Shallow bathymetry mapping using proximal sensing techniques is an active field of research that offers a new perspective in studying the seafloor. Drone-based imagery with centimeter resolution allows for bathymetry retrieval in unprecedented detail in areas with adequate water transparency. The majority of studies apply either spectral or photogrammetric techniques for deriving bathymetry from remotely sensed imagery. However, spectral methods require a certain amount of ground-truth depth data for model calibration, while photogrammetric methods cannot perform on texture-less seafloor types. The presented approach takes advantage of the interrelation of the two methods, in order to predict bathymetry in a more efficient way. Thus, we combine structure-from-motion (SfM) outputs along with band-ratios of radiometrically corrected drone images within a specially designed deep convolutional neural network (CNN) that outputs a reliable and robust bathymetry estimation. To achieve effective training of our deep learning system, we utilize interpolated uncrewed surface vehicle (USV) sonar measurements. We perform several predictions at three locations in the southern Mediterranean Sea, with varying seafloor types. Our results show low root-mean-square errors over all study areas (average RMSE ≅ 0.3 m), when the method was trained and tested on the same area each time. In addition, we obtain promising cross-validation performance across different study areas (average RMSE ≅ 0.9 m), which demonstrates the potential of our proposed approach in terms of generalization capabilities on unseen data. Furthermore, areas with mixed seafloor types are suitable for building a model that can be applied in similar locations where only drone data is available.

A new method for object tracking in video sequences is presented. This method exploits the benefits of particle filters to tackle the multimodal distributions emerging from cluttered scenes. The tracked object is described by several models of different complexity, which are probabilistically linked together. The parameter update for each model takes place hierarchically so that the simpler models, which are updated first, can guide the search in the parameter space of the more complex models to relevant regions. This strategy improves the target representation because of the multiple models and reduces the overall complexity. The likelihood for each object model is calculated using one or more visual cues thus increasing the robustness of the proposed algorithm. Our method is evaluated by fusing on salient points and contour models and we demonstrate its effectiveness.

Traditional craft education relies heavily on hands-on practice; however, novice learners often struggle with procedural complexity, material behavior, and the tacit knowledge typically transmitted through prolonged apprenticeship. This paper presents an integrated framework that combines semantic Knowledge Graphs (KGs), real-time Finite Element Method (FEM) simulation, and high-fidelity physically based rendering (PBR) to support the teaching, understanding, and preservation of traditional crafts. Craft processes are modelled as ontologically grounded KGs that capture tools, materials, actions, decision points, and common procedural errors through an extensible representation aligned with CIDOC-CRM. These semantic structures drive an interactive FEM-based simulation that enables learners to enact craft actions in a virtual environment while receiving predictive feedback and corrective guidance derived from expert-defined execution parameters. The resulting workpiece states are visualized using PBR techniques, providing perceptually accurate cues essential for assessing surface changes, deformation patterns, and material conditions. The methodology is embedded within an eLearning ecosystem that supports the generation of structured courses, multimodal exemplars, and instructional design informed by Cognitive Load Theory. A use case involving wood and aluminum carving demonstrates the system’s ability to simulate realistic tool–material interactions and produce visually interpretable outcomes. The results indicate that coupling executable semantic knowledge modelling with physically grounded simulation offers a viable pathway toward scalable, safe, and contextually rich craft training while supporting the long-term preservation of domain expertise.

In this work, we introduce the first pipeline that combines a refraction-aware structure from motion (SfM) method with a deep learning model specifically designed for airborne bathymetry. We accurately estimate the 3D positions of the submerged points by integrating refraction geometry within the SfM optimization problem. This way, no refraction correction as post-processing is required. Experiments with simulated data that approach real-world capturing conditions demonstrate that SfM with refraction correction is extremely accurate, with submillimeter errors. We integrate our refraction-aware SfM within a deep learning framework that also takes into account radiometrical information, developing a combined spectral and geometry-based approach, with further improvements in accuracy and robustness to different seafloor types, both textured and textureless. We conducted experiments with real-world data at two locations in the southern Mediterranean Sea, with varying seafloor types, which demonstrate the benefits of refraction correction for the deep learning framework. We made our refraction-aware SfM open source, providing researchers in airborne bathymetry with a practical tool to apply SfM in shallow water areas.