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Open AccessArticle

Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression

by Tianyu Ci 1,2, Zhen Liu 3,* and Ying Wang 1
1
Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
2
College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
3
Faculty of Education, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2858; https://doi.org/10.3390/rs11232858
Received: 14 October 2019 / Revised: 24 November 2019 / Accepted: 26 November 2019 / Published: 1 December 2019
(This article belongs to the Special Issue Remote Sensing of Natural Hazards)
We propose a new convolutional neural networks method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. The ordinal regression model and a deep learning algorithm are incorporated to make full use of the information to improve the accuracy of the assessment. A new loss function was introduced in this paper to combine convolutional neural networks and ordinal regression. Assessing the level of damage to buildings can be considered as equivalent to predicting the ordered labels of buildings to be assessed. In the existing research, the problem has usually been simplified as a problem of pure classification to be further studied and discussed, which ignores the ordinal relationship between different levels of damage, resulting in a waste of information. Data accumulated throughout history are used to build network models for assessing the level of damage, and models for assessing levels of damage to buildings based on deep learning are described in detail, including model construction, implementation methods, and the selection of hyperparameters, and verification is conducted by experiments. When categorizing the damage to buildings into four types, we apply the method proposed in this paper to aerial images acquired from the 2014 Ludian earthquake and achieve an overall accuracy of 77.39%; when categorizing damage to buildings into two types, the overall accuracy of the model is 93.95%, exceeding such values in similar types of theories and methods. View Full-Text
Keywords: earthquake; rapid mapping; damage assessment; deep learning; convolutional neural networks; ordinal regression; aerial image earthquake; rapid mapping; damage assessment; deep learning; convolutional neural networks; ordinal regression; aerial image
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MDPI and ACS Style

Ci, T.; Liu, Z.; Wang, Y. Assessment of the Degree of Building Damage Caused by Disaster Using Convolutional Neural Networks in Combination with Ordinal Regression. Remote Sens. 2019, 11, 2858.

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