Sponsor
The author(s) declare that financial support was received for the research and/or publication of this article. This work is supported by grants awarded by the National Science Foundation (#1951741 and #2230097).
Published In
Frontiers in Artificial Intelligence
Document Type
Article
Publication Date
5-12-2025
Subjects
Hydrological features
Abstract
High-resolution digital elevation models (HRDEMs) from LiDAR and InSAR technologies have significantly improved the accuracies of mapping hydrographic features such as river boundaries, streamlines, and waterbodies over large areas. However, drainage crossings that facilitate the passage of drainage flows beneath roads are not often represented in HRDEMs, resulting in erratic or distorted hydrographic features. At present, drainage crossing datasets are largely missing or available with variable quality. While previous studies have investigated basic convolutional neural network (CNN) models for drainage crossing characterization, it remains unclear if advanced deep learning models will improve the accuracy of drainage crossing classification. Although HRDEM-derived geomorphological features have been identified to enhance feature extraction in other hydrography applications, the contributions of these features to drainage crossing image classification have yet to be sufficiently investigated. This study develops advanced CNN models, EfficientNetV2, using four co-registered 1-meter resolution geomorphological data layers derived from HRDEMs for drainage crossing classification. These layers include positive openness (POS), geometric curvature, and two topographic position index (TPI) layers utilizing 3 × 3 and 21 × 21 cell windows. The findings reveal that the advanced CNN models with HRDEM, TPI (21 × 21), and a combination of HRDEM, POS, and TPI (21 × 21) improve classification accuracy in comparison to the baseline model by 3.39, 4.27, and 4.93%, respectively. The study culminates in explainable artificial intelligence (XAI) for evaluating those most critical image segments responsible for characterizing drainage crossings.
Rights
Copyright © 2025 Edidem, Xu, Li, Wu, Rekabdar and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Locate the Document
DOI
10.3389/frai.2025.1561281
Persistent Identifier
https://archives.pdx.edu/ds/psu/43650
Citation Details
Edidem, M., Xu, B., Li, R., Wu, D., Rekabdar, B., & Wang, G. (2025). Deep learning classification of drainage crossings based on high-resolution DEM-derived geomorphological information. Frontiers in Artificial Intelligence, 8.
