Contextual classification using photometry and elevation data for damage detection after an earthquake event

Rupnik, Ewelina; Nex, Francesco; Toschi, I.; Remondino, Fabio

doi:10.1080/22797254.2018.1458584

Cited by 8 publications

(7 citation statements)

References 45 publications

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“…Aerial and UAV-based orthophoto imageries are flexible data acquisitions both to flight patterns and time [9,10]. Moreover, these data have higher spatial resolutions compared to satellite imagery sensors that make them more appropriate for comprehensive inventories [11]. When a large-scale earthquake occurs, tremendous time is needed to visually interpret the aerial images.…”

Section: Of 22mentioning

confidence: 99%

“…Their results showed an overall accuracy (OA) of 80% compared to the field survey. Rupnik et al [11] developed an automatic workflow for the detection of the damaged building using semi-supervised random forests (RF) and unsupervised H MEAN and K-mean methods from airborne nadir imageries. Three classes were identified: (i) damaged buildings, (ii) intact buildings, and (iii) others.…”

Section: Related Studiesmentioning

confidence: 99%

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Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

et al. 2020

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In recent years, remote-sensing (RS) technologies have been used together with image processing and traditional techniques in various disaster-related works. Among these is detecting building damage from orthophoto imagery that was inflicted by earthquakes. Automatic and visual techniques are considered as typical methods to produce building damage maps using RS images. The visual technique, however, is time-consuming due to manual sampling. The automatic method is able to detect the damaged building by extracting the defect features. However, various design methods and widely changing real-world conditions, such as shadow and light changes, cause challenges to the extensive appointing of automatic methods. As a potential solution for such challenges, this research proposes the adaption of deep learning (DL), specifically convolutional neural networks (CNN), which has a high ability to learn features automatically, to identify damaged buildings from pre- and post-event RS imageries. Since RS data revolves around imagery, CNNs can arguably be most effective at automatically discovering relevant features, avoiding the need for feature engineering based on expert knowledge. In this work, we focus on RS imageries from orthophoto imageries for damaged-building detection, specifically for (i) background, (ii) no damage, (iii) minor damage, and (iv) debris classifications. The gist is to uncover the CNN architecture that will work best for this purpose. To this end, three CNN models, namely the twin model, fusion model, and composite model, are applied to the pre- and post-orthophoto imageries collected from the 2016 Kumamoto earthquake, Japan. The robustness of the models was evaluated using four evaluation metrics, namely overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and F1 score. According to the obtained results, the twin model achieved higher accuracy (OA = 76.86%; F1 score = 0.761) compare to the fusion model (OA = 72.27%; F1 score = 0.714) and composite (OA = 69.24%; F1 score = 0.682) models.

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Section: Of 22mentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

et al. 2020

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“…The automated detection of damages from images has been investigated for several years. Many recent approaches rely on both images and 3D points cloud to assess the damage state of a given area [14,16,38]. However, the generation of 3D information takes a considerable amount of time, thus limiting its use in FR procedures.…”

Section: Automated Building Damage Assessment For First Respondersmentioning

confidence: 99%

“…However, these satellite images typically have limited spatial resolution (0.3-1 m ground sampling distance, GSD) and the uncertainty and subjectivity in the identification of hazard-induced damages can often be solved by using higher resolution images [13]. On the other hand, airborne images [14][15][16] are often not available in remote places where the delivery of mapping airplanes is not feasible in an emergency timeframe. Therefore, in situ surveys are often preferred.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

et al. 2019

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The timely and efficient generation of detailed damage maps is of fundamental importance following disaster events to speed up first responders’ (FR) rescue activities and help trapped victims. Several works dealing with the automated detection of building damages have been published in the last decade. The increasingly widespread availability of inexpensive UAV platforms has also driven their recent adoption for rescue operations (i.e., search and rescue). Their deployment, however, remains largely limited to visual image inspection by skilled operators, limiting their applicability in time-constrained real conditions. This paper proposes a new solution to autonomously map building damages with a commercial UAV in near real-time. The solution integrates different components that allow the live streaming of the images on a laptop and their processing on the fly. Advanced photogrammetric techniques and deep learning algorithms are combined to deliver a true-orthophoto showing the position of building damages, which are already processed by the time the UAV returns to base. These algorithms have been customized to deliver fast results, fulfilling the near real-time requirements. The complete solution has been tested in different conditions, and received positive feedback by the FR involved in the EU funded project INACHUS. Two realistic pilot tests are described in the paper. The achieved results show the great potential of the presented approach, how close the proposed solution is to FR’ expectations, and where more work is still needed.

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“…Various optical-based studies for building damage detection have been proposed. The related studies vary from the methods based on multi-temporal optical images [7] to the methods based on single-temporal optical image [8], from the methods based on a single optical platform to the methods based on multiple optical platforms [9], from the methods based on pixels [7] to the methods based on objects [8], from the methods using machine learning [10] to the methods utilizing deep learning [11]. Optical-based methods have been studied widely and can obtain accurate detection results of building damage.…”

Section: Introductionmentioning

confidence: 99%

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

et al. 2021

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Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

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Contextual classification using photometry and elevation data for damage detection after an earthquake event

Cited by 8 publications

References 45 publications

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

Assessment of Convolutional Neural Network Architectures for Earthquake-Induced Building Damage Detection based on Pre- and Post-Event Orthophoto Images

Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions

Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

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