Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery

Gong, Li; Wang, Chao; Wu, Fan; Zhang, Jingfa; Hong, Zhang; Li, Qiang

doi:10.3390/rs8110887

Cited by 73 publications

(40 citation statements)

References 35 publications

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“…2020, 9, 238 2 of 16 improved [8,9]. Remote sensing images can be acquired quickly and can reflect the objective world comprehensively and intuitively, and they provide a new information source for the rapid recognition and assessment of earthquake damage [10,11]. There is a lot of research about disaster risk assessment based on remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

Chen

Yang

et al. 2020

IJGI

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The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method.

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Section: Introductionmentioning

confidence: 99%

A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

Chen

Yang

et al. 2020

IJGI

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“…Their findings suggest that texture information performs better for classifying collapsed buildings. Similar works use high-resolution SAR data, such as ALOS-2 PALSAR-2 in the case of the 2015 Nepal earthquake and TerraSAR-X datasets for the 2008 Wenchuan earthquake, to derive geometric and texture features, and evaluate several classic machine learning algorithms to classify damaged buildings from post-event remote sensing data [12,13]. On the other hand, frameworks that integrate pre-event vector information and post-event VHR remote sensing data together with advanced machine learning technologies have been proposed [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

et al. 2019

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This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.

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“…A variety of algorithms and parameters were tested on post-event aerial imagery for the earthquake in Christchurch, New Zealand, and the results showed that object-based approaches can produce better results than pixel-based approaches in earthquake damage detection using remotely sensed images [32]. Random forest (RF), SVM, and K-nearest neighbor (K-NN) classifiers were applied to classify collapsed and standing buildings with the post-event SAR image and the building footprint map [33].…”

Section: Introductionmentioning

confidence: 99%

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

Liu

Buchroithner

2018

Remote Sensing

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Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings.

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Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery

Cited by 73 publications

References 35 publications

A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning

Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

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