Enhanced deep learning network for accurate digital elevation model generation from LiDAR data - Al-Fugara, A'kif; Almomani, Mohammad H.; Zitar, Raed Abu; Alzahrani, Ahmed Ibrahim; Alwadain, Ayed; Abualigah, Laith (Automation in construction, 2024)
This paper presents a unique hierarchical deep network to tackle the task of identifying and filtering non-ground objects from point cloud data. This task is essential in the building of digital terrain models (DTMs). The proposed network is based on a deep encoder-decoder architecture and includes efficient convolutional connections to improve the identification of items that are not on the ground. In this architectural framework, a block for extracting features is intentionally created to capture a wide range of characteristics at many levels. Additionally, a technique for fusing global and local data is included to further enhance the accuracy of detection. The effectiveness of the proposed deep network in accurately detecting objects is validated by a comparative study with current approaches, utilizing ISPRS data. This analysis demonstrates the superiority of the proposed network in terms of object detection accuracy. •Created a hierarchical deep network for precise non-ground object identification•Implemented efficient feature extraction and fusion techniques to enhance object detection•Addressed topographical challenges by extracting features at different scales•Demonstrated superior accuracy in complex environments, surpassing existing methods