Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces
Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The pur...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied System Innovation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-5577/8/3/62 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849418169584713728 |
|---|---|
| author | Osvaldo Santos Natércia Santos |
| author_facet | Osvaldo Santos Natércia Santos |
| author_sort | Osvaldo Santos |
| collection | DOAJ |
| description | Forest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces. |
| format | Article |
| id | doaj-art-c66d2de0f2bb4069b0eb447f11982ace |
| institution | Kabale University |
| issn | 2571-5577 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-c66d2de0f2bb4069b0eb447f11982ace2025-08-20T03:32:31ZengMDPI AGApplied System Innovation2571-55772025-04-01836210.3390/asi8030062Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban InterfacesOsvaldo Santos0Natércia Santos1Polytechnic Institute of Castelo Branco, 6000-081 Castelo Branco, PortugalAxtron Systems, 6000-024 Castelo Branco, PortugalForest fires have become one of the most destructive natural disasters worldwide, causing catastrophic losses, sometimes with the loss of lives. Therefore, some countries have created legislation to enforce mandatory fuel management within buffer zones in the vicinity of buildings and roads. The purpose of this study is to investigate whether inexpensive off-the-shelf drones equipped with standard RGB cameras could be used to detect the excess of trees and vegetation within those buffer zones. The methodology used in this study was the development and evaluation of a complete system, which uses AI to detect the contours of buildings and the services provided by the CHAMELEON bundles to detect trees and vegetation within buffer zones. The developed AI model is effective at detecting the building contours, with a mAP50 of 0.888. The article analyses the results obtained from two use cases: a road surrounded by dense forest and an isolated building with dense vegetation nearby. The main conclusion of this study is that off-the-shelf drones equipped with standard RGB cameras can be effective at detecting non-compliant vegetation and trees within buffer zones. This can be used to manage biomass within buffer zones, thus helping to reduce the risk of wildfire propagation in wildland–urban interfaces.https://www.mdpi.com/2571-5577/8/3/62dronesUAVwildfireforest fireCHAMELEONfire prevention |
| spellingShingle | Osvaldo Santos Natércia Santos Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces Applied System Innovation drones UAV wildfire forest fire CHAMELEON fire prevention |
| title | Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces |
| title_full | Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces |
| title_fullStr | Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces |
| title_full_unstemmed | Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces |
| title_short | Using Drones to Estimate and Reduce the Risk of Wildfire Propagation in Wildland–Urban Interfaces |
| title_sort | using drones to estimate and reduce the risk of wildfire propagation in wildland urban interfaces |
| topic | drones UAV wildfire forest fire CHAMELEON fire prevention |
| url | https://www.mdpi.com/2571-5577/8/3/62 |
| work_keys_str_mv | AT osvaldosantos usingdronestoestimateandreducetheriskofwildfirepropagationinwildlandurbaninterfaces AT naterciasantos usingdronestoestimateandreducetheriskofwildfirepropagationinwildlandurbaninterfaces |