1,720,990 research outputs found
An original algorithm for bim generation from indoor survey point clouds
Full text linkNowadays, it is essential to find new strategies, able to perform the first step of the scan-to-BIM process, by retrieving the geometrical information contained in point clouds that are so easily collected through laser scanners and range cameras. This paper presents a new algorithm for the automatic extraction of the layout and the height of a small indoor environment from its point cloud. In particular, the algorithm was tested on a point cloud of 600000 vertices, selected from the dataset of the ISPRS benchmark on indoor modelling. The preliminary results are encouraging: the 3D shape (layout and height) of the investigated room is effectively reconstructed
An Open Source Ransac-Based Plug-In for Unsupervised Building Roof Extraction from LiDAR Point Clouds
No full textThis work presents a Plug-In for the Opticks open source soft-ware implementing an unsupervised workflow for building roof extraction from Light Detection and Ranging (LiDAR) data. In particular, a computer vision approach is employed to segment the points belonging to different objects (buildings, trees, etc.), whereas the RANSAC algorithm, the core of the proposed workflow, is used recursively for identifying the buildings and to model their roofs. The preliminary re-sults, qualitatively evaluated, are encouraging: the proposed roof extraction workflow works generally well-the 80% of the roofs are completely or partially modeled-but shows some issues with buildings characterized by several pitches with low slopes and/or located in proximity of dense vegetation
3DCD: a new dataset for 2d and 3d change detection using deep learning techniques
Full text linkChange detection is one of the main topics in Earth Observation, due to its wide range of applications, varying from urban development monitoring to natural disaster management. Most of the recently developed change detection methodologies rely on the use of deep learning algorithms. These kinds of algorithms are generally focused on generating two-dimensional (2D) change maps, thus they are only able to detect horizontal changes in land use/land cover, not considering nor returning any information on the corresponding elevation changes. Our work proposes a step forward, creating and sharing a dataset where two optical images acquired in different epochs are provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes. Particularly, our aim is to provide a dataset useful to address and possibly solve the change detection task in 3D. Indeed, the proposed dataset, on the one hand, can empower a further development of 2D change detection algorithms, and, on the other hand, can allow to develop algorithms able to provide 3D change detection maps from two optical images captured in different epochs, without the need to rely directly on elevation data as input. The proposed dataset is publicly available at the following link: https://bit.ly/3wDdo41
A high-resolution photogrammetric workflow based on focus stacking for the 3D modeling of small Aegean inscriptions
Full text linkAny attempt of decipherment and language identification of the scripts from the Aegean dating to the second millennium BCE (namely Cretan Hieroglyphic, Linear A, and Cypro-Minoan) has relied, until today, on traditional catalogues of inscriptions, consisting of incomplete or subjective 2D representations, such as photographs and hand-drawn copies, which are not suitable for documenting such three-dimensional writing systems. In contrast, 3D models of the inscribed media allow for an accurate and objective “autopsy” of the entire surface of the inscriptions. In this context, this work presents an efficient, accurate, high-resolution, and high-quality texture photogrammetric workflow based on focus-stacked macro images, designed for the 3D modeling of small Aegean inscriptions, to properly reconstruct their geometry and to enhance the identification of their signs, making their transcription as unbiased as possible. The pipeline we propose also benefits from a pre-processing stage to remove any coloration difference from the images, and a reliable and simple 3D scaling procedure. We tested this workflow on six inscribed artifacts (two in Cretan Hieroglyphic, three in Linear A, one of uncertain affiliation), whose average size ranges approximately from 1 to 3 cm. Our results show that this workflow achieved an accuracy of a few hundredths of mm, comparable to the technical specifications of standard commercial 3D scanners. Moreover, the high 3D density we obtained (corresponding to the edge average length of the 3D model mesh), up to ≈ 30 μm, allowed us to reconstruct even the smallest details of the inscriptions, both in the mesh and in the texture layer of the 3D models
SWOT Level 2 Lake Single-Pass Product: The L2_HR_LakeSP Data Preliminary Analysis for Water Level Monitoring
Full text linkThe Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, aims to address the crucial environmental goal of water monitoring to support preparedness for extreme events and facilitate adaptation to climate change on global and local scales. This mission will provide a comprehensive inventory of worldwide water resources, lakes, reservoir storage, and river dynamics. In this work, we carried out a preliminary assessment of SWOT’s Lake product Level 2 version 1.1, also known as “L2_HR_LakeSP”. The analysis was performed across six diverse lakes on three continents, revealing an average median bias of 0.08 m with respect to the considered reference, after suitable outlier removal. An overall precision of 0.22 m was found, combined with an average correlation of 68% between SWOT and reference time series. Moreover, the accuracy varied in the considered six lakes, since biases up to some decimeters were found for some of them; they could be due to residual inconsistencies between the vertical reference frame of SWOT and that of the considered reference. In summary, the first analysis of the “L2_HR_LakeSP” product, Version 1.1, demonstrated the promising potential of SWOT for monitoring seasonal variations in water levels. Nevertheless, notable anomalies were found in the water masks, particularly in higher latitudes, suggesting potential difficulties in accurately delineating water bodies in those regions. Additionally, a discernible reduction in accuracy was observed towards the end of the monitoring period. These preliminary findings indicate some issues that should be addressed in future investigations about the quality and potential of SWOT’s lake products for advancing our understanding of global water dynamics
First Test of Agisoft Metashape Satellite Image Processing for DSM Generation. A Case Study in Trento with Pléiades Imagery
No full textAs to the problem of DSM generation using satellite imagery, the latest version (1.6) of Agisoft Metashape - previously known as Photoscan - is one of the latest solutions, which follows other open source and commercial software tools, able to complete the task of extracting a discrete representation of the Earth surface from satellite sensor products. In order to quantitatively assess the new functionality implemented by Agisoft Metashape 1.6, this works presents an accuracy evaluation of the DSMs generated through a dataset consisting of a triplet of Pléiades images acquired on August 28, 2012. These images cover the area of Trento and the Adige valley, characterized by a great variety in terms of geomorphology, land uses and land covers. In addition to the accuracy assessment, consisting of a statistical analysis of the height discrepancies between the generated DSMs and a LiDAR DSM used as reference, the effectiveness of the software matching strategy and its efficiency are highlighted
A high-resolution photogrammetric workflow based on focus stacking for the 3D modeling of small Aegean inscriptions
Full text linkAny attempt of decipherment and language identification of the scripts from the Aegean dating to the second millennium BCE (namely Cretan Hieroglyphic, Linear A, and Cypro-Minoan) has relied, until today, on traditional catalogues of inscriptions, consisting of incomplete or subjective 2D representations, such as photographs and hand-drawn copies, which are not suitable for documenting such three-dimensional writing systems. In contrast, 3D models of the inscribed media allow for an accurate and objective “autopsy” of the entire surface of the inscriptions. In this context, this work presents an efficient, accurate, high-resolution, and high-quality texture photogrammetric workflow based on focus-stacked macro images, designed for the 3D modeling of small Aegean inscriptions, to properly reconstruct their geometry and to enhance the identification of their signs, making their transcription as unbiased as possible. The pipeline we propose also benefits from a pre-processing stage to remove any coloration difference from the images, and a reliable and simple 3D scaling procedure. We tested this workflow on six inscribed artifacts (two in Cretan Hieroglyphic, three in Linear A, one of uncertain affiliation), whose average size ranges approximately from 1 to 3 cm. Our results show that this workflow achieved an accuracy of a few hundredths of mm, comparable to the technical specifications of standard commercial 3D scanners. Moreover, the high 3D density we obtained (corresponding to the edge average length of the 3D model mesh), up to ≈ 30 μm, allowed us to reconstruct even the smallest details of the inscriptions, both in the mesh and in the texture layer of the 3D models
A New Dual Mode Directional Coupler for the Realization of Compact Dual Polarization Beam Forming Networks
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