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Civil and Geo-Environmental Laboratory, Lille University, 59650 Villeneuve d'Ascq, France
Dr. Marwan Alheib
INERIS—French National Institute for Industrial Environment and Risks, Parc Technologique Alata—BP2, 60550 Verneuil-en-Halatte, France
Department of Project, Quality and Logistics Management, Faculty of Management, Wrocław University of Science and Technology, Smoluchowskiego 25, 50-370 Wrocław, Poland
Prof. Dr. Fadi Comair
Energy, Environment, Water and Research Centre, Cyprus Institute, Nicosia, Cyprus
Department of Civil, Energy, Environmental and Material Engineering, Mediterranean University of Reggio Calabria, 89124 Reggio Calabria, Italy
Prof. Dr. Xiongyao Xie
Department of Geotechnical Engineering, Tongji University, Shaghai, China
Prof. Dr. Yasin Fahjan
Civil Engineering, Istanbul Technical University, Maslak, Turkey
Dr. Salah Zidi
Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia
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Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience

Abstract submission deadline
31 March 2025
Manuscript submission deadline
30 June 2025
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1372

Topic Information

Dear Colleague,

Countries worldwide are subjected to new and complex challenges related to the intensification of the frequency and severity of natural disasters because of the impact of climate change, rapid demographic growth, and intense urbanization. These challenges have a significant socio-economic impact because of the large-scale damage due to natural disasters. Indeed, natural disasters generally cover large areas, causing substantial human losses, severe environmental damage, and destruction of infrastructures that support social and economic activity.

The latest advances in monitoring using IoT, crowdsourcing, satellites, and drones provide new opportunities to collect large amounts of data related to natural disasters.

The use of machine learning and big data enables the development of effective solutions that improve urban systems' resilience to natural disasters, including a better understanding of the response of complex socio-technical systems to natural disasters, the development of early warning systems, rapid scanning of damage, optimization of emergency actions, use of automation to reduce and protect critical infrastructures, and the adaptation of infrastructures to the new level of natural hazards.

The objective of this Topic is to share the latest developments in this area with a focus on the following questions:

  • What are the new scientific challenges related to the intensification of natural disasters (floods, earthquakes, storms, heat waves, disasters, wildfire and landslides)?
  • How could digital technology (IoT, crowdsourcing, and satellite) enhance natural disaster monitoring?
  • How could ML and BigData empower real-time analysis of data related to natural disasters?
  • How could ML and BigData improve the efficiency of early warning systems?
  • How could ML and BigData help adaptation strategies to natural disasters?
  • How could ML and BigData help reduce damage related to natural disasters?

Prof. Dr. Isam Shahrour
Dr. Marwan Alheib
Dr. Anna Brdulak
Prof. Dr. Fadi Comair
Dr. Carlo Giglio
Prof. Dr. Xiongyao Xie
Prof. Dr. Yasin Fahjan
Dr. Salah Zidi
Topic Editors

Keywords

  • big data
  • machine learning
  • artificial intelligence
  • crowdsourcing
  • IoT
  • Resilience
  • natural disaster
  • flood
  • earthquake
  • storms
  • landslide
  • wildfire
  • climate change
  • early warning
  • adaptation

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Earth
earth 		2.1 3.3 2020 21.7 Days CHF 1200 Submit
GeoHazards
geohazards 		- 2.6 2020 20.4 Days CHF 1000 Submit
ISPRS International Journal of Geo-Information
ijgi 		2.8 6.9 2012 36.2 Days CHF 1700 Submit
Land
land 		3.2 4.9 2012 17.8 Days CHF 2600 Submit
Remote Sensing
remotesensing 		4.2 8.3 2009 24.7 Days CHF 2700 Submit
Smart Cities
smartcities 		7.0 11.2 2018 25.8 Days CHF 2000 Submit
Infrastructures
infrastructures 		2.7 5.2 2016 16.8 Days CHF 1800 Submit
Automation
automation 		- 2.9 2020 20.6 Days CHF 1000 Submit

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Published Papers (2 papers)

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19 pages, 6613 KiB  
Article
Multi-Type Structural Damage Image Segmentation via Dual-Stage Optimization-Based Few-Shot Learning
by Jiwei Zhong, Yunlei Fan, Xungang Zhao, Qiang Zhou and Yang Xu
Smart Cities 2024, 7(4), 1888-1906; https://doi.org/10.3390/smartcities7040074 - 22 Jul 2024
Viewed by 280
Abstract
The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have [...] Read more.
The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities. Full article
(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)
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19 pages, 11545 KiB  
Article
Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification
by Adib Saliba, Kifah Tout, Chamseddine Zaki and Christophe Claramunt
ISPRS Int. J. Geo-Inf. 2024, 13(7), 259; https://doi.org/10.3390/ijgi13070259 - 20 Jul 2024
Viewed by 491
Abstract
This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of [...] Read more.
This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining. Full article
(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)
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