https://www.um.edu.mt/library/oar/handle/123456789/346 Tue, 15 Sep 2026 07:49:58 GMT 2026-09-15T07:49:58Z https://www.um.edu.mt/library/oar/handle/123456789/149091 Title: Iterative hybrid discrete-continuous viewpoint planning for UAV photogrammetry Authors: Grech, Alan; Pisani, Daniel; Grima, Andre; Debono, Carl James; Formosa, Saviour; Seychell, Dylan Abstract: Unmanned aerial vehicle (UAV) photogrammetry requires camera networks that provide sufficient surface coverage, image overlap, parallax, and resolution, yet conventional flight patterns are often poorly adapted to scene geometry, resulting in local reconstruction errors. This paper proposes an iterative hybrid discrete-continuous viewpoint planning method for targeted UAV photogrammetry from a proxy reconstruction. The method scores sampled surface points using photogrammetric heuristics based on frontality, imaging distance, parallax, and multi-view observation count, while also evaluating the full viewpoint set in terms of visibility, pairwise overlap, and graph connectivity. Candidate viewpoints are generated around weakly observed regions, refined using clustered Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimisation, and removed when redundant. The final flight path combines close-range detail viewpoints with wider model-coverage viewpoints, balancing local reconstruction quality with global image-network robustness. Evaluation on three synthetic scenes shows that the proposed method improves both reconstruction accuracy and completeness compared with prior UAV path-planning methods. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/149091 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/149083 Title: Enhancing object detection with privileged information : a model-agnostic teacher-student approach Authors: Bartolo, Matthias; Seychell, Dylan; Hili, Gabriel; Montebello, Matthew; Debono, Carl James; Formosa, Saviour; Makantasis, Konstantinos Abstract: This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a general, model-agnostic methodology for injecting privileged information-such as bounding box masks, saliency maps, and depth cues-into deep learning-based object detectors through a teacher-student architecture. Experiments are conducted across five state-of-the-art object detection models and multiple public benchmarks, including UAV-based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI-trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource-constrained and real-world settings. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/149083 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/149076 Title: Extracting those LiDAR pointclouds – a ML search for shapes Authors: Kozhakin, Alexey; Nguyen, Tram; Seychell, Dylan; Formosa Pace, Janice Abstract: Emerging in the 1960s, LiDAR technology has experienced significant development due to its undeniable advantages and is now widely applied across various fields. Its most successful integrations have been seen in geodesy, cartography, archaeology, environmental monitoring, and the automotive industry. Today, LiDAR has become an integral part of modern systems such as unmanned aerial vehicles (UAVs), drones, and augmented reality platforms. LiDAR has supplemented—and in some cases replaced—traditional methods of spatial data acquisition. Providing high measurement accuracy, these technologies enable the rapid collection of large volumes of data. Alongside the development of LiDAR itself, there has been substantial progress in methods for interpreting the resulting data. Traditional approaches such as statistical analysis, geostatistics, and geospatial analysis are widely used. However, intelligent solutions based on machine learning (ML) have recently become more prominent for deriving valuable analytical insights from LiDAR data. Wed, 01 Jan 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/149076 2025-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/149072 Title: Spatially mapping the EU funding mechanisms (FondiMap) Abstract: The Study focuses on the researching of, updating and redrafting of the spatial visualisation component of the current EU funding mapper that includes the 2014-2020 projects. It will result in the updating of the current webmap, the inclusion of the previous funding periods’ projects and the creation of a live mapping function for the current 2021-2027 programme. Mon, 01 Sep 2025 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/149072 2025-09-01T00:00:00Z