Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Sublime, Jérémie; Kalinicheva, Ekaterina

doi:10.3390/rs11091123

Cited by 105 publications

(84 citation statements)

References 27 publications

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“…However, [25] and [82] are very specific case studies. Whereas in this paper and in [66,[76][77][78][80][81][82][98][99][100] the areas of study are heterogeneous, in [25], results were presented only related to an ice shelf in the Antarctic region, which is a homogeneous area of study. According to [100], results tend to be higher for homogeneous areas of study compared to heterogeneous ones.…”

Section: Study Accuracy Precision Recall F-measurementioning

confidence: 91%

“…Most current research focuses on outlier detection, such as [1,14,[19][20][21][22][23][24][25]44,[51][52][53][54]66,[76][77][78][80][81][82], however we discuss below only those research which are more relevant to this article. An outlier-based change detection method to detect abnormal points in multi-scale time-series images from remote sensing was proposed by Yin Shoujing et al in [76].…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

“…The detection of spatial-temporal processes of flooding in satellite data was used to assess the method. A deep-learning approach for change detection applied to satellite time-series images was presented by Sublime and Kalinicheva in [66]. Their approach was compared against other machine-learning methods.…”

Section: Research On Outliers Detectionmentioning

confidence: 99%

“…However, this could be considered trial and error learning [35]. The use of the taxonomy [13] can bring to the computational systems a more organized and structured approach of machine learning [28,35,[64][65][66][67][68][69][70][71][72][73][74] for detecting problems, i.e., more "intelligence." In this case, for example, a decision-making system should itself first identify a problem with the minimum interaction with humans before offering an adequate solution that could be based on spectral analysis [61].…”

mentioning

confidence: 99%

“…2020, 12, 43 3 of 36 different contexts and based on a single concept (the taxonomy [13]) make our study unique. This leads to a higher level of meaningful learning, i.e., an evolution in machine learning [28,35,[64][65][66][67][68][69][70][71][72][73][74].Among all relevant areas of research, we have found the first solution to this problem just in remote sensing. It opens up an entirely new perspective and a set of possibilities for future work in remote sensing and many other areas.…”

mentioning

confidence: 99%

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings. Kittler et al. in [13] proposed a taxonomy of anomalies which expanded the concept of anomaly beyond the conventional meaning of outlier. They used sensory data quality assessment [26], contextual [9] and non-contextual [27][28][29][30] classifiers [5,[15][16][17][18], and an incongruence indicator [16][17][18] to identify each type of anomaly [13]. According to this taxonomy [13] anomalies can be, for example, of the types unknown object, measurement model drift, unknown structure, unexpected structural component, component model drift, and unexpected structure and structural components. This taxonomy [13] is well-known and widely accepted by the scientific community because it has the potential to be applied for solving problems in many different research areas [13]. Therefore, studies related to the application of the taxonomy [13] on synthetic data are common, such as in [16][17][18]. However, to the knowledge of the authors, studies have not addressed the practical application of the taxonomy [13] to solve real-world problems [2,[31][32][33], because it remains a challenge for all research areas.Anomaly detection [13] and incongruence [15][16][17][18] are two powerful computational tools from pattern recognition (PR) [3,[11][12][13][14][15][16][17][18]34,35] and computer vision (CV) [3,36]. PR is a scientific area of study dedicated to analyze patterns and regularities in data [3]. PR provides powerful tools [34] for many different applications and research areas [35] such as scientific research, private and public industries, military activities, etc., [14,[21][22][23][24]32,[37][38][39][40][41][42][43][44][45][46][47][48]. For example, PR is important for geosciences as its tools are used to analyze geographical features of environments in digital images from remote sensing, i.e., scenes [11,12,[49][50][51][52][53][54]. Additionally, PR also provides powerful tools to help machine perception...

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Section: Study Accuracy Precision Recall F-measurementioning

confidence: 91%

Section: Research On Outliers Detectionmentioning

confidence: 99%

Section: Research On Outliers Detectionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

et al. 2019

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A Review on Multi‐temporal Earthquake Damage Assessment Using Satellite Images

Taşkın

Erten

Alataş

2021

Change Detection and Image Time Series Analysis 2

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Using data science for sustainable development in higher education

Filho

Eustachio

Danila

et al. 2023

Sustainable Development

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Despite the abundance of studies focused on how higher education institutions (HEIs) are implementing sustainable development (SD) in their educational programmes, there is a paucity of interdisciplinary studies exploring the role of technology, such as data science, in an SD context. Further research is thus needed to identify how SD is being deployed in higher education (HE), generating positive externalities for society and the environment. This study aims to address this research gap by exploring various ways in which data science may support university efforts towards SD. The methodology relied on a bibliometric analysis to understand and visualise the connections between data science and SD in HE, as well as reporting on selected case studies showing how data science may be deployed for creating SD impact in HE and in the community. The results from the bibliometric analysis unveil five research strands driving this field, and the case studies exemplify them. This study can be considered innovative since it follows previous research on artificial intelligence and SD. Moreover, the combination of bibliometric analysis and case studies provides an overview of trends, which may be useful to researchers and decision‐makers who wish to explore the use of data science for SD in HEIs. Finally, the findings highlight how data science can be used in HEIs, combined with a framework developed to support further research into SD in HE.

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Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Cited by 105 publications

References 27 publications

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

An Incongruence-Based Anomaly Detection Strategy for Analyzing Water Pollution in Images from Remote Sensing

A Review on Multi‐temporal Earthquake Damage Assessment Using Satellite Images

Using data science for sustainable development in higher education

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