Deep Learning for Automatic Building Damage Assessment: Application in Post-Disaster Scenarios Using Uav Data

Calantropio, Alessio; Chiabrando, Filiberto; Codastefano, Marco; Bourke, E.

doi:10.5194/isprs-annals-v-1-2021-113-2021

Cited by 22 publications

(8 citation statements)

References 17 publications

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“…The authors proposed a new CNN method in combination with ordinal regression aiming at assessing the degree of building damage caused by earthquakes with aerial imagery. Callantropio et al [30] used a DL tool to assess and map the damages that occurred in Pescara del Tronto village.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models

Hacıefendioğlu,

Başağa,

Kahya

et al. 2024

Buildings

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This study focuses on the identification of collapsed buildings in satellite images after earthquakes through deep learning-based image segmentation models. The performance of four different architectures, namely U-Net, LinkNet, FPN, and PSPNet, was evaluated using various performance metrics, such as accuracy, precision, recall, F1 score, specificity, AUC, and IoU. The study used satellite images taken from the area located in the south and southeast of Türkiye covering the eleven provinces which are most affected by the Mw 7.7 Pazarcık (Kahramanmaraş) and Mw 7.6 Elbistan (Kahramanmaraş) earthquakes. The results indicated that FPN and U-Net were the best-performing models depending on the performance metric of interest. FPN achieved the highest accuracy and specificity scores, as well as the best precision score, while U-Net achieved the best recall and F1 score values, as well as the best AUC and IoU scores. The training and validation accuracy and loss curves were analyzed, and the results indicated that all four models achieved an accuracy value of over 96%. The FPN model outperformed the others in terms of accurately segmenting images while maintaining a low loss value. This study provides insights into the potential of deep learning-based image segmentation models in disaster management and can be useful for future research in this field.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models

Hacıefendioğlu,

Başağa,

Kahya

et al. 2024

Buildings

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“…[5][6][7] Due to its flexible deployment, low-altitude imaging, higher resolution and lessened obstruction by clouds, UAV imaging has increasingly become the preferred method of sensing for automated building damage assessment, especially in the context of disaster response. 3,[8][9][10][11] The capacity to cover large land areas in high resolution results in large data collections and further introduces the challenge of having to evaluate the same data in relatively short amount of time. Manual evaluation of the data would take a lot of time, without being able to provide an objective insight into data statistics and comprehend the complete extent of the disaster in various regions.…”

Section: Related Workmentioning

confidence: 99%

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Hatic,

Rauhut,

Polushko

et al. 2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Natural disasters have devastating effects on communities, necessitating swift and accurate damage assessment. Manual assessment methods are time-consuming and costly, emphasizing the significance of semiautomatic approaches employing remote sensing and drone data. However, current datasets primarily focus on Western countries’ infrastructure, lacking information on damaged buildings in other regions specifically Africa. To bridge this gap, we present the EDDA dataset, comprising orthorectified mosaic images of rural and urban areas in Mozambique affected by Cyclone Idai. In this study, we utilize the EDDA dataset to evaluate the applicability of the lightweight object detection model YOLOv7 for efficient and timely disaster response. Testing the dataset with YOLOv7, assessed the datasets suitability for the task of building damage object detection under different class compositions and training data preprocessing configurations. Results showed promising results when utilizing Yolov7 as a building detector regardless of damage class, as a region proposal network, while building damage recognition requires additional research.

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“…They analyzed the UAV and satellite applications in earthquake damage investigations, such as building damage, and compared the fixed-wing and multirotor UAVs' applicable scenarios. Calantropio et al (2021) conducted deep learning on the image data of buildings acquired by UAVs as the main investigation method during the earthquake in central Italy in 2016. Moreover, Franke et al (2018) adopted digital imaging from small UAVs and other means to conduct phased surveys and record the 2016 earthquake-induced landslide disaster in central Italy.…”

Section: Introductionmentioning

confidence: 99%

Earthquake-induced building damage recognition from unmanned aerial vehicle remote sensing using scale-invariant feature transform characteristics and support vector machine classification

et al. 2023

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Building damage is the main cause of casualties and economic losses from earthquakes. Therefore, understanding building damage is critical for emergency handling. Current information acquisition methods for assessing earthquake damage using unmanned aerial vehicle (UAV) remote sensing systems offer great flexibility and high efficiency with the capability to obtain high-resolution images, which can reflect actual damage to affected areas intuitively. Consequently, UAV remote sensing has become a convenient and important means to acquire earthquake-induced building damage information. Although manual visual interpretation can achieve high recognition accuracy, it is extremely time-consuming. In contrast, although current automatic recognition methods require less time, they have relatively poor recognition accuracy. Neither approach can satisfactorily simultaneously meet efficiency and accuracy requirements for earthquake emergency handling. This article applies image classification algorithms based on the support vector machine (SVM) to earthquake-induced building damage recognition, and proposes a recognition method based on scale-invariant feature transform (SIFT) characteristics and SVM classification. We use the magnitude 6.4 earthquake at Yangbi (2021) as an example to validate the proposed method. Results verify that the proposed method can recognize earthquake damage quickly and accurately, providing effective support for decision-making regarding rescue actions.

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Deep Learning for Automatic Building Damage Assessment: Application in Post-Disaster Scenarios Using Uav Data

Cited by 22 publications

References 17 publications

Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models

Automatic Detection of Collapsed Buildings after the 6 February 2023 Türkiye Earthquakes Using Post-Disaster Satellite Images with Deep Learning-Based Semantic Segmentation Models

Disaster impact analysis in Africa: EDDA - UAV building damage dataset of rural and urban Mozambique

Earthquake-induced building damage recognition from unmanned aerial vehicle remote sensing using scale-invariant feature transform characteristics and support vector machine classification

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