Conference Agenda

Recreational fishing supports economies, wellbeing, and connection to the marine environment but can pressure fish stocks. Traditional monitoring in New Zealand is costly, sporadic, and self-reported. This study evaluates integrating mobile phone data (MPD) and satellite-based object detection (YOLO on Sentinel-2 and sub-meter imagery) to improve monitoring. MPD provides temporal coverage but is biased, while satellite imagery offers spatial validation but provides only snapshots. Combining these datasets mitigates biases and gaps, enabling more accurate, representative estimates of fishing activity. This is the first study to integrate these approaches, demonstrating the potential of hybrid methods for scalable, cost-effective recreational fisheries monitoring.

The aggregation of building footprints is a key task of cartographic generalization, which is an important topic in geoinformation science. It has been approached from various angles, ranging from heuristics and optimization algorithms to machine learning. Given a set of input polygons that represent the building footprints, the task is to generate a set of polygons that provide a coarser representation of the input. The problem has applications in the visualization of settlement areas in small-scale maps, as well as settlement classification and analysis. A popular solution approach is to construct a subdivision of the plane and then build a solution by selecting faces from the subdivision. Often, a triangulation is used for the subdivision. However, this can cause the orientations of the boundary edges in the solution to differ drastically from the input polygons, which leads to a loss of information about the underlying settlement structure. We explore an alternative method that constructs the subdivision by extending the input building edges, thereby automatically preserving their orientations. To make the approach scalable to large instances without substantially decreasing the solution quality, we propose different methods of reducing the complexity of the subdivision. Our experimental evaluation on real-world data shows that our method is able to aggregate towns containing up to approximately 10 000 building footprints while preserving input edge orientations much better than state-of-the-art methods.

Timely updates of lane-level dynamic information are crucial for intelligent vehicle path planning and driving safety. Most existing crowdsourced map update methods lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. We propose a novel method for updating lane-level dynamic information in HD maps based on crowdsourced data. First, a hybrid modelling multi-object detection method is used to reliably perceived lane markings and traffic cones. To address the issues of false detection and missed detection in single-vehicle perception, a multi-vehicle probabilistic fusion algorithm is proposed, which explicitly models perceptual uncertainty to effectively mitigate the impact of missed and false detections, enabling accurate, robust, and real-time detection of dynamic information such as temporary lane closures.To validate the effectiveness and accuracy of the proposed method, we conducted experiments in the Intelligent and Connected Vehicle Demonstration Zone in Wuhan.Experiments comparing single-vehicle and multi-vehicle fusion results demonstrate the effectiveness of the proposed method in enhancing detection performance.

This study uses machine learning and geospatial science to investigate how climate change may affect the foraging and habitat suitability of little penguins Eudyptula minor in Australia and New Zealand. An innovative modeling approach was followed here to identify favorable climatic conditions for the species across both regions. The model trained on Australian occurrence data was projected to New Zealand, and vice versa, to assess cross-regional habitat suitability and potential range shifts under changing climate conditions. This is to further evaluate adaptive potential and determine whether transoceanic relocation would be feasible in the event of local extinction. The study evaluated habitat suitability using the ML model and climate variables from the WorldClim dataset. The findings showed that the healthy habitat of little penguins is significantly shaped by temperature-related bioclimatic variables, especially temperature annual range. According to the models, the habitat suitability of little penguins varies between the two nations, with Australia offering the little penguins of New Zealand less hospitable conditions. But the New Zealand is predicted to offer relatively better habitat to Australia-based little penguins. This study offers vital information for conservation strategies by highlighting the possible changes in penguin populations brought on by climate change. A promising tool for comprehending how the climate affects marine ecosystems is provided by this study.

This paper presents an advanced computational framework developed to support decision-making for the placement of the underground Einstein Telescope, a third-generation gravitational-wave observatory. The system aims to automate the search for an optimal location through a multi-criteria analysis approach. Because the ET is extremely sensitive to environmental noise sources—including seismic, thermal, and anthropogenic vibrations—its design prioritises underground construction. This strategy, also adopted for the Japanese KAGRA detector and in contrast to surface-based observatories such as LIGO and Virgo, minimises interference from surface activities while ensuring subsurface stability.

The proposed methodology integrates Geographic Information System (GIS) data, incorporating a Digital Surface Model (DSM) to spatially represent relevant factors. The dominant site-selection criteria were identified and weighted according to their scientific and strategic importance in collaboration with the ET scientific community. An interactive parametric model was developed to interface directly with the GIS data, enabling evaluation of key factors and providing real-time analytical feedback on placement scenarios. Using an evolutionary algorithm combined with a composite fitness function, the system balances competing objectives and delivers optimised solutions, offering a robust decision-support tool for the early planning stages of the Einstein Telescope project.

Although the Sardinia site is currently considered a preliminary case study, the methodology is generalisable and applicable to other candidate sites to host ET

In fractured rock slopes, the geometric configuration and spatial arrangement of unstable rock blocks are fundamentally governed by the intersection of multiple joint sets. The mechanical weakening along these joints markedly reduces the integral strength of the rock mass and establishes potential kinematic release boundaries. This study establishes an in-situ hazardous-rock detection and characterization framework utilizing high resolution three-dimensional point cloud acquired under realistic topographic conditions. This method first examines the spatial interaction between joints and slope morphology, and incorporates explicit kinematic criteria to automatically identify structural combinations capable of different failures. Consequently, the spatial positions and distribution patterns of potentially unstable blocks are delineated within the point cloud. Subsequently, point cloud differencing is employed to achieve volumetric extraction and statistical classification of block sizes, enabling quantitative characterization of block volume and elevation across the source areas. Representative blocks are then selected as initial release elements, with their actual geometrical and volumetric attributes incorporated into rockfall simulations. This allows for the computation of key kinematic parameters including rockfall frequency, bounce height, velocity, and kinetic energy. Overall, the presented approach delivers a scalable pathway for rapid detection, quantitative assessment, and hazard evaluation of structurally controlled rockfalls in complex mountainous terrain. The results provide technical support and decision insights for the safe operation and disaster-resilient planning of transportation infrastructure.