Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Earth earth	3.4	5.9	2020	19.4 Days	CHF 1400	Submit
GeoHazards geohazards	1.6	2.2	2020	17.2 Days	CHF 1400	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	34.2 Days	CHF 1900	Submit
Land land	3.2	5.9	2012	16 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit
Infrastructures infrastructures	2.9	6.0	2016	15.7 Days	CHF 1800	Submit
Automation automation	2.0	4.1	2020	23.4 Days	CHF 1200	Submit

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Published Papers (12 papers)

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15 pages, 3141 KB  

Open AccessArticle

Gravity Data-Driven Machine Learning: A Novel Approach for Predicting Volcanic Vent Locations in Geohazard Investigation

by Murad Abdulfarraj, Ema Abraham, Faisal Alqahtani and Essam Aboud

GeoHazards 2025, 6(3), 49; https://doi.org/10.3390/geohazards6030049 - 29 Aug 2025

Viewed by 865

Abstract

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed [...] Read more.

Geohazard investigation in volcanic fields is essential for understanding and mitigating risks associated with volcanic activity. Volcanic vents are often concealed by processes such as faulting, subsidence, or uplift, which complicates their detection and hampers hazard assessment. To address this challenge, we developed a predictive framework that integrates high-resolution gravity data with multiple machine learning algorithms. Logistic Regression, Gradient Boosting Machine (GBM), Decision Tree, Support Vector Machine (SVM), and Random Forest models were applied to analyze the gravitational characteristics of known volcanic vents and predict the likelihood of undiscovered vents at other locations. The problem was formulated as a binary classification task, and model performance was assessed using accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The Random Forest algorithm yielded optimal outcomes: 95% classification accuracy, AUC-ROC score of 0.99, 75% geographic correspondence between real and modeled vent sites, and a 95% certainty degree. Spatial density analysis showed that the distribution patterns of predicted and actual vents are highly similar, underscoring the model’s reliability in identifying vent-prone areas. The proposed method offers a valuable tool for geoscientists and disaster management authorities to improve volcanic hazard evaluation and implement effective mitigation strategies. These results represent a significant step forward in our ability to model volcanic dynamics and enhance predictive capabilities for volcanic hazard assessment. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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20 pages, 2036 KB  

Open AccessArticle

Symmetry-Based Data Augmentation Method for Deep Learning-Based Structural Damage Identification

by Long Li, Xiaoming Tao, Hui Song, Xiaolong Li, Zhilong Ye, Yao Jin, Qiuyu He, Shiyin Wei and Wenli Chen

Infrastructures 2025, 10(6), 145; https://doi.org/10.3390/infrastructures10060145 - 12 Jun 2025

Viewed by 719

Abstract

The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural [...] Read more.

The big data collected from structural health monitoring systems (SHMs), combined with the rapid advances in machine learning (ML), have enabled data-driven methods in practical SHM applications. These methods typically use ML algorithms to identify patterns within features extracted from data representing structural conditions, thereby inferring damage from changes in these patterns. However, data-driven models often struggle to generalize effectively to unseen datasets. This study addresses this challenge through three key contributions: dataset augmentation, an efficient feature representation, and a probabilistic modeling approach. First, a data augmentation method leveraging the symmetric properties of bridge structures is introduced to enhance dataset diversity. Second, a novel damage indicator named Fre-GraRMSC1 is proposed, capable of distinguishing both damage locations and severity. Finally, a probabilistic generative model based on a deep belief network (DBN) is developed to predict damage locations and degrees. The proposed methods are validated using vibration data from a numerical three-span continuous bridge subjected to random vehicle excitations. Results demonstrate high accuracy in damage identification and improved generalization performance. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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24 pages, 7273 KB  

Open AccessArticle

Study on the Risk of Urban Population Exposure to Waterlogging in Huang-Huai Area Based on Machine Learning Simulation Analysis—A Case Study of Xuzhou Urban Area

by Shuai Tong, Jiuxin Wang, Jiahui Qin, Xiang Ji and Zihan Wu

Land 2025, 14(5), 939; https://doi.org/10.3390/land14050939 - 25 Apr 2025

Viewed by 651

Abstract

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, [...] Read more.

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of 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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26 pages, 10308 KB  

Open AccessArticle

Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard

by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha and Jean Pierre Bizimana

ISPRS Int. J. Geo-Inf. 2025, 14(4), 161; https://doi.org/10.3390/ijgi14040161 - 8 Apr 2025

Cited by 1 | Viewed by 2344

Abstract

Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. [...] Read more.

Rapid urbanization and climate change are increasing the risks associated with natural hazards, especially in cities where socio-economic disparities are significant. Current hazard risk assessment frameworks fail to consider socio-economic factors, which limits their ability to effectively address vulnerabilities at the community level. This study introduces a machine learning framework designed to assess flood susceptibility and socio-economic vulnerability, particularly in urban areas with limited data. Using Kigali, Rwanda, as a case study, we quantified socio-economic vulnerability through a composite index that includes indicators of sensitivity and adaptive capacity. We utilized a variety of data sources, such as demographic, environmental, and remotely sensing datasets, applying machine learning algorithms like Multilayer Perceptron (MLP), Random Forest, Support Vector Machine (SVM), and XGBoost. Among these, MLP achieved the best predictive performance, with an AUC score of 0.902 and an F1-score of 0.86. The findings indicate spatial differences in socio-economic vulnerability, with central and southern Kigali showing greater vulnerability due to a mix of socio-economic challenges and high flood risk. The vulnerability maps created were validated against historical flood records, socio-economic research, and expert insights, confirming their accuracy and relevance for urban risk assessment. Additionally, we tested the framework’s scalability and adaptability in Kampala, Uganda, and Dar es Salaam, Tanzania, showing that making context-specific adjustments to the model improves its transferability. This study offers a solid, data-driven approach for combining assessments of flood susceptibility and socio-economic vulnerability, filling important gaps in urban resilience planning. The results support the advancement of risk-informed decision-making, especially in areas with limited access to detailed socio-economic information. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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30 pages, 31096 KB  

Open AccessArticle

Decadal Trends and Drivers of Dust Emissions in East Asia: Integrating Statistical and SHAP-Based Interpretability Approaches

by Ziwei Yi, Yaqiang Wang, Zhaoliang Zeng, Weijie Li, Huizheng Che and Xiaoye Zhang

Remote Sens. 2025, 17(7), 1313; https://doi.org/10.3390/rs17071313 - 7 Apr 2025

Cited by 1 | Viewed by 1644

Abstract

Dust emissions significantly impact the radiation balance, ecosystems, human health, and global climate change through long-range transport. However, their spatiotemporal characteristics and driving mechanisms in East Asia remain poorly understood. This study integrates multi-source reanalysis and remote sensing data (1980–2023) to analyze dust [...] Read more.

Dust emissions significantly impact the radiation balance, ecosystems, human health, and global climate change through long-range transport. However, their spatiotemporal characteristics and driving mechanisms in East Asia remain poorly understood. This study integrates multi-source reanalysis and remote sensing data (1980–2023) to analyze dust emissions across East Asian source regions using statistical methods and SHapley Additive exPlanations (SHAP) interpretability. The results show significant spatial and seasonal variations, with peak emissions occurring in spring (March–May). The Taklamakan Desert (S4) accounts for 38.1% of total emissions and is the largest source region. Meteorological factors are the main drivers (49.4–68.8% contribution), while climate indices contribute the least (2.9–8.0%). Wind speed is the most critical factor driving dust emissions, showing a significant positive correlation and interacting with 850 hPa geopotential height and boundary layer height. The driving factors of dust emissions vary across regions. In Mongolia (S1), dust emissions are mainly influenced by wind speed and atmospheric circulation, while in S4, near-surface meteorological conditions play a dominant role. In the Tsaidam Basin and Kumutage Desert (S5), as well as the Badain Jaran, Tengger, and Ulan Buh Deserts (S6), dust emissions are primarily driven by wind speed and boundary layer height, with atmospheric circulation also playing a certain role. Relative humidity shows a significant negative correlation with dust emissions in S5 and S6, while snowmelt and soil temperature have significant impacts on S4 and S5. The negative phases of the Arctic Oscillation and North Atlantic Oscillation enhance cold air activity and wind speed, significantly promoting dust emissions in S1 and S6. This study quantifies the mechanisms of dust emissions in East Asia and offers scientific support for improving climate models and developing disaster mitigation strategies. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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24 pages, 10312 KB  

Open AccessArticle

Spatial Network in SQL Databases for Real-Time Multimodal Emergency Routing in Wildland Fires

by Mateusz Ilba

ISPRS Int. J. Geo-Inf. 2025, 14(3), 110; https://doi.org/10.3390/ijgi14030110 - 2 Mar 2025

Viewed by 1281

Abstract

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining [...] Read more.

Evacuation routing in wildland areas is an important aspect during various emergencies, including fire incidents. A review of the literature found a lack of research on vector routing systems for evacuations from wildland areas. This article aims to address the issue of determining evacuation routes using vector object database technology with various optimization methods. To this end, the author developed a novel algorithm for network creation and optimization through heuristic data aggregation. Case studies were conducted in a wooded area of the Bieszczady Mountains, where the potential of determining evacuation routes in the proprietary geodatabase (SQLite SpatiaLite) was examined, and the results were compared with traditional methods based on raster least-cost path analyses. The analyses confirmed the feasibility of creating a network of connections in the database within an area of 3.74 km² with undefined roads. Through the implementation of optimizations, the determination of evacuation routes in wildland areas was reduced to less than 1 s. Additionally, the possibility of the system operating for areas covering 40 km² was presented. The use of optimized vector data and database technology enabled the development of a comprehensive forest area management system, encompassing points of rescue units situated at significant distances from the area. This facilitated the establishment of flexible evacuation routes or rescue missions, particularly allowing for the establishment of multimodal routes using different means of transportation to reach the destination. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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25 pages, 13626 KB  

Open AccessArticle

Fine-Tuning LLM-Assisted Chinese Disaster Geospatial Intelligence Extraction and Case Studies

by Yaoyao Han, Jiping Liu, An Luo, Yong Wang and Shuai Bao

ISPRS Int. J. Geo-Inf. 2025, 14(2), 79; https://doi.org/10.3390/ijgi14020079 - 11 Feb 2025

Cited by 4 | Viewed by 2249

Abstract

The extraction of disaster geospatial intelligence (DGI) from social media data with spatiotemporal attributes plays a crucial role in real-time disaster monitoring and emergency decision-making. However, conventional machine learning approaches struggle with semantic complexity and limited Chinese disaster corpus. Recent advancements in large [...] Read more.

The extraction of disaster geospatial intelligence (DGI) from social media data with spatiotemporal attributes plays a crucial role in real-time disaster monitoring and emergency decision-making. However, conventional machine learning approaches struggle with semantic complexity and limited Chinese disaster corpus. Recent advancements in large language models (LLMs) offer new opportunities to overcome these challenges due to their enhanced semantic comprehension and multi-task learning capabilities. This study investigates the potential application of LLMs in disaster intelligence extraction and proposes an efficient, scalable method for multi-hazard DGI extraction. Building upon a unified ontological framework encompassing core natural disaster elements, this method employs parameter-efficient low-rank adaptation (LoRA) fine-tuning to optimize open-source Chinese LLMs using a meticulously curated instruction-tuning dataset. It achieves simultaneous identification of multi-hazard intelligence cues and extraction of disaster spatial entity attributes from unstructured Chinese social media texts through unified semantic parsing and structured knowledge mapping. Compared to pre-trained models such as BERT and ERNIE, the proposed method was shown to achieve state-of-the-art evaluation results, with the highest recognition accuracy (F1-score: 0.9714) and the best performance in structured information generation (BLEU-4 score: 92.9649). Furthermore, we developed and released DGI-Corpus, a Chinese instruction-tuning dataset covering various disaster types, to support the research and application of LLMs in this field. Lastly, the proposed method was applied to analyze the spatiotemporal evolution patterns of the Zhengzhou “7.20” flood disaster. This study enhances the efficiency of natural disaster monitoring and emergency management, offering technical support for disaster response and mitigation decision-making. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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23 pages, 22602 KB  

Open AccessArticle

Enhancing Human Detection in Occlusion-Heavy Disaster Scenarios: A Visibility-Enhanced DINO (VE-DINO) Model with Reassembled Occlusion Dataset

by Zi-An Zhao, Shidan Wang, Min-Xin Chen, Ye-Jiao Mao, Andy Chi-Ho Chan, Derek Ka-Hei Lai, Duo Wai-Chi Wong and James Chung-Wai Cheung

Smart Cities 2025, 8(1), 12; https://doi.org/10.3390/smartcities8010012 - 16 Jan 2025

Cited by 2 | Viewed by 3119

Abstract

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) [...] Read more.

Natural disasters create complex environments where effective human detection is both critical and challenging, especially when individuals are partially occluded. While recent advancements in computer vision have improved detection capabilities, there remains a significant need for efficient solutions that can enhance search-and-rescue (SAR) operations in resource-constrained disaster scenarios. This study modified the original DINO (Detection Transformer with Improved Denoising Anchor Boxes) model and introduced the visibility-enhanced DINO (VE-DINO) model, designed for robust human detection in occlusion-heavy environments, with potential integration into SAR system. VE-DINO enhances detection accuracy by incorporating body part key point information and employing a specialized loss function. The model was trained and validated using the COCO2017 dataset, with additional external testing conducted on the Disaster Occlusion Detection Dataset (DODD), which we developed by meticulously compiling relevant images from existing public datasets to represent occlusion scenarios in disaster contexts. The VE-DINO achieved an average precision of 0.615 at IoU 0.50:0.90 on all bounding boxes, outperforming the original DINO model (0.491) in the testing set. The external testing of VE-DINO achieved an average precision of 0.500. An ablation study was conducted and demonstrated the robustness of the model subject when confronted with varying degrees of body occlusion. Furthermore, to illustrate the practicality, we conducted a case study demonstrating the usability of the model when integrated into an unmanned aerial vehicle (UAV)-based SAR system, showcasing its potential in real-world scenarios. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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33 pages, 629 KB  

Open AccessArticle

Enhancing Smart City Connectivity: A Multi-Metric CNN-LSTM Beamforming Based Approach to Optimize Dynamic Source Routing in 6G Networks for MANETs and VANETs

by Vincenzo Inzillo, David Garompolo and Carlo Giglio

Smart Cities 2024, 7(5), 3022-3054; https://doi.org/10.3390/smartcities7050118 - 17 Oct 2024

Cited by 9 | Viewed by 2592

Abstract

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel [...] Read more.

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly 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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21 pages, 1921 KB  

Open AccessArticle

Utilizing Machine Learning and Multi-Station Observations to Investigate the Visibility of Sea Fog in the Beibu Gulf

by Qin Huang, Peng Zeng, Xiaowei Guo and Jingjing Lyu

Remote Sens. 2024, 16(18), 3392; https://doi.org/10.3390/rs16183392 - 12 Sep 2024

Cited by 1 | Viewed by 1554

Abstract

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations [...] Read more.

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ($T_{\mathrm{a}}-T_{\mathrm{d}}$), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. 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, 6613 KB  

Open AccessArticle

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

Cited by 3 | Viewed by 1746

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 KB  

Open AccessArticle

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 2021

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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