Remote Sensing

Journal Browser

► Journal Browser

Special Issue Information

Dear Colleagues,

Natural disasters are extreme events within the Earth’s system that may have a catastrophic impact on the environment and humanity. Efficient disaster management is crucial in the aftermath of a disaster for a speedy recovery with minimal possible loss. Effective recovery planning requires fast and accurate disaster impact assessment, and remote sensing provides big data to facilitate such assessments. This Special Issue focuses on open big data, computer vision, and artificial intelligence methods that can be used to process remote sensing data for aftermath impact assessment.

Prof. Dr. Turgay Celik
Prof. Dr. Terence Van Zyl
Prof. Dr. Elif Sertel
Prof. Dr. Changwook Lee
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

Computer vision
Big data
Artificial intelligence
Remote sensing
Disaster impact assessment
Disaster management
Change detection
Object recognition
Open data

Published Papers (2 papers)

Download All Papers

Research

23 pages, 23382 KiB

Open AccessArticle

Estimating the Pre-Historical Volcanic Eruption in the Hantangang River Volcanic Field: Experimental and Simulation Study

by Wahyu Luqmanul Hakim, Suci Ramayanti, Sungjae Park, Bokyun Ko, Dae-Kyo Cheong and Chang-Wook Lee

Remote Sens. 2022, 14(4), 894; https://doi.org/10.3390/rs14040894 - 13 Feb 2022

Cited by 5 | Viewed by 2756

Abstract

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence, Big Data and Computer Vision in Remote Sensing for Natural Disaster Impact Assessment)

► Show Figures

Figure 1

24 pages, 5468 KiB

Open AccessArticle

Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping

by Taskin Kavzoglu, Alihan Teke and Elif Ozlem Yilmaz

Remote Sens. 2021, 13(23), 4776; https://doi.org/10.3390/rs13234776 - 25 Nov 2021

Cited by 22 | Viewed by 3585

Abstract

Natural disaster impact assessment is of the utmost significance for post-disaster recovery, environmental protection, and hazard mitigation plans. With their recent usage in landslide susceptibility mapping, deep learning (DL) architectures have proven their efficiency in many scientific studies. However, some restrictions, including insufficient model variance and limited generalization capabilities, have been reported in the literature. To overcome these restrictions, ensembling DL models has often been preferred as a practical solution. In this study, an ensemble DL architecture, based on shared blocks, was proposed to improve the prediction capability of individual DL models. For this purpose, three DL models, namely Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM), together with their ensemble form (CNN–RNN–LSTM) were utilized to model landslide susceptibility in Trabzon province, Turkey. The proposed DL architecture produced the highest modeling performance of 0.93, followed by CNN (0.92), RNN (0.91), and LSTM (0.86). Findings proved that the proposed model excelled the performance of the DL models by up to 7% in terms of overall accuracy, which was also confirmed by the Wilcoxon signed-rank test. The area under curve analysis also showed a significant improvement (~4%) in susceptibility map accuracy by the proposed strategy. Full article

(This article belongs to the Special Issue Artificial Intelligence, Big Data and Computer Vision in Remote Sensing for Natural Disaster Impact Assessment)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Artificial Intelligence, Big Data and Computer Vision in Remote Sensing for Natural Disaster Impact Assessment

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI