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Close-Range Sensing in the AEC Industry

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 1933

Special Issue Editors


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Guest Editor
Geomatics Research Group, Faculty of Engineering Technology, KU Leuven, Gent, Belgium
Interests: building information modeling (BIM); 3D reconstruction; point cloud; laser scanning; photogrammetry; machine learning; construction monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth Observation Science, University of Twente, 7522 NB Enschede, The Netherlands
Interests: spatial analysis; mapping; geoinformation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The digital reconstruction of built assets, i.e., buildings, infrastructure, and utilities, is being extensively researched. The resulting information models such as GIS, BIM, or CAD are becoming increasingly important for facility management, project planning, and refurbishment. However, the current state of the art is not yet able to capture and process the required geometries and texture in a reliable unsupervised manner.

Typically, close-range sensing such as laser scanning and photogrammetry are used to digitize the assets in the built environment. This requires the unsupervised interpretation of the scenery and the automated parameter extraction for the widely varying domain-specific objects, i.e., heritage, structure, MEP, and architecture finishes. In the last few years, there has been intense research activity towards the automation of this process. However, there is still important work to be carried out involving (i) the collection and processing of close-range sensing data of the built environment, (ii) scene interpretation including semantic segmentation and object detection, and (iii) parameter extraction for the final information models.

This Special Issue will collect new technologies and methodologies that target the above objectives. We welcome submissions that cover but are not limited to the following:

  • Close-range sensing systems;
  • Geometric evaluation of mapping systems;
  • Close-range sensing data structures and models;
  • Scene interpretation including semantic segmentation, classification, and object detection;
  • Collision and change detection with existing models;
  • Remote sensing data processing in information models such as BIM and GIS.

Dr. Maarten Bassier
Dr. Florent Poux
Dr. Shayan Nikoohemat
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • close-range sensing
  • 3D reconstruction
  • classification
  • semantic segmentation
  • photogrammetry
  • BIM
  • GIS

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Published Papers (1 paper)

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Research

19 pages, 8591 KiB  
Article
Statewide Implementation of Salt Stockpile Inventory Using LiDAR Measurements: Case Study
by Justin Anthony Mahlberg, Haydn Malackowski, Mina Joseph, Yerassyl Koshan, Raja Manish, Zach DeLoach, Ayman Habib and Darcy M. Bullock
Remote Sens. 2024, 16(2), 410; https://doi.org/10.3390/rs16020410 - 20 Jan 2024
Cited by 1 | Viewed by 1542
Abstract
The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during [...] Read more.
The state of Indiana maintains approximately 120 salt storage facilities strategically distributed across the state for winter operations. In April 2023, those facilities contained approximately 217,000 tons of salt with an estimated value of USD 21 million. Accurate inventories at each facility during the winter season are important for scheduling re-supply so the facilities do not run out of salt. Inventories are also important at the end of the season for restocking to provide balanced inventories. This paper describes the implementation of a portable pole-mounted LiDAR system to measure salt stockpile inventory at 120 salt storage facilities in Indiana. Using two INDOT staff members, the end-of-season inventory took 9 working days, with volumetric inventories provided within 24 h of data collection. To provide an independent evaluation of the methodologies, the Hovermap ST backpack was used at selected facilities to provide control volumes. This system has a range of 100 m and an accuracy of ±3 cm, which reduces the occlusion to less than 8%. The pre-season facility capacity ranged from 0% to 100%, with an average of 66% full across all facilities. The post-season facility percentage ranged from 3% to 100%, with an average of 70% full. In addition, permanent roof-mounted LiDAR systems were deployed at two facilities to evaluate the effectiveness of monitoring salt stockpile inventories during winter operation activities. Plans are now underway to install fixed LiDAR systems at 15 additional facilities for the 2023–2024 winter season. Full article
(This article belongs to the Special Issue Close-Range Sensing in the AEC Industry)
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