Application of Advanced Deep Learning Models for Flood Image Processing and Semantic Segmentation
EGU General Assembly 2024, 2024
Abstract
In developing Version 2.0 of our Flood Image Classifier, we underscore the significant role of Convolutional Neural Networks (CNNs), mainly Faster R-CNN and YOLOv3, in detecting and segmenting flood-related labels in images. Additionally, our research delves into the potential of Vision Transformers (ViT) for advanced object detection and image classification for flood-related images extracted for the USGS river cameras. Transformer methods offer improved predictions of flood depth and inundation areas, marking a substantial step forward in flood vision technology. The integration of advanced image processing techniques, the enhancement of CNN capabilities, and the incorporation of cutting-edge detection and classification models are pivotal in developing a comprehensive, real-time flood monitoring system. This system is designed to equip frontline decision-makers and emergency responders with essential insights into flooding conditions, thereby significantly contributing to disaster management and response through the innovative use of our flood image classifier, Version 2.0.
Cite this work
Sai Praneeth Dulam, Vidya Samadi, and Carlos Toxtli-Hernández. 2024. Application of Advanced Deep Learning Models for Flood Image Processing and Semantic Segmentation. EGU General Assembly 2024. https://doi.org/10.5194/egusphere-egu24-22491
@inproceedings{Dulam_2025,
title = {Application of Advanced Deep Learning Models for Flood Image Processing and Semantic Segmentation},
url = {http://dx.doi.org/10.5194/egusphere-egu24-22491},
doi = {10.5194/egusphere-egu24-22491},
publisher = {Copernicus GmbH},
author = {Dulam, Sai Praneeth and Samadi, Vidya and Toxtli-Hernández, Carlos},
year = {2024},
month = Jan,
booktitle = {EGU General Assembly 2024}
}Related
