Enhanced deep learning network for accurate digital elevation model generation from LiDAR data

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...

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Bibliographic Details
Published in:Automation in construction Vol. 167; p. 105708
Main Authors: Al-Fugara, A'kif, Almomani, Mohammad H., Zitar, Raed Abu, Alzahrani, Ahmed Ibrahim, Alwadain, Ayed, Abualigah, Laith
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2024
Elsevier
Subjects:
Computer Science
Digital terrain model (DTM)
Feature fusion
Hierarchical deep network
Object classification
Performance evaluation
Point cloud data
Performance evaluation
Hierarchical deep network
Digital terrain model (DTM)
Feature fusion
Object classification
Point cloud data
ISSN:0926-5805
Online Access:Get full text
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Summary: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
ISSN:0926-5805
DOI:10.1016/j.autcon.2024.105708