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Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 38119

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Special Issue Editor

Prof. Dr. Jason Levy

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Guest Editor

Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advances in remote sensing and GIS for hazards analysis, emergency studies, and disaster risk reduction in the age of intelligence. Governments around the world are investing in remote sensing for integrated all-hazard management in the age of artificial intelligence. This Special Issue will focus on technological transformations and ways in which advances in GIS and remote sensing can reduce disaster risk and increase integrated, all hazards, and comprehensive emergency management. This Special Issue also emphasizes that reducing disaster risk and investing in remote sensing technology can boost overall economic productivity, save lives, minimize damage to critical infrastructure and revitalize the economy.

Prof. Jason K. Levy
Guest Editor

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Keywords

integrated disaster risk reduction
all hazards emergency management
intelligent systems
remote sensing for disaster studies
GIS for hazards analysis
age of intelligence

Published Papers (5 papers)

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Research

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18 pages, 1069 KiB

Open AccessArticle

Incremental Composition Process for the Construction of Component-Based Management Systems

by Tauseef Rana, Yawar Abbas Bangash, Abdullah Baz, Toqir Ahmad Rana and Muhammad Ali Imran

Sensors 2020, 20(5), 1351; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051351 - 29 Feb 2020

Cited by 4 | Viewed by 3256

Abstract

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence)

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25 pages, 45705 KiB

Open AccessArticle

Improving GIS-Based Landslide Susceptibility Assessments with Multi-temporal Remote Sensing and Machine Learning

by Jhe-Syuan Lai and Fuan Tsai

Sensors 2019, 19(17), 3717; https://0-doi-org.brum.beds.ac.uk/10.3390/s19173717 - 27 Aug 2019

Cited by 36 | Viewed by 4079

Abstract

This study developed a systematic approach with machine learning (ML) to apply the satellite remote sensing images, geographic information system (GIS) datasets, and spatial analysis for multi-temporal and event-based landslide susceptibility assessments at a regional scale. Random forests (RF) algorithm, one of the ML-based methods, was selected to construct the landslide susceptibility models. Different ratios of landslide and non-landslide samples were considered in the experiments. This study also employed a cost-sensitive analysis to adjust the decision boundary of the developed RF models with unbalanced sample ratios to improve the prediction results. Two strategies were investigated for model verification, namely space- and time-robustness. The space-robustness verification was designed for separating samples into training and examining data based on a single event or the same dataset. The time-robustness verification was designed for predicting subsequent landslide events by constructing a landslide susceptibility model based on a specific event or period. A total of 14 GIS-based landslide-related factors were used and derived from the spatial analyses. The developed landslide susceptibility models were tested in a watershed region in northern Taiwan with a landslide inventory of changes detected through multi-temporal satellite images and verified through field investigation. To further examine the developed models, the landslide susceptibility distributions of true occurrence samples and the generated landslide susceptibility maps were compared. The experiments demonstrated that the proposed method can provide more reasonable results, and the accuracies were found to be higher than 93% and 75% in most cases for space- and time-robustness verifications, respectively. In addition, the mapping results revealed that the multi-temporal models did not seem to be affected by the sample ratios included in the analyses. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence)

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20 pages, 4357 KiB

Open AccessArticle

Unmanned Surface Vehicle Simulator with Realistic Environmental Disturbances

by Marcelo Paravisi, Davi H. Santos, Vitor Jorge, Guilherme Heck, Luiz Marcos Gonçalves and Alexandre Amory

Sensors 2019, 19(5), 1068; https://0-doi-org.brum.beds.ac.uk/10.3390/s19051068 - 02 Mar 2019

Cited by 39 | Viewed by 8941

Abstract

The use of robotics in disaster scenarios has become a reality. However, an Unmanned Surface Vehicle (USV) needs a robust navigation strategy to face unpredictable environmental forces such as waves, wind, and water current. A starting step toward this goal is to have a programming environment with realistic USV models where designers can assess their control strategies under different degrees of environmental disturbances. This paper presents a simulation environment integrated with robotic middleware which models the forces that act on a USV in a disaster scenario. Results show that these environmental forces affect the USV’s trajectories negatively, indicating the need for more research on USV control strategies considering harsh environmental conditions. Evaluation scenarios were presented to highlight specific features of the simulator, including a bridge inspection scenario with fast water current and winds. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence)

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18 pages, 3736 KiB

Open AccessArticle

A New Approach Using AHP to Generate Landslide Susceptibility Maps in the Chen-Yu-Lan Watershed, Taiwan

by Thi To Ngan Nguyen and Cheng-Chien Liu

Sensors 2019, 19(3), 505; https://0-doi-org.brum.beds.ac.uk/10.3390/s19030505 - 26 Jan 2019

Cited by 27 | Viewed by 4620

Abstract

This paper proposes a new approach of using the analytic hierarchy process (AHP), in which the AHP was combined with bivariate analysis and correlation statistics to evaluate the importance of the pairwise comparison. Instead of summarizing expert experience statistics to establish a scale, we then analyze the correlation between the properties of the related factors with the actual landslide data in the study area. In addition, correlation and dependence statistics are also used to analyze correlation coefficients of preparatory factors. The product of this research is a landslide susceptibility map (LSM) generated by five factors (slope, aspect, drainage density, lithology, and land-use) and pre-event landslides (Typhoon Kalmaegi events), and then validated by post-event landslides and new landslides occurring in during the events (Typhoon Kalmaegi and Typhoon Morakot). Validating the results by the binary classification method showed that the model has reasonable accuracy, such as 81.22% accurate interpretation for post-event landslides (Typhoon Kalmaegi), and 70.71% exact predictions for new landslides occurring during Typhoon Kalmaegi. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence)

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Review

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44 pages, 586 KiB

Open AccessReview

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

by Vitor A. M. Jorge, Roger Granada, Renan G. Maidana, Darlan A. Jurak, Guilherme Heck, Alvaro P. F. Negreiros, Davi H. dos Santos, Luiz M. G. Gonçalves and Alexandre M. Amory

Sensors 2019, 19(3), 702; https://0-doi-org.brum.beds.ac.uk/10.3390/s19030702 - 08 Feb 2019

Cited by 111 | Viewed by 16509

Abstract

Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps. Full article

(This article belongs to the Special Issue Integrated Disaster Risk Management and Remote Sensing in the Age of Intelligence)

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