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IJGI
Special Issues
GIS in Seismic Disaster Risk Assessment and Management
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GIS in Seismic Disaster Risk Assessment and Management

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 3186

Special Issue Editor

Dr. Gerardo Grelle

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Rome "Sapienza", 00184 Rome, Italy
Interests: seismic risk reduction; landslide risk reduction; site seismic response; GIS method for seismic effects evaluation; spatial distribution by metamodeling and data mining modeling

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to present works based on data GIS processing such as spatial analysis, geostatistical, and modeling or metamodeling, as well as geoinformation algorithms or interactive representation architectures, all focused on seismic-Induced Hazards (or Risk) assessing or their governance and management. The studies can involve and be applied to different study areas, both theoretical/demonstrative and case studies.

Therefore, GIS-based models, processes or procedures able to produce maps that combine morphometric and subsoil data using canonical or simplified (empirical or stochastic) models and multimodel combinations are welcome, taking into account the soil and rock features, as well as their dynamic behavior. The phenomena should be seismic shaking and its effects on the anthropized environment, earthquake-induced landslides, liquefaction, and densification, as well as tsunami invasion areas.

Dr. Gerardo Grelle
Guest Editor

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. 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 1700 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

  • GIS modeling
  • geoinformation algorithms
  • seismic hazard
  • seismic effects
  • co-seismic effects

Published Papers (1 paper)

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Research

16 pages, 2962 KiB  
Article
A Postearthquake Multiple Scene Recognition Model Based on Classical SSD Method and Transfer Learning
by Zhiqiang Xu, Yumin Chen, Fan Yang, Tianyou Chu and Hongyan Zhou
ISPRS Int. J. Geo-Inf. 2020, 9(4), 238; https://doi.org/10.3390/ijgi9040238 - 11 Apr 2020
Cited by 12 | Viewed by 2539
Abstract
The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition [...] Read more.
The recognition of postearthquake scenes plays an important role in postearthquake rescue and reconstruction. To overcome the over-reliance on expert visual interpretation and the poor recognition performance of traditional machine learning in postearthquake scene recognition, this paper proposes a postearthquake multiple scene recognition (PEMSR) model based on the classical deep learning Single Shot MultiBox Detector (SSD) method. In this paper, a labeled postearthquake scenes dataset is constructed by segmenting acquired remote sensing images, which are classified into six categories: landslide, houses, ruins, trees, clogged and ponding. Due to the insufficiency and imbalance of the original dataset, transfer learning and a data augmentation and balancing strategy are utilized in the PEMSR model. To evaluate the PEMSR model, the evaluation metrics of precision, recall and F1 score are used in the experiment. Multiple experimental test results demonstrate that the PEMSR model shows a stronger performance in postearthquake scene recognition. The PEMSR model improves the detection accuracy of each scene compared with SSD by transfer learning and data augmentation strategy. In addition, the average detection time of the PEMSR model only needs 0.4565s, which is far less than the 8.3472s of the traditional Histogram of Oriented Gradient + Support Vector Machine (HOG+SVM) method. Full article
(This article belongs to the Special Issue GIS in Seismic Disaster Risk Assessment and Management)
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