People as Sensors: An End-to-End Deep Learning Framework for Near-Real-Time Wildfire Detection, Classification, and Geospatial Intelligence from Greek Social Media

Source abstract

Citizens' social media posts can provide rapid, complementary situational information during emerging wildfires, alongside conventional sensing and official reporting. For Greek, no annotated wildfire corpus or end-to-end pipeline existed, and most systems stop at binary detection or depend on geotags. We present an end-to-end Greek-language NLP pipeline, a research prototype that turns keyword-retrieved, unlabeled X posts into a geolocated, near-real-time wildfire map: it detects active fires, types them as wildland, urban, or mixed, resolves toponyms, and clusters reports into incidents. On 3,277 manually annotated posts from two fire seasons, to our knowledge the first publicly released, agreement-audited Greek wildfire corpus, fine-tuned transformers reached F2 = 0.911 for detection and macro-F1 = 0.874 for fire type, outperforming classical baselines and, under the tested prompting conditions, two large language models, faster and at zero API cost. On a chronological hold-out of the unseen 2025 season, detection retains F2 = 0.850 (classification recall 0.907); fire-type classification degrades more (macro-F1 0.769 ± 0.085). Spatiotemporal toponym disambiguation raised geocoding accuracy from 54.68% to 80.58% (p < 0.001); a repeatability test revealed gazetteer drift. In a retrospective replay of 12 August 2024 posts, it resolved hundreds of reports of an Attica wildfire into one incident cluster, complementing emergency calls.