{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T10:08:58Z","timestamp":1790158138915,"version":"4.1.0"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: A wildfire smoke detector\u2019s practical worth is counted in minutes, not percentage points: an alarm raised ten minutes after ignition is worth far less than one raised at two, whatever the two systems\u2019 frame-level scores suggest. Time to detection, however, is almost always computed after training rather than optimized during it. (2) Methods: We treat incipient smoke not as an object to be recognized but as an onset to be caught: a faint, spatially coherent structure that thickens monotonically. At the same time, the rest of the scene continues behaving as it always has. The proposed study learns that habitual behavior from unlabeled fire-free video, surfaces departures from it as a per-tile prediction residual, and reads those residuals through an emergence field of four inspectable cues: residual magnitude, opacity accretion, motion coherence, and temporal persistence. Each common distractor fails a different cue, and detection delay and false alarms directly contribute to the loss. (3) Results: On FIgLib, the proposed study raises its first alarm 1.52 min after ignition while holding 0.21 false alarms per camera-day, and carries over to PyroNear-2025 and SKLFS-WildFire without labels from either. (4) Conclusions: Treating earliness as an optimization target rather than a reporting statistic yields detectors that stay both early and quiet on cameras they never saw in training.<\/jats:p>","DOI":"10.3390\/s26196020","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T09:20:46Z","timestamp":1790155246000},"page":"6020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Wildfire Smoke Onset from Fixed View Camera Networks"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6223-4502","authenticated-orcid":false,"given":"Shakhnoza","family":"Muksimova","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gachon University, Seongnam-si 13120, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Sabina","family":"Umirzakova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Chungbuk National University, Cheongju-si 28644, Chungcheongbuk-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1038\/s41558-024-02140-w","article-title":"Global burned area increasingly explained by climate change","volume":"14","author":"Burton","year":"2024","journal-title":"Nat. Clim. Change"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1038\/s41559-024-02452-2","article-title":"Increasing frequency and intensity of the most extreme wildfires on Earth","volume":"8","author":"Cunningham","year":"2024","journal-title":"Nat. Ecol. Evol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"eadl5889","DOI":"10.1126\/science.adl5889","article-title":"Global rise in forest fire emissions linked to climate change in the extratropics","volume":"386","author":"Jones","year":"2024","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6390","DOI":"10.1038\/s41467-025-61608-1","article-title":"Climate change has increased the odds of extreme regional forest fire years globally","volume":"16","author":"Abatzoglou","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41612-024-00575-8","article-title":"Europe faces up to tenfold increase in extreme fires in a warming climate","volume":"7","author":"Barnard","year":"2024","journal-title":"npj Clim. Atmos. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102369","DOI":"10.1016\/j.inffus.2024.102369","article-title":"A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management","volume":"108","author":"Boroujeni","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bugari\u0107, M., Krstini\u0107, D., \u0160eri\u0107, L., and Stipani\u010dev, D. (2025). Current trends in wildfire detection, monitoring and surveillance. Fire, 8.","DOI":"10.3390\/fire8090356"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111958","DOI":"10.1016\/j.engappai.2025.111958","article-title":"An efficient multi-task forest fire and smoke detection model","volume":"160","author":"Chen","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_9","first-page":"1","article-title":"RFWNet: A multiscale remote sensing forest wildfire detection network with digital twinning, adaptive spatial aggregation, and dynamic sparse features","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111528","DOI":"10.1016\/j.knosys.2024.111528","article-title":"SMWE-GFPNNet: A high-precision and robust method for forest fire smoke detection","volume":"289","author":"Li","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3351","DOI":"10.1007\/s10694-025-01733-x","article-title":"Recognition of forest fire smoke based on improved yolov8n model","volume":"61","author":"Chen","year":"2025","journal-title":"Fire Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6493","DOI":"10.1007\/s00371-023-03178-1","article-title":"Improving YOLOX network for multi-scale fire detection","volume":"40","author":"Wang","year":"2024","journal-title":"Vis. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dewangan, A., Pande, Y., Braun, H.W., Vernon, F., Perez, I., Altintas, I., Cottrell, G.W., and Nguyen, M.H. (2022). FIgLib & SmokeyNet: Dataset and deep learning model for real-time wildland fire smoke detection. Remote Sens., 14.","DOI":"10.3390\/rs14041007"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"128963","DOI":"10.1016\/j.neucom.2024.128963","article-title":"10-minute forest early wildfire detection: Fusing multi-type and multi-source information via recursive transformer","volume":"616","author":"Zhang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110848","DOI":"10.1016\/j.engappai.2025.110848","article-title":"From smoke to fire: A forest fire early warning and risk assessment model fusing multimodal data","volume":"152","author":"Jin","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"111021","DOI":"10.1016\/j.patcog.2024.111021","article-title":"Video anomaly detection via self-supervised and spatio-temporal proxy tasks learning","volume":"158","author":"Yang","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/TIP.2025.3558089","article-title":"Crcl: Causal representation consistency learning for anomaly detection in surveillance videos","volume":"34","author":"Liu","year":"2025","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2024.12.025","article-title":"Intelligent segmentation of wildfire region and interpretation of fire front in visible light images from the viewpoint of an unmanned aerial vehicle (UAV)","volume":"220","author":"Li","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1038\/s41597-025-05634-0","article-title":"Boreal Forest Fire: UAV-collected wildfire detection and smoke segmentation dataset","volume":"12","author":"Pesonen","year":"2025","journal-title":"Sci. Data"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123489","DOI":"10.1016\/j.eswa.2024.123489","article-title":"M4SFWD: A Multi-Faceted synthetic dataset for remote sensing forest wildfires detection","volume":"248","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1610145","DOI":"10.1155\/int\/1610145","article-title":"Wildfire smoke detection system: Model architecture, training mechanism, and dataset","volume":"2025","author":"Wang","year":"2025","journal-title":"Int. J. Intell. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lostanlen, M., Isla, N., Guillen, J., Zanca, R., Veith, F., Buc, C., and Barriere, V. (2024). Constructing a Real-World Benchmark for Early Wildfire Detection with the New PYRONEAR-2025 Dataset. arXiv.","DOI":"10.3390\/electronics15122652"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TCSVT.2023.3297114","article-title":"Learning appearance-motion synergy via memory-guided event prediction for video anomaly detection","volume":"34","author":"Guo","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"111595","DOI":"10.1016\/j.patcog.2025.111595","article-title":"MPE: Multi-frame prediction error-based video anomaly detection framework for robust anomaly inference","volume":"164","author":"Kim","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112010","DOI":"10.1016\/j.patcog.2025.112010","article-title":"Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention","volume":"170","author":"Lyu","year":"2026","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107115","DOI":"10.1016\/j.neunet.2024.107115","article-title":"SSIM over MSE: A new perspective for video anomaly detection","volume":"185","author":"Fan","year":"2025","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TIP.2024.3372466","article-title":"Context recovery and knowledge retrieval: A novel two-stream framework for video anomaly detection","volume":"33","author":"Cao","year":"2024","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11751","DOI":"10.1109\/TCSVT.2024.3420775","article-title":"Produce once, utilize twice for anomaly detection","volume":"34","author":"Wang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26321","DOI":"10.1007\/s00521-025-11659-8","article-title":"Video anomaly detection in 10 years: A survey and outlook","volume":"37","author":"Abdalla","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Karakonstantis, I., and Xylomenos, G. (2026). A Review of Two-Dimensional Cellular Automata Models for Wildfire Simulation: Methods, Capabilities, and Limitations. Fire, 9.","DOI":"10.3390\/fire9030108"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T09:31:32Z","timestamp":1790155892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,23]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196020"],"URL":"https:\/\/doi.org\/10.3390\/s26196020","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,23]]}}}