Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon

ElGharbawi, Tamer; Zarzoura, Fawzi

doi:10.1016/j.isprsjprs.2021.01.001

Cited by 21 publications

(9 citation statements)

References 23 publications

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“…The explosion occurred in the port of Sri Lanka, killing 6 people [5]. Further examples include SEVESO in Italy 1976 [14], Chernobyl in Ukraine 1986 [15], and the recent Beirut port terminal disaster in 2020 [16], etc. It should be noted that, recently, many dangerous goods terminals have been moved outside cities as a solution to eliminate some risk variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Seaport Terminals Risks Prioritization Using a Structural Modeling-Based Approach: A Real Case Study

Dhahri

Elmsalmi

Aljuaid

et al. 2022

JMSE

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Port risk management (PRM) during port operations is a current problem that can negatively affect the environment, people, and economic issues. In the literature, there is an enormous amount of research related to supply chain risk management (SCRM) in various areas and with various objectives. However, PRM has not received the same degree of attention. In fact, port terminals are crucial links in most supply chain networks and an important pillar of international trade. Therefore, for better risk mitigation efficiency, a risk assessment and prioritization step are vital. Recently in the literature, researchers have applied prospective methods such as structural analysis methods to risk prioritization in SCRM. The aim of this research is to prioritize various man-made risk variables in PRM using a qualitative structural modeling-based approach, specifically, the MICMAC method (referring to its French acronym: Matrice d’Impacts Croisés Multiplication Appliquée à un Classement). An empirical study was conducted to assesses and prioritize risk variables of the seaport terminals of Sfax (Tunisia). The main contributions of the empirical research are twofold. First, to prioritize the key risk variables to define the most critical ones that require immediate intervention. Second, to analyze the structure of the influences between all identified risk variables. The results for the port terminals of Sfax show that the highest-priority risk variables are the manual handling (Ph3), disregard for safety aspects (Ph4), unsafe storage of goods (Inc1), absence of a prevention system and a rescue organization (Inc2), neglect of the regulatory aspects of handling equipment (M1), ignorance of good handling practices during the operation of loading and unloading (Cho2), and inadequate lifting accessories (Cho3). These risk variables must be the subject of urgent risk reduction strategies.

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Section: Literature Reviewmentioning

confidence: 99%

Seaport Terminals Risks Prioritization Using a Structural Modeling-Based Approach: A Real Case Study

Dhahri

Elmsalmi

Aljuaid

et al. 2022

JMSE

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“…Urban and transportation networks monitoring (UTNM) is an integral part of numerous applications, such as urban damage assessment [1], the production of digital maps (Sultanov et al,2018), land use categorization & verification [2], modernizing Geographic Information System (GIS) databases, and route planning [3].…”

Section: Introductionmentioning

confidence: 99%

Urban monitoring application in new administrative capital in Egypt using machine learning techniques and Sentinel 2 data

Fathy¹,

ElGharbawi²,

Amin

et al. 2022

Port-Said Engineering Research Journal

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Recently, Egypt has been undergoing rapid development in the urban and transportation sectors with a focus on the New Administrative Capital. This requires temporal monitoring and assessment for such projects with low cost, high accuracy, and simple application. Therefore, sentinel-2 data was utilized to assess the effectiveness of several methods for urban classification and monitoring. The study discusses three main approaches: histogram threshold, spectral indices, and machine learning techniques. In machine learning, we focus on K-nearest neighbor (KNN), Linear discrimination analysis (LDA), and Random Forest (RF) due to their robustness and simplicity. It was found that the RF technique presents the highest accuracy for our study region with 98.8%. Moreover, this study monitored the effect of such development on air pollution in the New Administrative Capital using sentinel-5P data which showed an increase in the CO and NO2 levels during the last five years by nearly 11% and 75% respectively, with a noticeable drop during the year 2020 due to the Covid19 lockdown. The classification model was trained and verified using ground truth data obtained on the 10 th of Ramadan City. Then, it was applied to monitoring urban development and the associated environmental impact in the New Administrative Capital in Egypt.

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“…The diversity of RS Earth observation imagery and its free availability has meant that monitoring of changes following disasters has turned into a hot topic for research [21][22][23][24][25][26][27][28]. Indeed, we are witnessing many BAM products on a global scale that differ in terms of spatial resolution and reliability of the burned areas mapped.…”

Section: Introductionmentioning

confidence: 99%

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

2021

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Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

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Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon

Cited by 21 publications

References 23 publications

Seaport Terminals Risks Prioritization Using a Structural Modeling-Based Approach: A Real Case Study

Seaport Terminals Risks Prioritization Using a Structural Modeling-Based Approach: A Real Case Study

Urban monitoring application in new administrative capital in Egypt using machine learning techniques and Sentinel 2 data

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

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