Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

Wang, Jianhua; Qin, Qiming; Zhao, Jianghua; Ye, Xin; Xiao, Feng; Qin, Xuebin; Yang, Xiucheng

doi:10.3390/rs70404948

Cited by 20 publications

(15 citation statements)

References 28 publications

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“…During recent years, the development of remote sensing satellites has allowed researchers to widely employ multitemporal image datasets with high spectral, spatial, and temporal resolutions [16,17]. Such techniques play a key role in environmental monitoring in many applications, especially in damage assessment [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

et al. 2021

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Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%.

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Section: Introductionmentioning

confidence: 99%

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

et al. 2021

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“…For road damage detection, preearthquake road vector map can be overlaid on postearthquake imagery to discover the roads from the imagery as well as their damage [27,28]. Road center line extraction method can be used to get roads from postearthquake imagery without aid of road vector data [29]. However, few of them mentioned such case that road damage is occluded by trees and the relationship between road damage and the surrounding geological hazards, such as landslides.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images

Zhu

et al. 2020

Advances in Civil Engineering

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On 8 August 2017, Jiuzhaigou earthquake, magnitude 7.0, hit northern Sichuan, China. As the earthquake-stricken area is located in the mountainous region with forest and low residential density, the main damage is to vegetation and roads by earthquake-triggered landslides. In this study, the core area of Jiuzhaigou natural reserve, one of the highest seismic intensity zones, is selected. The landslides are extracted by examining vegetation changes from the preearthquake and postearthquake images using the Normalized Difference Vegetation Index (NDVI) and are verified by slope. As most road damage in the mountainous region could be attributed to the landslides nearby, the impacts of landslide on road are studied based on spatial analysis and are used to infer occluded road damage. Then, a knowledge-based method for postearthquake road detection and road capacity assessment from preearthquake road data and postearthquake high-resolution remote sensing imagery is proposed, as well as the quantitative road capacity assessment indicators to classify the road grades. This method is evaluated using the Beijing-2 (BJ-2) satellite images over the study area acquired on 28 April and 9 August. Compared with visual interpretation results, the extraction accuracy reached 90% for landslides and 85% for postearthquake roads, indicating that the approaches are effective and promising for quick response to devastating earthquake in similar circumstances.

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“…For geometric features, a road appears as an image object with an obvious linear shape or two boundary lines in the remote sensing images. Some scholars extract road regions in the images according to geometric features of roads (in the form of lines or ribbons) and spectral features (spectrum of typical surface material) [6][7][8][9][10]; (2) methods based on multi-scale features. Road regions are identified according to the spectral and geometric characteristics of roads on images at different scales.…”

Section: Introductionmentioning

confidence: 99%

Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

Liu

Qin

et al. 2017

IJGI

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Road information as a type of basic geographic information is very important for services such as city planning and traffic navigation, as such there is an urgent need for updating road information in a timely manner. Scholars have proposed various methods of extracting roads from remote sensing images, but most of them are not applicable to rural roads with diverse materials, large curvature changes, and a severe shelter problem. In view of these problems, we propose a road extraction method based on geometric feature inference. In this method, we make full use of the linear characteristics of roads, and construct a geometric knowledge base of rural roads using information on selected sample road segments. Based on the knowledge base, we identify the parallel line pairs in images, and further conduct grouping and connection instructed by knowledge reasoning, and finally obtain complete rural roads. The case study in Xiangtan City of China's Hunan Province validates the performance of the proposed method.

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Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image

Cited by 20 publications

References 28 publications

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery

Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images

Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference

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