Browsing by Author "Pittaluga, Simone"

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    3D Modeling and Integration of Heterogeneous Geo-data
    (The Eurographics Association, 2021) Miola, Marianna; Cabiddu, Daniela; Pittaluga, Simone; Mortara, Michela; Vetuschi Zuccolini, Marino; Imitazione, Gianmario; Frosini, Patrizio and Giorgi, Daniela and Melzi, Simone and Rodolà, Emanuele
    This paper tackles the volumetric representation of geophysical and geotechnical data, gathered during exploration surveys of the subsoil. The creation of a 3D model as support to geological interpretation has to take into account the specificity of the diverse input data, that are heterogeneous. Some data are massive, but cover the domain unevenly, e.g., structured along dense differently spaced lines, while others are very sparse, e.g., borehole locations with soil sampling and CPTU (Piezocone Penetration Test) locations. In this work, we focus on the exploration and analysis of underwater deposits. After a discussion about the data typically acquired in an offshore campaign, we present an automatic process to generate the subsurfaces and volume defining an underground deposit, starting from the identification of relevant morphological features in seismic data. In particular, data simplification and refinement based on geostatistics have been applied to generate regular 2D meshes from strongly anisotropic data, in order to improve the quality of the final 3D tetrahedral mesh. Furthermore, we also use geostatistics to predict geotechnical parameters from local surveys and estimate their distribution on the whole domain: in this way the 3D model will include relevant geological features of the deposit and allow extrapolating different geotechnical information with associated uncertainty. The volume characterization and its 3D inspection will improve the structural and stratigraphic interpretation of deposits, to support geological analysis and planning of future engineering activities.
  • Thumbnail Image
    Item
    Advancing Environmental Modeling with Unstructured Meshes: Current Research and Development
    (The Eurographics Association, 2024) Miola, Marianna; Cabiddu, Daniela; Mortara, Michela; Pittaluga, Simone; Sorgente, Tommaso; Zuccolini, Marino Vetuschi; Caputo, Ariel; Garro, Valeria; Giachetti, Andrea; Castellani, Umberto; Dulecha, Tinsae Gebrechristos
    Modeling the distribution of environmental variables across spatial domains presents significant challenges. Geostatistics offers a robust set of tools for accurately predicting values and associated uncertainties at unsampled locations, accounting for spatial correlations. However, these tools are often constrained by their reliance on structured domain representations, limiting their flexibility in modeling complex or irregular structures. By exploring the use of unstructured meshes, we can achieve a more efficient and accurate representation of localized phenomena, thereby enhancing our ability to model spatial patterns. Our current efforts are focused on integrating unstructured meshes into the geostatistical modeling pipeline, encompassing everything from mesh generation (and possibly refinement) to their application in stochastic simulation and the segmentation of the domain into regions where the distribution of variables is homogeneous. Preliminary results are promising, demonstrating the potentialities of this innovative approach.
  • Thumbnail Image
    Item
    Mobile Laser Scanning of Challenging Urban Sites: a Case Study in Matera
    (The Eurographics Association, 2022) Scalas, Andreas; Cabiddu, Daniela; Mortara, Michela; Pittaluga, Simone; Spagnuolo, Michela; Ponchio, Federico; Pintus, Ruggero
    The creation of 3D models of heritage and architectural sites requires proper technologies able to capture a wide area at fine geometric and appearance detail. In this paper we address the acquisition and digitization of three challenging Points of Interest in Matera, Italy. The sites, both outdoor and indoor, are characterised by limited accessibility, complex morphology and poor lighting conditions. We describe our experience with a portable, lightweight laser scanner, describing the planning, acquisition and post-processing phases, and providing some lessons learnt in order to achieve good results in terms of quality and resolution.
  • No Thumbnail Available
    Item
    MUSE: Modeling Uncertainty as a Support for Environment
    (The Eurographics Association, 2022) Miola, Marianna; Cabiddu, Daniela; Pittaluga, Simone; Vetuschi Zuccolini, Marino; Cabiddu, Daniela; Schneider, Teseo; Allegra, Dario; Catalano, Chiara Eva; Cherchi, Gianmarco; Scateni, Riccardo
    To fully understand a Natural System, the representation of an environmental variable's distribution in 3D space is a mandatory and complex task. The challenge derives from a scarcity of samples number in the survey domain (e.g., logs in a reservoir, soil samples, fixed acquisition sampling stations) or an implicit difficulty in the in-situ measurement of parameters. Field or lab measurements are generally considered error-free, although not so. That aspect, combined with conceptual and numerical approximations used to model phenomena, makes the results intrinsically less performing, fading the interpretation. In this context, we design a computational infrastructure to evaluate spatial uncertainty in a multi-scenario application in Environment survey and protection, such as in environmental geochemistry, coastal oceanography, or infrastructure engineering. Our Research aims to expand the operative knowledge by developing an open-source stochastic tool, named MUSE, the acronym for Modeling Uncertainty as a Support for Environment. At this stage, the methodology mainly includes the definition of a flexible environmental data format, a geometry processing module to discretize the space, and geostatistics tools to evaluate the spatial continuity of sampled parameters, predicting random variable distribution. The implementation of the uncertainty module and the development of a graphic interface for ad-hoc visualization will be integrated as the next step. The poster summarizes research purposes, and MUSE computational code structure developed so far.
  • No Thumbnail Available
    Item
    Semantic Segmentation of High-resolution Point Clouds Representing Urban Contexts
    (The Eurographics Association, 2023) Romanengo, Chiara; Cabiddu, Daniela; Pittaluga, Simone; Mortara, Michela; Banterle, Francesco; Caggianese, Giuseppe; Capece, Nicola; Erra, Ugo; Lupinetti, Katia; Manfredi, Gilda
    Point clouds are becoming an increasingly common digital representation of real-world objects, and they are particularly efficient when dealing with large-scale objects and/or when extremely high-resolution is required. The focus of our work is on the analysis, 3D feature extraction and semantic annotation of point clouds representing urban scenes, coming from various acquisition technologies, e.g., terrestrial (fixed or mobile) or aerial laser scanning or photogrammetry; the task is challenging, due to data dimensionality and noise. In particular, we present a pipeline to segment high-resolution point clouds representing urban environments into geometric primitives; we focus on planes, cylinders and spheres, which are the main features of buildings (walls, roofs, arches, ...) and ground surfaces (streets, pavements, platforms), and identify the unique parameters of each instance. This paper focuses on the semantic segmentation of buildings, but the approach is currently being generalised to manage extended urban areas. Given a dense point cloud representing a specific building, we firstly apply a binary space partitioning method to obtain small enough sub-clouds that can be processed. Then, a combination of the well-known RANSAC algorithm and a recognition method based on the Hough transform (HT) is applied to each sub-cloud to obtain a semantic segmentation into salient elements, like façades, walls and roofs. The parameters of primitive instances are saved as metadata to document the structural element of buildings for further thematic analyses, e.g., energy efficiency. We present a case study on the city of Catania, Italy, where two buildings of historical and artistic value have been digitized at very high resolution. Our approach is able to semantically segment these huge point clouds and it proves robust to uneven sampling density, input noise and outliers.