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Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster...
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Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster Management

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 18473

Special Issue Editors

Dr. Ivanka Pelivan

E-Mail Website
Guest Editor
Fraunhofer-Institut für Nachrichtentechnik Heinrich-Hertz-Institut, Berlin, Germany
Interests: artificial intelligence; machine learning; remote sensing; small solar system bodies; thermal modeling; fluid dynamics
Dr. Raffaele Albano

E-Mail Website
Guest Editor
School of Engineering, University of Basilicata, Potenza, Italy
Interests: floods; water resource management; soil moisture; hydrology; artificial intelligence; GIS; geomorphic method; DEM; geospatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Reza Arghandeh

E-Mail Website
Guest Editor
Department of Computer Science, Electrical Engineering, and Mathematical Sciences, Western Norway University, Bergen, Norway
Interests: artificial intelligence; machine learning; computer vision; remote sensing; infrastructure networks; resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), in combination with remote sensing (RS), has shown significant potential in a wide range of applications, including detection, mapping, and monitoring of natural hazards, such as floods, earthquakes, landslides, snow avalanches, wildfires, droughts, volcanic eruptions, hurricanes, and tsunamis. RS data can also be used to develop or improve methods for a better understanding of the complex physical phenomena underlying the occurrence of big earthquakes, severe storm events, or volcanic eruptions, to assess risks, and to build forecast and early warning systems.

Tremendeous advances in remote sensing technologies are connected to improved spatio-temporal resolution and increased coverage. Enablers, such as open data access and the development of user-friendly open-source AI tools, facilitate a wide spectrum of applications within the geosciences. However, with the increase in number of operating satellites and other Earth observation platforms, challenges when dealing with data gaps, inconsistencies, and combining heterogeneous data persist, and new difficulties relating to the highly increased volume and complexity of data have to be tackled when aiming at providing timely and relevant information about hazard extend, exposure, and impacts. Leveraging uptake of ML and RS solutions requires to address responsible AI alongside the consideration of data and algorithmic biases, sustainable development, and ethical implications.

This Special Issue is open to a diverse range of contributions on recent advances in the application of machine learning methods, such as explainable and interpretable AI, scalable AI, edge computing and federated learning, and remote sensing including multisensor fusion, for single or multi-hazard mitigation, preparedness, response, and recovery. We invite submissions that may include, but are not limited to, the following topics:

  • Mapping of (historical) events
  • (Near) real-time hazard monitoring
  • Remote sensing for risk analysis and damage assessment
  • Single and multi-hazard detection, modeling, and prediction
  • Explainable and interpretable AI for informed decision making
  • Responsible AI for natural hazard mitigation
  • Physical model integration
  • Multisensor data fusion
  • Benchmark datasets for model validation

Dr. Ivanka Pelivan
Dr. Raffaele Albano
Prof. Dr. Reza Arghandeh
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

  • artificial intelligence
  • machine learning
  • remote sensing
  • earth observation
  • natural hazard

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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36 pages, 9154 KiB  
Article
Landslide Susceptibility Mapping: Analysis of Different Feature Selection Techniques with Artificial Neural Network Tuned by Bayesian and Metaheuristic Algorithms
by Farkhanda Abbas, Feng Zhang, Fazila Abbas, Muhammad Ismail, Javed Iqbal, Dostdar Hussain, Garee Khan, Abdulwahed Fahad Alrefaei and Mohammed Fahad Albeshr
Remote Sens. 2023, 15(17), 4330; https://doi.org/10.3390/rs15174330 - 2 Sep 2023
Cited by 14 | Viewed by 2155
Abstract
The most frequent and noticeable natural calamity in the Karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram Highway, particularly during monsoons, causing a major loss of life and property. Therefore, it is necessary to look for a solution to increase [...] Read more.
The most frequent and noticeable natural calamity in the Karakoram region is landslides. Extreme landslides have occurred frequently along Karakoram Highway, particularly during monsoons, causing a major loss of life and property. Therefore, it is necessary to look for a solution to increase growth and vigilance in order to lessen losses related to landslides caused by natural disasters. By utilizing contemporary technologies, an early warning system might be developed. Artificial neural networks (ANNs) are widely used nowadays across many industries. This paper’s major goal is to provide new integrative models for assessing landslide susceptibility in a prone area in the north of Pakistan. To achieve this, the training of an artificial neural network (ANN) was supervised using metaheuristic and Bayesian techniques: Particle Swarm Optimization (PSO) algorithm, Genetic algorithm (GA), Bayesian Optimization Gaussian Process (BO_GP), and Bayesian Optimization Tree-structured Parzen Estimator (BO_TPE). In total, 304 previous landslides and the eight most prevalent conditioning elements were combined to form a geospatial database. The models were hyperparameter optimized, and the best ones were employed to generate susceptibility maps. The obtained area under the curve (AUC) accuracy index demonstrated that the maps produced by both Bayesian and metaheuristic algorithms are highly accurate. The effectiveness and efficiency of applying ANNs for landslide mapping, susceptibility analysis, and forecasting were studied in this research, and it was observed from experimentation that the performance differences for GA, BO_GP, and PSO compared to BO_TPE were relatively small, ranging from 0.32% to 1.84%. This suggests that these techniques achieved comparable performance to BO_TPE in terms of AUC. However, it is important to note that the significance of these differences can vary depending on the specific context and requirements of the ML task. Additionally, in this study, we explore eight feature selection algorithms to determine the geospatial variable importance for landslide susceptibility mapping along the Karakoram Highway (KKH). The algorithms considered include Information Gain, Variance Inflation Factor, OneR Classifier, Subset Evaluators, principal components, Relief Attribute Evaluator, correlation, and Symmetrical Uncertainty. These algorithms enable us to evaluate the relevance and significance of different geospatial variables in predicting landslide susceptibility. By applying these feature selection algorithms, we aim to identify the most influential geospatial variables that contribute to landslide occurrences along the KKH. The algorithms encompass a diverse range of techniques, such as measuring entropy reduction, accounting for attribute bias, generating single rules, evaluating feature subsets, reducing dimensionality, and assessing correlation and information sharing. The findings of this study will provide valuable insights into the critical geospatial variables associated with landslide susceptibility along the KKH. These insights can aid in the development of effective landslide mitigation strategies, infrastructure planning, and targeted hazard management efforts. Additionally, the study contributes to the field of geospatial analysis by showcasing the applicability and effectiveness of various feature selection algorithms in the context of landslide susceptibility mapping. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster Management)
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26 pages, 29095 KiB  
Article
Self-Incremental Learning for Rapid Identification of Collapsed Buildings Triggered by Natural Disasters
by Jiayi Ge, Hong Tang and Chao Ji
Remote Sens. 2023, 15(15), 3909; https://doi.org/10.3390/rs15153909 - 7 Aug 2023
Cited by 1 | Viewed by 1813
Abstract
The building damage caused by natural disasters seriously threatens human security. Applying deep learning algorithms to identify collapsed buildings from remote sensing images is crucial for rapid post-disaster emergency response. However, the diversity of buildings, limited training dataset size, and lack of ground-truth [...] Read more.
The building damage caused by natural disasters seriously threatens human security. Applying deep learning algorithms to identify collapsed buildings from remote sensing images is crucial for rapid post-disaster emergency response. However, the diversity of buildings, limited training dataset size, and lack of ground-truth samples after sudden disasters can significantly reduce the generalization of a pre-trained model for building damage identification when applied directly to non-preset locations. To address this challenge, a self-incremental learning framework (i.e., SELF) is proposed in this paper, which can quickly improve the generalization ability of the pre-trained model in disaster areas by self-training an incremental model using automatically selected samples from post-disaster images. The effectiveness of the proposed method is verified on the 2010 Yushu earthquake, 2023 Turkey earthquake, and other disaster types. The experimental results demonstrate that our approach outperforms state-of-the-art methods in terms of collapsed building identification, with an average increase of more than 6.4% in the Kappa coefficient. Furthermore, the entire process of the self-incremental learning method, including sample selection, incremental learning, and collapsed building identification, can be completed within 6 h after obtaining the post-disaster images. Therefore, the proposed method is effective for emergency response to natural disasters, which can quickly improve the application effect of the deep learning model to provide more accurate building damage results. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster Management)
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22 pages, 10375 KiB  
Article
Sentinel-1 SAR Images and Deep Learning for Water Body Mapping
by Fernando Pech-May, Raúl Aquino-Santos and Jorge Delgadillo-Partida
Remote Sens. 2023, 15(12), 3009; https://doi.org/10.3390/rs15123009 - 8 Jun 2023
Cited by 9 | Viewed by 4127
Abstract
Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing [...] Read more.
Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster Management)
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Review

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41 pages, 4018 KiB  
Review
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities
by Jing Jia and Wenjie Ye
Remote Sens. 2023, 15(16), 4098; https://doi.org/10.3390/rs15164098 - 21 Aug 2023
Cited by 12 | Viewed by 8013
Abstract
Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a [...] Read more.
Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Natural Hazard and Disaster Management)
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