Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Fayez Tarsha Kurdi"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3010-3022 (2024)
Tree modeling and visualization still represent a challenge in the light detecting and ranging area. Starting from the segmented tree point clouds, this article presents an innovative tree modeling and visualization approach. The algorithm simulates
Externí odkaz:
https://doaj.org/article/e7426a76260e465c91afd058ae967af2
Autor:
Zahra Gharineiat, Fayez Tarsha Kurdi, Krish Henny, Hamish Gray, Aaron Jamieson, Nicholas Reeves
Publikováno v:
Remote Sensing, Vol 16, Iss 17, p 3256 (2024)
The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the
Externí odkaz:
https://doaj.org/article/720539dbd27545faa7b5a8c130f3954c
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2220 (2024)
This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the
Externí odkaz:
https://doaj.org/article/69c8f8dc094f49369c056cf6e2496ed1
Publikováno v:
European Journal of Remote Sensing, Vol 56, Iss 1 (2023)
ABSTRACTThree-dimensional (3D) reconstruction of a building can be facilitated by correctly segmenting different feature points (e.g. in the form of boundary, fold edge, and planar points) over the building roof, and then, establishing relationships
Externí odkaz:
https://doaj.org/article/c900ad33764a4c27a4bf84525d3ace4a
Publikováno v:
Sensors, Vol 23, Iss 17, p 7360 (2023)
The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two str
Externí odkaz:
https://doaj.org/article/1528edc5ecbd450aa5246b3055e7d6a3
Publikováno v:
Remote Sensing, Vol 15, Iss 13, p 3324 (2023)
The development of autonomous navigation systems requires digital building models at the LoD3 level. Buildings with atypically shaped features, such as turrets, domes, and chimneys, should be selected as landmark objects in these systems. The aim of
Externí odkaz:
https://doaj.org/article/f908215cbf4c43409e7c419aaac38362
Autor:
Fayez Tarsha Kurdi, Mohammad Awrangjeb
Publikováno v:
Canadian Journal of Remote Sensing, Vol 46, Iss 5, Pp 603-621 (2020)
This paper studies the fidelity level of the extracted LiDAR (Light Detection And Ranging) building point cloud in relation to the original building. In this context, the building point cloud is compared with a reference model. This comparison allows
Externí odkaz:
https://doaj.org/article/2e14bc5de4f746598b9e67b263245eaa
Publikováno v:
Remote Sensing, Vol 14, Iss 19, p 4687 (2022)
This paper presents an innovative approach to the automatic modeling of buildings composed of rotational surfaces, based exclusively on airborne LiDAR point clouds. The proposed approach starts by detecting the gravity center of the building’s foot
Externí odkaz:
https://doaj.org/article/aac6fdee6a484baeb9684b633334c54a
Publikováno v:
Remote Sensing, Vol 14, Iss 19, p 4685 (2022)
Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more re
Externí odkaz:
https://doaj.org/article/a6ab8ad524b347d0b9330251ce74fe22
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 822 (2022)
Recent public discourse regarding unmanned aerial vehicle (UAV) usage and regulation is focused around public privacy and safety. Most authorities have employed key guidelines and licensing procedures for piloting UAVs, however there is marginal cons
Externí odkaz:
https://doaj.org/article/2475cc45325e4565a781b7cc307a589a