This survey provides a comprehensive overview of the latest advances in artificial intelligence (AI)-enabled drone systems for wildfire detection, with a particular focus on lightweight, edge-deployable architectures suitable for resource-constrained UAV platforms. We critically examine the landscape of computer vision approaches across both single- and multi-modal sensor pipelines. Our analysis reveals that YOLOv8 consistently offers one of the strongest trade-offs between detection accuracy, inference speed, and energy efficiency on edge devices. Emphasis is placed on adaptive camera management strategies dynamically switching between RGB and thermal imaging based on environmental conditions and the practical integration of gas sensors for early warning or fallback verification, while deliberately avoiding the high computational and power overhead associated with advanced multi-sensor data fusion. Analysis of real-world deployments highlights persistent challenges, including limited battery endurance, reliable detection of small or obscured fires, and robustness under environmental variability such as fog or low light. Beyond conventional directions, this survey points to promising research avenues including ultra-lightweight model optimization via quantization, pruning, and knowledge distillation, advanced adaptive sensing logic, predictive modeling of time-dependant fire spread dynamics using sequential UAV imagery, and the creation of more diverse, large-scale annotated datasets. This synthesis offers practical guidance for researchers and practitioners developing next-generation, truly field-ready wildfire detection systems.
AbdusalomovA.UmirzakovaS.Bakhtiyor ShukhratovichM., et al. (2024). Drone-Based wildfire detection with multi-sensor integration. Remote Sens (Basel), 16(24), 4651. https://doi.org/10.3390/rs16244651
2.
AbdusalomovA.UmirzakovaS.KutlimuratovA., et al. (2025). Lightweight UAV-based system for early fire-risk identification in wild forests. Fire, 8(8), 288. https://doi.org/10.3390/fire8080288
3.
AhmadN.AkbarM.AlkhammashE. H., et al. (2025a). CN2VF-Net: A hybrid convolutional neural network and vision transformer framework for multi-scale fire detection in complex environments. Fire, 8(6), 211. https://doi.org/10.3390/fire8060211
4.
AhmadN.AkbarM.AlkhammashE. H., et al. (2025b). FireNet-KD: Swin transformer-based wildfire detection with multi-source knowledge distillation. Fire, 8(8), 295. https://doi.org/10.3390/fire8080295
5.
AhuchogaM. C. (2025). Real-time image-based data processing and its applications in managerial decision-making and risk analysis. EKSPLORIUM, 46(1), 1552–1565. https://doi.org/10.52783/eksplorium.181
6.
Al-DabbaghA. M.IlyasM. (2023). Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models. Geomatics, Natural Hazards and Risk, 14(1), 2196370. https://doi.org/10.1080/19475705.2023.2196370
7.
AliM.AhmadI.GeunI., et al. (2025). A comprehensive review of advanced sensor technologies for fire detection with a focus on gasistor-based sensors. Chemosensors, 13(7), 230. https://doi.org/10.3390/chemosensors13070230
8.
AllisonR.JohnstonJ.CraigG., et al. (2016). Airborne optical and thermal remote sensing for wildfire detection and monitoring. Sensors, 16(8), 1310. https://doi.org/10.3390/s16081310
9.
Al-SmadiY.AlauthmanM.Al-QeremA., et al. (2023). Early wildfire smoke detection using different YOLO models. Machines, 11(2), 246. https://doi.org/10.3390/machines11020246
10.
BahharC.KsibiA.AyadiM., et al. (2023). Wildfire and smoke detection using staged YOLO model and ensemble CNN. Electronics (Basel), 12(1), 228. https://doi.org/10.3390/electronics12010228
11.
BakirciM.BayraktarI. (2024). Harnessing UAV technology and YOLOv9 algorithm for real-time forest fire detection. In 2024 International Russian automation conference (RusAutoCon) (pp. 95–100). IEEE.
12.
BarmpoutisP.PapaioannouP.DimitropoulosK., et al. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20(22), 6442. https://doi.org/10.3390/s20226442
13.
BoroujeniS. P. H.RaziA.KhoshdelS., et al. (2024). A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management. Information Fusion, 108, 102369. https://doi.org/10.1016/j.inffus.2024.102369
14.
BushnaqO. M.ChaabanA.Al-NaffouriT. Y. (2021). The role of UAV-IoT networks in future wildfire detection. IEEE Internet Things J, 8(23), 16984–16999. https://doi.org/10.1109/JIOT.2021.3077593
15.
Camara de MC.SantosR.CoelhoM.OliveiraR. (2024). Real-time object detection performance analysis using YOLOv7 on edge devices. IEEE Latin America Transactions, 22(10), 799–805. https://doi.org/10.1109/TLA.2024.10705971
CasasE.RamosL.BendekE., et al. (2024). YOLOv5 vs. YOLOv8: Performance benchmarking in wildfire and smoke detection scenarios. Journal of Image and Graphics, 12(2), 127–136. https://doi.org/10.18178/joig.12.2.127-136
18.
ChenX.HopkinsB.WangH., et al. (2022). Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset. IEEE Access, 10, 121301–121317. https://doi.org/10.1109/ACCESS.2022.3222805
19.
ChenY.ZhangY.ZhangG., et al. (2024). A drone swarm-based wildfire search and rescue method with autonomous behavior modeling and centralized task assignment. In 2024 IEEE 4th international conference on power, electronics and computer applications (ICPECA) (pp. 341–345). IEEE.
20.
DiruitW.le BrisA.BajjoukT., et al. (2022). Seaweed habitats on the shore: Characterization through hyperspectral UAV imagery and field sampling. Remote Sens (Basel), 14(13), 3124. https://doi.org/10.3390/rs14133124
21.
DuangsuwanS.KlubsuwanK. (2023). Accuracy assessment of drone real-time open burning imagery detection for early wildfire surveillance. Forests, 14(9), 1852. https://doi.org/10.3390/f14091852
GhaliR.AkhloufiM. A. (2024). Deep learning approach for wildland fire recognition using RGB and thermal infrared aerial image. Fire, 7(10), 343. https://doi.org/10.3390/fire7100343
24.
GhaliR.AkhloufiM. A.MseddiW. S. (2022). Deep learning and transformer approaches for UAV-based wildfire detection and segmentation. Sensors, 22(5), 1977. https://doi.org/10.3390/s22051977
25.
HanY.DuanB.GuanR., et al. (2024). LUFFD-YOLO: A lightweight model for UAV remote sensing forest fire detection based on attention mechanism and multi-level feature fusion. Remote Sens (Basel), 16(12), 2177. https://doi.org/10.3390/rs16122177
26.
HeH.ZhangZ.JiaQ., et al. (2023). Wildfire detection for transmission line based on improved lightweight YOLO. Energy Reports, 9(Supplement 1), 512–520. https://doi.org/10.1016/j.egyr.2022.10.435
27.
HendelI.-G.RossG. M. (2020). Efficacy of remote sensing in early forest fire detection: A thermal sensor comparison. Canadian Journal of Remote Sensing, 46(4), 414–428. https://doi.org/10.1080/07038992.2020.1776597
28.
HinkleyE. A.ZajkowskiT. (2011). USDA Forest service–NASA: Unmanned aerial systems demonstrations – pushing the leading edge in fire mapping. Geocarto Int, 26(2), 103–111. https://doi.org/10.1080/10106049.2011.555823
29.
HoangM. L. (2023). Smart drone surveillance system based on AI and on IoT communication in case of intrusion and fire accident. Drones, 7(12), 694. https://doi.org/10.3390/drones7120694
30.
HusseinS.SalehA. (2025). Hybrid Forest Fires Prediction (HF2P) Strategy Based on Ensemble Classification of Convolutional Neural Networks (CNN) and Decision Tree (DT) models. Nile Journal of Communication and Computer Science, 9(1).https://doi.org/10.21608/njccs.2025.381784.1050
31.
IvanovaS.ProsekovA.KaledinA. (2022). A survey on monitoring of wild animals during fires using drones. Fire, 5, 60. https://doi.org/10.3390/fire5030060
32.
JinB.LiaoL.ShenX., et al. (2025). Advancement in Research on Silicon/Carbon Composite Anode Materials for Lithium-Ion Batteries. Metals (Basel, 15(386). https://doi.org/10.3390/met15040386
33.
KimS.JungY.-H.MinH., et al. (2024). Adaptive sensor management for UGV monitoring based on risk maps. Rob Auton Syst, 172, 104605. https://doi.org/10.1016/j.robot.2023.104605
34.
KinanevaD.HristovG.RaychevJ., et al. (2019). Early forest fire detection using drones and artificial intelligence. In 2019 42nd international convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 1060–1065). IEEE.
35.
KrizhevskyA.SutskeverI.HintonG. E. (2017) Imagenet classification with deep convolutional neural networks. Commun AC, 60(M2017), 84–90. https://doi.org/10.1145/3065386
36.
KrüllW.ToberaR.WillmsI., et al. (2012). Early forest fire detection and verification using optical smoke, gas and microwave sensors. Procedia Eng, 45, 584–594. https://doi.org/10.1016/j.proeng.2012.08.208
37.
KularatneS. D. M. W.CasadoCÁRajalaJ., et al. (2024). FireMan-UAV-RGBT: A novel UAV-based RGB-thermal video dataset for the detection of wildfires in the Finnish forests. In 2024 IEEE 29th international conference on emerging technologies and factory automation (ETFA) (pp. 1–8). IEEE.
LeeS.-J.YunH.-S.SimY.-B., et al. (2025). Design and validation of an edge-AI fire safety system with SmartThings integration for accelerated detection and targeted suppression. Applied Sciences, 15, 8118. https://doi.org/10.3390/app15148118
40.
LiS.HanJ.ChenF., et al. (2024). Fire-Net: Rapid recognition of forest fires in UAV remote sensing imagery using embedded devices. Remote Sens (Basel, 16, 2846. https://doi.org/10.3390/rs16152846
41.
LiuF.Baijnath-RodinoJ. A.ChangT.-C., et al. (2023). DOME: Drone-assisted monitoring of emergent events for wildland fire resilience. In Proceedings of the ACM/IEEE 14th international conference on cyber-physical systems (with CPS-IoT week 2023) (pp. 56–67). ACM.
42.
MahdiA. S.MahmoodS. A. (2022). An edge computing environment for early wildfire detection. Annals of Emerging Technologies in Computing, 6, 56–68. https://doi.org/10.33166/AETiC.2022.03.005
MeimetisD.PapaioannouS.KatsoniP., et al. (2024). An architecture for early wildfire detection and spread estimation using unmanned aerial vehicles, base stations, and space assets. Drones and Autonomous Vehicles, 1, 10006–10006. https://doi.org/10.35534/dav.2024.10006
45.
MohapatraA.TrinhT. (2022). Early wildfire detection technologies in practice—A review. Sustainability, 14, 12270. https://doi.org/10.3390/su141912270
46.
MomeniM.Mirzapour Al-e-HashemS. M. J. (2024). Collaboration of thermal sensors and drones in fighting wildfires; mathematical model and heuristic approach. Internet of Things, 26, 101168. https://doi.org/10.1016/j.iot.2024.101168
47.
MuL.YangY.WangB., et al. (2025). Edge computing-based real-time forest fire detection using UAV thermal and color images. IEEE J Sel Top Appl Earth Obs Remote Sens, 18, 6760–6771. https://doi.org/10.1109/JSTARS.2025.3528652
48.
MuL.YangY.ZhangY., et al. (2025). A lightweight forest fire detection method based on UAV dual-modal images. IEEE Geoscience and Remote Sensing Letters, 22, 1–5. https://doi.org/10.1109/LGRS.2025.3580564
49.
MukhiddinovM.AbdusalomovA. B.ChoJ. (2022). A wildfire smoke detection system using unmanned aerial vehicle images based on the optimized YOLOv5. Sensors, 22, 9384. https://doi.org/10.3390/s22239384
50.
MukkamalaR.OlariuS.AljohaniM., et al. (2024). An adaptive drone-based multigenerational sensor system for monitoring large ecosystems. In 2024 20th international conference on distributed computing in smart systems and the internet of things (DCOSS-IoT) (pp. 361–368). IEEE.
51.
MuksimovaS.UmirzakovaS.MardievaS., et al. (2024). Revolutionizing wildfire detection through UAV-driven fire monitoring with a transformer-based approach. Fire, 7, 443. https://doi.org/10.3390/fire7120443
52.
NovacI.GeipelK. R.GilJ. d. D., et al. (2020). A framework for wildfire inspection using deep convolutional neural networks. In 2020 IEEE/SICE international symposium on system integration (SII) (pp. 867–872). IEEE.
53.
ParkM.TranD. Q.BakJ., et al. (2022). Advanced wildfire detection using generative adversarial network-based augmented datasets and weakly supervised object localization. International Journal of Applied Earth Observation and Geoinformation, 114, 103052. https://doi.org/10.1016/j.jag.2022.103052
RamadanM. N. A.AliM. A. H.KhooS. Y., et al. (2024). AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging. Results in Engineering, 24, 103277. https://doi.org/10.1016/j.rineng.2024.103277
56.
RamosL.CasasE.BendekE., et al. (2024). Hyperparameter optimization of YOLOv8 for smoke and wildfire detection: Implications for agricultural and environmental safety. Artificial Intelligence in Agriculture, 12, 109–126. https://doi.org/10.1016/j.aiia.2024.05.003
57.
RamosL. T.CasasE.RomeroC., et al. (2025). A study of YOLO architectures for wildfire and smoke detection in ground and aerial imagery. Results in Engineering, 26, 104869. https://doi.org/10.1016/j.rineng.2025.104869
58.
RedmonJ.DivvalaS.GirshickR., et al. (2016). You only Look once: Unified, real-time object detection. In 2016 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 779–788). IEEE.
59.
RjoubD.AlsharoaA.MasadehA. (2023). Unmanned-Aircraft-System-Assisted early wildfire detection with air quality sensors. Electronics (Basel, 12, 1239. https://doi.org/10.3390/electronics12051239
60.
RuiX.LiZ.ZhangX., et al. (2023). A RGB-thermal based adaptive modality learning network for day–night wildfire identification. International Journal of Applied Earth Observation and Geoinformation, 125, 103554. https://doi.org/10.1016/j.jag.2023.103554
61.
SaydirasulovichS. N.MukhiddinovM.DjuraevO., et al. (2023). An improved wildfire smoke detection based on YOLOv8 and UAV images. Sensors, 23, 8374. https://doi.org/10.3390/s23208374
62.
SeidaliyevaU.AkhmetovD.IlipbayevaL., et al. (2020). Real-Time and accurate drone detection in a video with a static background. Sensors, 20, 3856. https://doi.org/10.3390/s20143856
63.
ShamtaI.DemirB. E. (2024). Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV. PLoS One, 19, e0299058. https://doi.org/10.1371/journal.pone.0299058
64.
ShaoD.LiuY.LiuG., et al. (2025). YOLOv7scb: A small-target object detection method for fire smoke inspection. Fire, 8, 62. https://doi.org/10.3390/fire8020062
65.
ShiP.LuJ.WangQ., et al. (2023). An efficient forest fire detection algorithm using improved YOLOv5. Forests, 14, 2440. https://doi.org/10.3390/f14122440
66.
SinghA.PrakashK.MandaP. A., et al. (2024). Development of an autonomous UAS for on air surveillance and object detection: A real execution. Journal of Electrical Engineering & Technology, 19, 723–737. https://doi.org/10.1007/s42835-023-01573-1
67.
TalaatF. M.ZainEldinH. (2023). An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput Appl, 35, 20939–20954. https://doi.org/10.1007/s00521-023-08809-1
68.
Tavakol SadrabadiM.PeiróJ.InnocenteM. S., et al. (2025). Conceptual design of a wildfire emergency response system empowered by swarms of unmanned aerial vehicles. International Journal of Disaster Risk Reduction, 124, 105493. https://doi.org/10.1016/j.ijdrr.2025.105493
69.
TituM. F. S.PavelM. A.MichaelG. K. O., et al. (2024). Real-Time fire detection: Integrating lightweight deep learning models on drones with edge computing. Drones, 8, 483. https://doi.org/10.3390/drones8090483
70.
TzoumasG.PitonakovaL.SalinasL., et al. (2023). Wildfire detection in large-scale environments using force-based control for swarms of UAVs. Swarm Intelligence, 17, 89–115. https://doi.org/10.1007/s11721-022-00218-9
71.
WangG.LiH.LiP., et al. (2024). M SFWD: A multi-faceted synthetic dataset for remote sensing forest wildfires detection. Expert Syst Appl, 248, 123489. https://doi.org/10.1016/j.eswa.2024.123489
72.
WangS.WuM.WeiX., et al. (2025). An advanced multi-source data fusion method utilizing deep learning techniques for fire detection. Eng Appl Artif Intell, 142, 109902. https://doi.org/10.1016/j.engappai.2024.109902
73.
WangY.WangY.XuC., et al. (2024). Computer vision-driven forest wildfire and smoke recognition via IoT drone cameras. Wireless Networks, 30, 7603–7616. https://doi.org/10.1007/s11276-024-03718-0
74.
YandouziM.BerrahalM.GrariM., et al. (2024). Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones. Bulletin of Electrical Engineering and Informatics, 13, 2784–2796. https://doi.org/10.11591/eei.v13i4.7663
75.
YuanC.LiuZ.ZhangY. (2017). Aerial images-based forest fire detection for firefighting using optical remote sensing techniques and unmanned aerial vehicles. J Intell Robot Syst, 88, 635–654. https://doi.org/10.1007/s10846-016-0464-7
76.
YunB.ZhengY.LinZ., et al. (2024). FFYOLO: A lightweight forest fire detection model based on YOLOv8. Fire, 7, 93. https://doi.org/10.3390/fire7030093
77.
ZhangC.YangZ.ZhuoH., et al. (2023). A lightweight and drift-free fusion strategy for drone autonomous and safe navigation. Drones, 7, 34. https://doi.org/10.3390/drones7010034
78.
ZhangY.RuiX.SongW. (2025). A UAV-based multi-scenario RGB-thermal dataset and fusion model for enhanced forest fire detection. Remote Sens (Basel, 17, 2593. https://doi.org/10.3390/rs17152593
79.
ZhaoH.ZhouY.ZhangL., et al. (2020). Mixed YOLOv3-LITE: A lightweight real-time object detection method. Sensors, 20(7), 1861. https://doi.org/10.3390/s20071861
80.
ZhengH.DembéléS.WuY., et al. (2023). A lightweight algorithm capable of accurately identifying forest fires from UAV remote sensing imagery. Frontiers in Forests and Global Change, 6, 1134942. https://doi.org/10.3389/ffgc.2023.1134942
81.
ZhengY.ZhangG.TanS., et al. (2023). A forest fire smoke detection model combining convolutional neural network and vision transformer. Frontiers in Forests and Global Change, 6, https://doi.org/10.3389/ffgc.2023.1136969
82.
ZhouM.WuL.LiuS., et al. (2023). RETRACTED ARTICLE: UAV forest fire detection based on lightweight YOLOv5 model. Multimed Tools Appl, 83, 61777–61788. https://doi.org/10.1007/s11042-023-15770-7
83.
ZhuW.NiuS.YueJ., et al. (2025). Multiscale wildfire and smoke detection in complex drone forest environments based on YOLOv8. Sci Rep, 15, 2399. https://doi.org/10.1038/s41598-025-86239-w
