Special Issue "Selected Papers from the International Conference on Geo-Information Technology and its Applications (ICGITA2019)"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Prof. Dr. Weicheng Wu
E-Mail Website
Guest Editor
Professor and Team Leader at the Key Lab of Digital Land & Resources, East China University of Technology, Nanchang 330013, China
Interests: environment; sustainability; environmental impact assessment; natural resources management; spatial analysis; urban planning; soil salinity mapping; climate change
Special Issues and Collections in MDPI journals
Prof. Dr. Yalan Liu
E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, North of 20A, Datun Road, Chaoyang District, Beijing 100101, China
Interests: Intelligent remote sensing information extraction for natural resource and environment, including land cover/land use change, disaster monitoring and assessment, and key technologies of space information integration.
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing has been playing a more and more important role in environmental monitoring, land resource quantification and mapping, natural hazard damage and risk assessment, urban planning, smart city development and other elements of land use change monitoring and modeling. New advancements and innovations have been achieved, especially with the emergence of big data mining and machine learning, including the deep learning technique. It is, hence, the objective of the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) to provide a platform for worldwide experts in these fields to exchange and communicate their outcomes and experiences with each other and to promote the advancement of geo-information technology and its applications. The ICGITA 2019 was held in Nanchang, Jiangxi, China on 11–13 October 2019, and a number of innovative technical researches related to big data mining, machine learning, and new algorithm development and their applications in different domains were presented. The aim of this Special Issue within Remote Sensing is to present these achievements. However, while this issue is dedicated to the selected papers of this conference, it is not limited to them. Authors are kindly invited to submit their works (extended abstracts with references are also accepted) to this Special Issue with the following scope:

  • Machine learning and big data mining technique;
  • Land resource mapping and land cover change tracking;
  • Natural hazard damage assessment and risk zoning;
  • Land degradation and dust storm monitoring;
  • Geological prospecting and assessment of mining impacts on the environment;
  • Deformation monitoring by radar, InSAR and GPS/Beidou.

Prof. Dr. Weicheng Wu
Prof. Dr. Yalan Liu
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. 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 2000 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

  • Remote sensing
  • Big data mining
  • Machine learning
  • Land resource mapping
  • Hazard damage and risk
  • Land degradation
  • Drought and dust storm
  • Geological exploration
  • Mining assessment

Published Papers (1 paper)

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Research

Open AccessArticle
Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images
Remote Sens. 2020, 12(2), 260; https://doi.org/10.3390/rs12020260 - 11 Jan 2020
Abstract
Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it [...] Read more.
Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images. Full article
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