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Comparison of DEM Generated from UAV Images and ICESat-1datasetICESat-1 Elevation Datasets with an Assessment of the Cartographic Potential of UAV-Based Sensor Datasets Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Disaster Risk Mapping from Aerial Imagery Using Deep Learning Techniques

Authors : Amit Kumar Jena, Sai Sudhamsa Potru, Deepak Raghavan Balaji, Abhinayana Madu, Kuldeep Chaurasia

Published in: Proceedings of UASG 2021: Wings 4 Sustainability

Publisher: Springer International Publishing

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Abstract

In regions prone to natural disasters, the buildings must follow specific construction standards to avoid demolition. One of the factors that predict the risk of damage is the roof material. This paper investigates the performance of various deep convolutional neural network architectures to classify buildings based on roof material from aerial drone imagery. We also propose a method that is an ensemble of ResNet, ResNeXt, and EfficientNet variants of convolutional neural networks, which performed the best in our experiments. We obtained a log loss value as low as 0.4373 using the proposed method. Therefore, the proposed method can be used to perform an accurate classification of roof material using aerial drone imagery.

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previous chapter Role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges
next chapter High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2
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Metadata
Title
Disaster Risk Mapping from Aerial Imagery Using Deep Learning Techniques
Authors
Amit Kumar Jena
Sai Sudhamsa Potru
Deepak Raghavan Balaji
Abhinayana Madu
Kuldeep Chaurasia
Copyright Year
2023
Book
Proceedings of UASG 2021: Wings 4 Sustainability

Print ISBN: 978-3-031-19308-8

Electronic ISBN: 978-3-031-19309-5

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-19309-5_23