Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities—A case study for Muzzaffarabad, Pakistan

Pitts, D. A. de Alwis; So, Emily

doi:10.1016/j.jag.2017.07.010

Cited by 9 publications

(7 citation statements)

References 32 publications

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“…Similar to [38], we determine K i for each vertex . X i through the following steps: 1 ⃝ We define the maximum and minimum possible neighbor counts as k max = ⌊k ratio × N S ⌋ and k min = ⌊k max /10⌋, where k ratio is the neighbor ratio and ⌊•⌋ represents floor division. Then, we calculate the feature distance dist X i,j = X i − X j 2 2 between vertex .…”

Section: Structural Graph Construction and CI Measurementmentioning

confidence: 99%

“…Remote sensing image change detection (CD) is an emerging technology attracting attention across various domains including natural resource monitoring [1,2], disaster warning [3,4], and urban planning [5,6]. It enables the detection and analysis of surface changes on Earth through the comparative analysis of remote sensing images captured within the same geographic region over time.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection

Tang,

Yang,

Han

et al. 2024

Remote Sensing

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Heterogeneous change detection (CD) is widely applied in various fields such as urban planning, environmental monitoring, and disaster management. It enhances the accuracy and comprehensiveness of surface change monitoring by integrating multi-sensor remote sensing data. Scholars have proposed many graph-based methods to address the issue of incomparable heterogeneous images caused by imaging differences. However, these methods often overlook the influence of changes in vertex status on the graph structure, which limits their ability to represent image structural features. To tackle this problem, this paper presents an unsupervised heterogeneous CD method based on enhanced graph structure representation (EGSR). This method enhances the representation capacity of the graph structure for image structural features by measuring the unchanged probabilities of vertices, thereby making it easier to detect changes in heterogeneous images. Firstly, we construct the graph structure using image superpixels and measure the structural graph differences of heterogeneous images in the same image domain. Then, we calculate the unchanged probability of each vertex in the structural graph and reconstruct the graph structure using this probability. To accurately represent the graph structure, we adopt an iterative framework for enhancing the representation of the graph structure. Finally, at the end of the iteration, the final change map (CM) is obtained by binary segmentation of the graph vertices based on their unchanged probabilities. The effectiveness of this method is validated through experiments on four sets of heterogeneous image datasets and two sets of homogeneous image datasets.

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Section: Structural Graph Construction and CI Measurementmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection

Tang,

Yang,

Han

et al. 2024

Remote Sensing

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“…With the quick advancement of remote sensing technology, it has become gradually easier to obtain remote sensing images that can be used for CD, which promoted the development of CD to a certain extent. At present, CD is widely used in land use monitoring [1], disaster assessment [2], urban development planning [3], environmental monitoring [4], and other fields.…”

Section: Introductionmentioning

confidence: 99%

HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images

Wang

Cheng

2022

Sensors

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Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth’s surface. The existing CD methods focus on the design of feature extraction network, ignoring the strategy fusion and attention enhancement of the extracted features, which will lead to the problems of incomplete boundary of changed area and missing detection of small targets in the final output change map. To overcome the above problems, we proposed a hierarchical attention residual nested U-Net (HARNU-Net) for remote sensing image CD. First, the backbone network is composed of a Siamese network and nested U-Net. We remold the convolution block in nested U-Net and proposed ACON-Relu residual convolution block (A-R), which reduces the missed detection rate of the backbone network in small change areas. Second, this paper proposed the adjacent feature fusion module (AFFM). Based on the adjacency fusion strategy, the module effectively integrates the details and semantic information of multi-level features, so as to realize the feature complementarity and spatial mutual enhancement between adjacent features. Finally, the hierarchical attention residual module (HARM) is proposed, which locally filters and enhances the features in a more fine-grained space to output a much better change map. Adequate experiments on three challenging benchmark public datasets, CDD, LEVIR-CD and BCDD, show that our method outperforms several other state-of-the-art methods and performs excellent in F1, IOU and visual image quality.

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“…Contreras et al [20] integrated high-resolution RS imagery and ground data to monitor the recovery process after the 2009 L'Aquila earthquake, in Italy. de Alwis Pitts and So [21] proposed a semi-automated object-based change detection method using pre-disaster map data and very high-resolution optical images to monitor buildings after the Kashmir earthquake, Pakistan. In addition, Derakhshan et al [22] used remote sensingbased indices to monitor land cover changes in urban areas for post-earthquake recovery time.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Ghaffarian

Emtehani

2021

Climate

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Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

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Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities—A case study for Muzzaffarabad, Pakistan

Cited by 9 publications

References 32 publications

Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection

Enhanced Graph Structure Representation for Unsupervised Heterogeneous Change Detection

HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images

Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

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