Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation

Keerthinathan, Pirunthan; Amarasingam, Narmilan; Hamilton, Grant; Gonzalez, Felipe

doi:10.1080/01431161.2023.2249604

Cited by 6 publications

(2 citation statements)

References 231 publications

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“…Based on the application's aim, after searching for the objectives, the UAVs implement a high-altitude fire-fighting operation [14], which requires the capability to produce largecapacity video transmission with a low delay. UAVs localize the victims and evaluate the rescue difficulty level [15], which requires the capability to produce rapid image transmission with a low delay. UAVs implement an emergency communication accessing service to connect the victims and the base station [16], which requires a stable network state.…”

Section: Related Work 21 Search and Rescue Task In Fanetmentioning

confidence: 99%

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Li,

Hao,

Wen

et al. 2024

Drones

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In the event of a sudden natural disaster, the damaged communication infrastructure cannot provide a necessary network service for vehicles. Unfortunately, this is the critical moment when the occupants of trapped vehicles need to urgently use the vehicular network’s emergency service. How to efficiently connect the trapped vehicle to the base station is the challenge facing the emergency vehicular network. To address this challenge, this study proposes a UAV-assisted multi-objective and multi-hop ad hoc network (UMMVN) that can be used as an emergency vehicular network. Firstly, it presents an integrated design of a search system to find a trapped vehicle, the communication relay, and the networking, which significantly decreases the UAV’s networking time cost. Secondly, it presents a multi-objective search for a trapped vehicle and navigates UAVs along multiple paths to different objectives. Thirdly, it presents an optimal branching node strategy that allows the adequate use of the overlapping paths to multiple targets, which decreases the networking cost within the limited communication and searching range. The numerical experiments illustrate that the UMMVN performs better than other state-of-the-art networking methods.

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Section: Related Work 21 Search and Rescue Task In Fanetmentioning

confidence: 99%

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Li,

Hao,

Wen

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…UAS technologies show potential to strategically fit into the operational framework of this challenging fire environment (Ambrosia and Wegener, 2009;Zajkowski et al, 2016). For example, UAS technology proves to be relatively safe, inexpensive, and low complexity when compared to their manned counterparts (Shamaoma et al, 2022;Keerthinathan et al, 2023). UAS-captured information is also relatively fine resolution and temporally flexible when compared to space-borne and airborne remote sensing methods (Muchiri and Kimathi 2016;Pádua et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

Lawrence,

de Lemmus

2024

Preprint

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The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision has led to significant research output regarding fire and smoke detection. Previous studies often focus on themes like early fire detection, increased operational awareness, and post-fire assessment. To further test the capabilities of deep learning detection in these scenarios, we collected and labeled a unique aerial image dataset that determined whether specific types of fire behavior could be reliably detected in prescribed fire settings. Our 960 labeled images were sourced from over 20.97 hours of UAS video collected during prescribed fire operations covering a large region of Texas and Louisiana, U.S.. National Wildfire Coordinating Group (NWCG) fire behavior observations and descriptions served as a reference for determining fire behavior classes during labeling. YOLOv8 models were trained on NWCG Rank 1-3 fire behavior descriptions in grassland, shrubland, forested, and combined fire regimes within our study area. Models were first trained and validated on isolated image objects of fire behavior, and then on segmenting fire behavior in their original parent images. Models trained using isolated image objects of fire behavior consistently performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model at a box and mask mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire behavior with computer vision is possible in most fire regimes and fuel models, whereas segmenting fire behavior around background information is relatively difficult. However, it may be a manageable task with enough data, and when models are developed for a specific fire regime. With an increasing number of destructive wildfires and new challenges confronting fire managers, identifying how new technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our conclusion is that levels of abstraction deeper than mere detection of smoke or fire are possible using computer vision, and could make even more detailed fire monitoring possible.

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Using computer vision to classify, locate and segment fire behavior in UAS-captured images

Lawrence,

de Lemmus

2024

Science of Remote Sensing

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Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation

Cited by 6 publications

References 231 publications

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Multi-Objective Deployment of UAVs for Multi-Hop FANET: UAV-Assisted Emergency Vehicular Network

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

Using computer vision to classify, locate and segment fire behavior in UAS-captured images

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