Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications.
S. Verykokou, C. Ioannidis, C. Potsiou et al.· Italian National Conference...· 0 citations
Septoria leaf blotch (SLB), caused by Zymoseptoria tritici, is one of the most destructive foliar diseases of wheat and requires accurate methods for early detection and disease severity assessment. This study evaluated the potential of hyperspectral ASD measurements and UAV multispectral imagery combined with machine learning for the detection and quantification of SLB in winter wheat. Six spectral datasets derived from hyperspectral reflectance, UAV multispectral imagery, and vegetation indices were analyzed using CatBoost, Random Forest, and XGBoost algorithms. Random Forest achieved the highest classification performance, reaching an accuracy of 0.9583 and a balanced accuracy of 0.9483. For disease severity prediction, the best performance was obtained using ASD-derived vegetation indices with Random Forest (R2 = 0.9199), while CatBoost consistently provided high regression accuracy across hyperspectral datasets. A reduced set of green, red, red-edge, and near-infrared bands produced classification results comparable to those obtained with the full hyperspectral spectrum, indicating that most diagnostic information is concentrated within these spectral regions. Although UAV multispectral data showed lower accuracy than hyperspectral measurements, particularly for disease severity prediction, they enabled effective field-scale disease monitoring. These findings demonstrate that hyperspectral sensing provides a valuable reference for developing accurate disease detection models, whereas UAV multispectral imagery represents a practical and scalable solution for operational precision agriculture.
Andrzej Wójtowicz, Jan Piekarczyk, Marek Wójtowicz et al.· Remote Sensing· 0 citations
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