Special Issue "Advanced Methodology for Developing an Inventory Database of Human-Made Structures in Urban Areas for Assessment of Risk and Vulnerability"

Special Issue Editors

Dr. Hiroyuki Miura
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Guest Editor
Department of Architecture, Hiroshima University, Hiroshima 739-8527, Japan
Interests: earthquake engineering; ground motion analysis; geospatial analysis for damage assessment; remote sensing for disaster response; DEM analysis for geomorphology
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Prof. Dr. Masashi Matsuoka
E-Mail Website
Guest Editor
Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan
Interests: earthquake engineering; geomorphology; GIS and application of remote sensing technology to disaster management
Special Issues and Collections in MDPI journals
Prof. Dr. Yoshihisa Maruyama
E-Mail Website
Guest Editor
Department of Urban Environment Systems, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba 263-8522, Japan
Interests: real-time earthquake engineering; urban disaster mitigation; lifeline engineering

Special Issue Information

Dear Colleagues,

Assessment of risk and vulnerability of human-made structures, such as buildings and infrastructures, is an important issue when it comes to taking countermeasures against natural disasters. GIS-based risk and vulnerability analysis can be powerful tools to comprehend the extent and amount of damage expected in scenarios, to support effective disaster mitigation strategies, and to assist early recovery and reconstruction activities.

An inventory database for human-made structures would be crucial for such assessments. GIS inventories have been officially developed by local and national governments in most urban areas. Open-source databases such as OpenStreetMap are now also available online. The existing databases, however, need to be updated to follow recent developments in urban areas in a timely manner if significant discrepancy between the database and the real world is found. Additionally, detailed information, which is required for risk and vulnerability assessment, such as the typical materials of the structure, structural systems, use, and construction year, is not contained in the database. The development of methodologies to effectively construct or update the inventory database and efficiently provide or estimate the attributes for risk assessment are important tasks that must be completed to prepare for coming disasters.

In order to concentrate the knowledge and experiences accumulated thus far, we would like to invite you to submit articles on your recent work. The topics of interest include but are not limited to the following keywords.

Dr. Hiroyuki Miura
Prof. Dr. Masashi Matsuoka
Prof. Dr. Yoshihisa Maruyama
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 papers will be 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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1400 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

  • Risk and vulnerability analysis
  • Damage and loss estimation for scenarios
  • Inventory data development for buildings and infrastructures
  • Remote sensing for data generation
  • AI computing for spatial attribute
  • Disaster mitigation planning
  • Spatial data analysis for recovery/reconstruction process
  • Critical infrastructure protection against disasters
  • GIS-based decision support systems for risk analysis, emergency management, scenario simulations
  • Resilience enhancement strategies

Published Papers (1 paper)

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Research

Article
Simultaneous Extraction of Road and Centerline from Aerial Images Using a Deep Convolutional Neural Network
ISPRS Int. J. Geo-Inf. 2021, 10(3), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030147 - 08 Mar 2021
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Abstract
The extraction of roads and centerlines from aerial imagery is considered an important topic because it contributes to different fields, such as urban planning, transportation engineering, and disaster mitigation. Many researchers have studied this topic as a two-separated task that affects the quality [...] Read more.
The extraction of roads and centerlines from aerial imagery is considered an important topic because it contributes to different fields, such as urban planning, transportation engineering, and disaster mitigation. Many researchers have studied this topic as a two-separated task that affects the quality of extracted roads and centerlines because of the correlation between these two tasks. Accurate road extraction enhances accurate centerline extraction if these two tasks are processed simultaneously. This study proposes a multitask learning scheme using a gated deep convolutional neural network (DCNN) to extract roads and centerlines simultaneously. The DCNN is composed of one encoder and two decoders implemented on the U-Net backbone. The decoders are assigned to extract roads and centerlines from low-resolution feature maps. Before extraction, the images are processed within an encoder to extract the spatial information from a complex, high-resolution image. The encoder consists of the residual blocks (Res-Block) connected to a bridge represented by a Res-Block, and the bridge connects the two identical decoders, which consists of stacking convolutional layers (Conv.layer). Attention gates (AGs) are added to our model to enhance the selection process for the true pixels that represent road or centerline classes. Our model is trained on a dataset of high-resolution aerial images, which is open to the public. The model succeeds in efficiently extracting roads and centerlines compared with other multitask learning models. Full article
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