AI

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 3277 manually annotated posts from two fire seasons, the first publicly released, agreement-audited Greek wildfire corpus to our knowledge, 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, while being faster and free of API costs. 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.

Artificial Intelligence (AI)-enabled systems are now increasingly deployed across safety-critical, mission-critical, and socio-technical domains. Existing engineering disciplines offer mature approaches to human and software assurance; however, AI-enabled systems can exhibit characteristics such as probabilistic behaviour, data dependency, limited explainability, and adaptation that challenge traditional assurance methods. Our research examines assurance precepts across three domains that arguably dominated assurance reforms in capability development during different periods: (1) human assurance, circa 1980 to 2000, (2) software assurance, circa 2000 to 2020, and (3) assurance of AI-enabled systems, emerging since 2020. While software assurance is largely concerned with verifying deterministic behaviour against specified requirements and human assurance focuses on managing variability in socio-technical systems, AI assurance must address systems whose behaviour is learned, probabilistic, and inherently dependent on data. We contribute a set of 20 assurance precepts for safety-critical AI-enabled capabilities. To extend current assurance frameworks, we structured these precepts across four quadrants of assurance activities, responsibilities, and critical questions, and organized them within a novel Dual Assurance Spiral for AI-enabled Capabilities (DAS4AIC). We propose the DAS4AIC to operationalise the 20 AI assurance precepts throughout capability development and sustainment. Further, we demonstrate how classical safety precepts map from human-dominant assurance through software-dominant assurance to the assurance of AI-enabled systems with conceptual validation. Our preliminary evaluation is that some human assurance precepts map more directly to AI-enabled systems than through the intermediate step of software assurance, suggesting that established human assurance concepts may have continuing relevance for AI-enabled capabilities. To our knowledge, this is the first study to align emerging AI-assurance precepts explicitly with the earlier and still overlapping, human-dominant and software-dominant assurance paradigms.

Residue-soil mixing and reduced spectral sensitivity complicate summer maize leaf area index (LAI) estimation from unmanned aerial vehicle multispectral imagery. We compared full-plot and canopy domains, paired differences, neighborhood statistics, and approximate days after sowing (DAS), calculated from a common reference date. Five input schemes (B₀–B₄) and four algorithms were evaluated using 345 samples collected on five dates in one growing season. Spatial validation comprised ten repetitions of five-fold grouped cross-validation, with preprocessing and feature selection restricted to training data. For gradient boosting decision trees, R² increased from 0.515 for B₀ to 0.686 for B₃ and 0.846 for B₄, whereas RMSE decreased from 0.834 to 0.671 and 0.470. Low-LAI Bias decreased from 0.580 to 0.343 and 0.160. Fixed-capacity controls supported conditional improvements after adding paired differences and neighborhood statistics. Approximate DAS produced the largest sequential RMSE reduction, mainly through between-date adjustment. Time-baseline and training-fold residual analyses distinguish calendar-time adjustment from within-date predictive information. High-LAI underestimation and unseen-date errors delimit application beyond the sampled dates and production-field conditions. Within this defined scope, the framework provides a reproducible comparison of image-domain representations under heterogeneous production conditions.