using subtractive skip connections instead of traditional concatenative ones, improving the network’s flexibility to adapt to terrain variations and significantly enhancing performance across diverse environments. Our novel approach demonstrates superior performance and adaptability compared to other versions of autoencoders across ten diverse datasets, including urban areas, mountainous regions, predominantly vegetation-covered landscapes, and a combination of these environments.