Comparative analysis of mobile laser scanning and terrestrial laser scanning for the indoor mapping

Yi̇ği̇t, Abdurahman Yasi̇n; Hamal, Seda Nur Gamze; Ulvi̇, Ali̇; Yakar, Murat

doi:10.1080/09613218.2023.2227900

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…They showed that data can be evaluated as a metric measurement technique for sustainable and precise operational planning in forests. Similarly, Yi git et al [27] conclude that both indoor and outdoor mapping have improved greatly with the use of laser scanning technology. In this research, the greatest challenge for measurements of diameters and lengths remotely using UAS photogrammetry was in the cases of an unclear log border, and sometimes a single-band raster had to be used to define the border more clearly.…”

Section: Discussionmentioning

confidence: 99%

Terrestrial vs. UAV-Based Remote Measurements in Log Volume Estimation

Đuka,

Papa,

Lovrinčević

et al. 2023

Remote Sensing

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This study compared oak butt-log volume estimations gained through terrestrial measurements in the forest stand with a remote approach using an unmanned aerial system (UAS) and photogrammetric post-processing. Terrestrial measurements were conducted in the lowland part of Croatia after a completed motor–manual final felling of a 140-year-old even-aged oak stand. Butt-logs’ volumes were estimated with four methods: the sectioning method and Huber’s, Smailan’s and Riecke–Newton’s methods. Measuring diameters and lengths and estimating volumes remotely were based on orthophotos using four different software: ArcGIS, QGIS, AutoCAD and Pix4D. Riecke–Newton’s method for volume estimation had the smallest relative bias of +1.74%, while for Huber’s method it was −8.07% and with Smailan’s method it was +21.23%. Log volume estimations gained remotely via ArcGIS and QGIS were, in the case of Huber’s method, at +3.63% relative bias, and in the case of Riecke–Newton’s method at +1.39% relative bias. Volume estimation using the sectioning method resulted in a total of 51.334 m3 for the whole sample, while the sectioning method performed with the help of AutoCAD resulted in 55.151 m3, i.e., +7.43% relative bias. Volume estimation of thirty oak butt-logs given by Pix4D software (version 4.8.4) resulted in +9.34% relative bias (56.134 m3). Comparing terrestrial measurements and the volume estimations based on them to those gained remotely showed a very high correlation in all cases. This study showed that using a UAS for log volume estimation surveys has the potential for broader use, especially after final felling in even-aged forests where the remaining trees in the stand would not block photogrammetric analysis.

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Section: Discussionmentioning

confidence: 99%

Terrestrial vs. UAV-Based Remote Measurements in Log Volume Estimation

Đuka,

Papa,

Lovrinčević

et al. 2023

Remote Sensing

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Mobil haritalama amaçlı Mobilenet tabanlı trafik işaretleri tespit sistemi: kitlesel coğrafi bilgi toplama sistemi

Özcan Tatar,

Yılmaz,

Efe

et al. 2024

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Mobil haritalama sistemleri (Mobile Mapping Systems- MMS) coğrafi veri toplama yetenekleri ile birlikte, gelişmiş sürücü destek sistemleri (Advanced Driver Assistance Systems- ADAS) ve akıllı ulaşım sistemleri (Intelligent Transportation Systems - ITS) gibi birçok uygulama alanın sayısal harita ihtiyacını karşılayabilmektedir. Üretilen haritalarda özellikle trafik işaretlerinin konum ve sınıf bilgilerinin bulunması, bahsi geçen uygulama alanları için önem arz etmektedir. Ancak, MMS tarafından toplanan verilerin geniş ölçekli ve karmaşık olması, trafik işaretlerinin konum-sınıf çıkarımlarını zorlaştırmaktadır. Bu nedenle araştırmacılar, trafik işareti verilerinin işlenmesi için yapay zekâ tabanlı yöntemler geliştirmiştir. Bu çalışmada, trafik işaretlerinin konum ve sınıf bilgilerinin yapay zekâ ile çıkarımına yönelik tasarlanan Kitlesel Coğrafi Bilgi Toplama Sistemi (KCVTS) açıklanmıştır. KCVTS; MobileNet tabanıyla mobil cihazlarda etkinlik gösteren, cihazın gerçek-zamanlı kamera görüntülerinde bulunan trafik işaretlerini tespit eden ve sınıflandıran ve böylece, işaretlerin konum-sınıf bilgilerini veri tabanına aktaran hafif-yapılı bir sistemdir. Çalışmada KCVTS’nin manuel işlem gerektiren geleneksel yöntemlerden, trafik işaretlerinin şekil ve renk gibi özelliklerinin çıkarımına dayanan yarı-geleneksel yöntemlerden ve saha verilerinin merkezdeki güçlü bilgisayarlarda, bilgisayarlı görü ve makine öğrenmesi teknikleri ile işlendiği YZ tabanlı yöntemlerden birçok noktada daha pratik ve verimli olduğu gösterilmiştir.

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Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

Yang,

Zuo,

Zhao

et al. 2024

IJGI

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The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

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Comparative analysis of mobile laser scanning and terrestrial laser scanning for the indoor mapping

Cited by 3 publications

References 46 publications

Terrestrial vs. UAV-Based Remote Measurements in Log Volume Estimation

Terrestrial vs. UAV-Based Remote Measurements in Log Volume Estimation

Mobil haritalama amaçlı Mobilenet tabanlı trafik işaretleri tespit sistemi: kitlesel coğrafi bilgi toplama sistemi

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

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