Automated flood depth estimation on roadways

Kwame Ampofo; Megan Witherow; Alex Glandon; Monibor Rahman; Ahmed Temtam; Mecit Cetin; Khan M. Iftekharuddin

doi:10.1117/12.3028126

30 September 2024 Automated flood depth estimation on roadways

Kwame Ampofo, Megan Witherow, Alex Glandon, Monibor Rahman, Ahmed Temtam, Mecit Cetin, Khan M. Iftekharuddin

Author Affiliations +

Proceedings Volume 13136, Optics and Photonics for Information Processing XVIII; 1313603 (2024) https://doi.org/10.1117/12.3028126
Event: Optical Engineering + Applications, 2024, San Diego, California, United States

Abstract

Recurrent nuisance flooding is common across many parts of the globe and causes extensive challenges for drivers on the roadways. The prevailing monitoring methods for roadway flooding are costly and not automated or effective. The ubiquity of visual data from cameras and advancements in computing such as deep learning may offer cost-effective methods for automated flood depth estimation on roadways based on reference objects such as cars. However, flood depth estimation faces challenges due to the limited amount of data annotated with water levels and diverse scenes showing reference objects at various scales and perspectives. This study proposes a novel deep learning approach to automated flood depth estimation on roadways. Our proposed pipeline addresses variations in object perspective and scale. We have developed an innovative approach to generate and annotate flood images by manipulating existing image datasets of cars in various orientations and scales to simulate four floodwater levels for augmenting real flood images. Furthermore, we propose object scale normalization for our reference objects (cars) to improve water level predictions. The proposed model achieves an accuracy of 74.85% and F1 score of 74.32% for four water levels when tested with real flood data. The proposed approach substantially reduces the time and labor required for labeling datasets while addressing challenges in perspective/scale, offering a promising solution for image-based flood depth estimation.

Conference Presentation

Citation Download Citation

Kwame Ampofo, Megan Witherow, Alex Glandon, Monibor Rahman, Ahmed Temtam, Mecit Cetin, and Khan M. Iftekharuddin "Automated flood depth estimation on roadways", Proc. SPIE 13136, Optics and Photonics for Information Processing XVIII, 1313603 (30 September 2024); https://doi.org/10.1117/12.3028126

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