Building Damage Segmentation After Natural Disasters in Satellite Imagery with Mathematical Morphology and Convolutional Neural Networks

Antônio Neto, Daniel Dantas

2024

Abstract

In this study, our main motivation was to develop and optimize an image segmentation model capable of accurately assessing damage caused by natural disasters, a critical challenge today where the frequency and intensity of these events are increasing. In order to predict damage categories, including no damage, minor damage, and major damage, we compared several models and approaches. we explored and compared several models, focusing on the Unet architecture employing BDANet and other architectures such as ResNet18, VGG16, and ResNet50. Layers with mathematical morphology operations were applied as a filtering strategy. The results indicated that the Unet model with the BDANet backbone had the best performance, with an F1-score of 0.761, which increased to 0.799 after applying mathematical morphology operations.

Paper Citation

in Harvard Style

Neto A. and Dantas D. (2024). Building Damage Segmentation After Natural Disasters in Satellite Imagery with Mathematical Morphology and Convolutional Neural Networks. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 828-836. DOI: 10.5220/0012706300003690

in Bibtex Style

@conference{iceis24,
author={Antônio Neto and Daniel Dantas},
title={Building Damage Segmentation After Natural Disasters in Satellite Imagery with Mathematical Morphology and Convolutional Neural Networks},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={828-836},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012706300003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Building Damage Segmentation After Natural Disasters in Satellite Imagery with Mathematical Morphology and Convolutional Neural Networks
SN - 978-989-758-692-7
AU - Neto A.
AU - Dantas D.
PY - 2024
SP - 828
EP - 836
DO - 10.5220/0012706300003690
PB - SciTePress