Conceptual design of a wildfire emergency response system empowered by swarms of unmanned aerial vehicles

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: unknown, P. 105493 - 105493

Published: April 1, 2025

Language: Английский

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UAVs-FFDB: A high-resolution dataset for advancing forest fire detection and monitoring using unmanned aerial vehicles (UAVs)

Data in Brief, Journal Year: 2024, Volume and Issue: 55, P. 110706 - 110706

Published: July 3, 2024

Forest ecosystems face increasing wildfire threats, demanding prompt and precise detection methods to ensure efficient fire control. However, real-time forest data accessibility timeliness require improvement. Our study addresses the challenge through introduction of Unmanned Aerial Vehicles (UAVs) based database (UAVs-FFDB), characterized by a dual composition. Firstly, it encompasses collection 1653 high-resolution RGB raw images meticulously captured utilizing standard S500 quadcopter frame in conjunction with RaspiCamV2 camera. Secondly, incorporates augmented data, culminating total 15560 images, thereby enhancing diversity comprehensiveness dataset. These were within forested area adjacent Adana Alparslan Türkeş Science Technology University Adana, Turkey. Each image dataset spans dimensions from 353 × 314 640 480, while ranges 398 358 resulting size 692 MB for subset. In contrast, subset accounts considerably larger size, totaling 6.76 GB. The are obtained during UAV surveillance mission, camera precisely angled -180-degree be horizontal ground. taken altitudes alternating between 5 - 15 meters diversify field vision build more inclusive database. During operation, speed is 2 m/s on average. Following this, underwent meticulous annotation using advanced platform, Makesense.ai, enabling accurate demarcation boundaries. This resource equips researchers necessary infrastructure develop innovative methodologies early continuous monitoring, efforts protect human lives promoting sustainable management practices. Additionally, UAVs-FFDB serves as foundational cornerstone advancement refinement state-of-the-art AI-based methodologies, aiming automate classification, recognition, detection, segmentation tasks unparalleled precision efficacy.

Language: Английский

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A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia

Technology Knowledge and Learning, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

Language: Английский

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Hot Topics and Frontier Evolution of Formation Control Research in Multiple Robots

Journal of Robotics, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

This research conducts an extensive bibliometric analysis of the literature on formation control multiple robots. The main objective is to offer a comprehensive present condition and future potential this area. To accomplish this, employs methodology examine current trends forecast developments. gathered total 1656 journal articles from Web Science (WoS) database. utilizes bibliographic coupling keyword co‐occurrence analyses identify influential publications, delineate knowledge structure, trends. yielded four separate clusters, while revealed clusters. Although importance in robots growing, more scholarly effort necessary fully understand academic panorama. article provides significant insights into rapidly expanding field Furthermore, it offers thorough examination diverse emphasizes its for ongoing advancement.

Language: Английский

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An integrated framework for wildfire emergency response and post-fire debris flow prediction: a case study from the wildfire event on 20 April 2021 in Mianning, Sichuan, China

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: May 8, 2025

Language: Английский

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