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

Xu, Zhiqiang; Chen, Yumin; Yang, Fan; Chu, Tianyou; Zhou, Hongyan

doi:10.3390/ijgi9040238

Cited by 10 publications

(3 citation statements)

References 42 publications

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“…Xu et al [106] proposed a post-earthquake multi-scene recognition (PEMSR) model based on a single shot multibox detector (SSD) approach. TL combined with data enhancement and balancing strategies are used in the model to solve the problem of insufficient and unbalanced data in the original dataset.…”

Section: Transfer Learningmentioning

confidence: 99%

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Jia,

2023

Remote Sensing

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Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields.

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Section: Transfer Learningmentioning

confidence: 99%

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Jia,

2023

Remote Sensing

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“…More recently, Xu et al studied the post-earthquake scene classification task using three deep learning methods. These methods included a Single Shot MultiBox Detector (SSD), post-earthquake multiple scene recognition (PEMSR) based on transfer learning from SSD, and Histogram of Oriented Gradient along with Support Vector Machine (HOG+SVM) [13]. Within the proposed method, the aerial images were initially classified into six classes, including landslide, houses, ruins, trees, clogged, and ponding.…”

Section: Studies Used 2d Images For Detection and Classificationmentioning

confidence: 99%

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

Yi-jun

Mohammadi

Wood

2020

Drones

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Efficient and rapid data collection techniques are necessary to obtain transitory information in the aftermath of natural hazards, which is not only useful for post-event management and planning, but also for post-event structural damage assessment. Aerial imaging from unpiloted (gender-neutral, but also known as unmanned) aerial systems (UASs) or drones permits highly detailed site characterization, in particular in the aftermath of extreme events with minimal ground support, to document current conditions of the region of interest. However, aerial imaging results in a massive amount of data in the form of two-dimensional (2D) orthomosaic images and three-dimensional (3D) point clouds. Both types of datasets require effective and efficient data processing workflows to identify various damage states of structures. This manuscript aims to introduce two deep learning models based on both 2D and 3D convolutional neural networks to process the orthomosaic images and point clouds, for post windstorm classification. In detail, 2D convolutional neural networks (2D CNN) are developed based on transfer learning from two well-known networks AlexNet and VGGNet. In contrast, a 3D fully convolutional network (3DFCN) with skip connections was developed and trained based on the available point cloud data. Within this study, the datasets were created based on data from the aftermath of Hurricanes Harvey (Texas) and Maria (Puerto Rico). The developed 2DCNN and 3DFCN models were compared quantitatively based on the performance measures, and it was observed that the 3DFCN was more robust in detecting the various classes. This demonstrates the value and importance of 3D datasets, particularly the depth information, to distinguish between instances that represent different damage states in structures.

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“…Compared with traditional methods, deep learning methods can automatically learn features through convolution operations with the help of deep learning frameworks and replace manual features recognition with hierarchical feature extractions [27][28][29]. However, the methods based on Convolutional Neural Networks (CNN) designed for landslides extraction have only just begun.…”

Section: Introductionmentioning

confidence: 99%

A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

Liu

Wei

Wang

et al. 2021

IJGI

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Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

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

Cited by 10 publications

References 42 publications

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities

Deep Learning Classification of 2D Orthomosaic Images and 3D Point Clouds for Post-Event Structural Damage Assessment

A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

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