Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Multimodal Remote Sensing and Artificial Intelligence Technologies for Disaster Prevention...
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Multimodal Remote Sensing and Artificial Intelligence Technologies for Disaster Prevention and Mitigation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 1348

Special Issue Editors

Dr. Futao Wang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: remote sensing; artificial intelligence; big earth data; disaster emergency monitoring; risk assessment
Dr. Marco Materazzi

E-Mail Website
Guest Editor
School of Science and Technology, University of Camerino, 62032 Camerino, Italy
Interests: hydrogeological mapping and GIS-based mapping for water resources; soil erosion and land degradation; geomorphology
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengchao Chen

E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Interests: radiometric calibration for remote sensing facility; remote sensing image processing; remote sensing information extraction based on deep learning
Dr. Ming Liu

E-Mail Website
Guest Editor
National Disaster Reduction Center of China, Beijing, China
Interests: disaster monitoring; satellite image; disaster assessment; GIS
Dr. Muhammad Hasan Ali Baig

E-Mail Website
Guest Editor
Institute of Geo-Information and Earth Observation (IGEO), PMAS University of Arid Agriculture Rawalpindi, Rawalpindi, Pakistan
Interests: geo-informatics; remote sensing
Dr. Margherita Bufalini

E-Mail Website
Guest Editor
School of Science and Technology, University of Camerino, 62032 Camerino, Italy
Interests: landslide; geological mapping; watershed hydrology; geomorphological hazard; flood hazard; hydrogeological hazard; soil erosion; hydrologic and hydraulic modelling; GIS analysis and mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with the development of high-speed industrialization, urbanization and globalization, the external environment and conditions of human life have significantly changed. Extreme meteorological events have led to prominent disaster risks, such as major floods, forest and grassland fires, droughts, and storm surges. In addition, production safety accidents are still in a period of frequent occurrence. Various hidden dangers and safety risks are intertwined and prone to occur, forming a complex and diverse disaster chain and accident chain, and the factors that affect public safety are increasing. At present, the global space infrastructure has entered a new stage of systematic development and globalization of services, and satellite remote sensing is developing into global observation and multi-satellite network observation, forming a three-dimensional, multi-dimensional, integrated global observation capability combining high, medium and low resolution. With the application of new technologies such as artificial intelligence, remote sensing has become an important means for countries around the world to carry out risk monitoring and early warning and enhance disaster risk reduction capabilities, bringing new opportunities to comprehensively improve society's natural disaster prevention and control capabilities.

This Special Issue calls for articles that deal with innovative approaches to disaster emergency monitoring, risk assessment and predictive early warning using multimodal remote sensing (multispectral, hyperspectral, radar and thermal infrared) and artificial intelligence techniques or related case studies. The disasters can cover a wider range of areas from natural hazards (earthquakes, landslides, mudslides, floods, fires, droughts and storm surges) to production safety accidents (mining accidents, hazardous chemical explosions and fires). Hence, submissions that focus on how multimodal remote sensing, artificial intelligence and other technological approaches can be effectively applied and assist in decision-making for the different stages of disaster prevention and mitigation, among other issues, are welcome.

Dr. Futao Wang
Dr. Marco Materazzi
Dr. Zhengchao Chen
Dr. Ming Liu
Dr. Muhammad Hasan Ali Baig
Dr. Margherita Bufalini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • disaster monitoring
  • natural hazards
  • multimodal remote sensing
  • artificial intelligence
  • risk assessment
  • disaster risks
  • soil erosion
  • landslides
  • big earth data
  • GIS

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 11165 KiB  
Article
Monitoring and Analyzing the Effectiveness of the Effective Refuge Area of Emergency Shelters by Using Remote Sensing: A Case Study of Beijing’s Fifth Ring Road
by Di You, Shixin Wang, Futao Wang, Yi Zhou, Zhenqing Wang, Yanchao Wang, Jingming Wang, Yibing Xiong and Jianwan Ji
Remote Sens. 2023, 15(14), 3646; https://doi.org/10.3390/rs15143646 - 21 Jul 2023
Cited by 1 | Viewed by 955
Abstract
The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is [...] Read more.
The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation. Full article
(This article belongs to the Special Issue Multimodal Remote Sensing and Artificial Intelligence Technologies for Disaster Prevention and Mitigation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop