A Voxel-Based Fusing Method for Aerial Laser Scanning and Oblique Image Point Cloud Via Noise-and-Occupancy-Aware

2022 ◽  
pp. 205-227
Author(s):  
Shiming Li ◽  
Qing Zhu ◽  
Han Hu ◽  
Xuming Ge ◽  
Chuncheng Zhu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Image Point ◽  
Aerial Laser Scanning

scholarly journals The Use of UAV in Cadastral Mapping of the Czech Republic

2021 ◽  
Vol 10 (6) ◽  
pp. 380
Author(s):  
Václav Šafář ◽  
Markéta Potůčková ◽  
Jakub Karas ◽  
Jan Tlustý ◽  
Eva Štefanová ◽  
...  
Keyword(s):  
Czech Republic ◽  
Real Estate ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Maximum Efficiency ◽  
Image Point ◽  
The Czech Republic ◽  
Main Challenge ◽  
Check Points

The main challenge in the renewal and updating of the Cadastre of Real Estate of the Czech Republic is to achieve maximum efficiency but to retain the required accuracy of all points in the register. The paper discusses the possibility of using UAV photogrammetry and laser scanning for cadastral mapping in the Czech Republic. Point clouds from images and laser scans together with orthoimages were derived over twelve test areas. Control and check points were measured using geodetic methods (RTK-GNSS and total stations). The accuracy of the detailed survey based on UAV technologies was checked on hundreds of points, mainly building corners and fence foundations. The results show that the required accuracy of 0.14 m was achieved on more than 80% and 98% of points in the case of the image point clouds and orthoimages and the case of the LiDAR point cloud, respectively. Nevertheless, the methods lack completeness of the performed survey that must be supplied by geodetic measurements. The paper also provides a comparison of the costs connected to traditional and UAV-based cadastral mapping, and it addresses the necessary changes in the organisational and technological processes in order to utilise the UAV based technologies.

scholarly journals LONG LINE CLIFF TOPOGRAPHY MEASUREMENT BY USING STRUCTURE FROM MOTION ALGORITHM IN PHOTOGRAMMETRY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 5-12
Author(s):  
C. Altuntas
Keyword(s):  
Structure From Motion ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Measurement Techniques ◽  
High Accuracy ◽  
Aerial Laser Scanning ◽  
The Earth ◽  
Long Line ◽  
Dense Point

<p><strong>Abstract.</strong> The topography of cliffs and steep slopes must be measured to acquire additional information for landscaping, visualizing changes and taking precautions against natural hazards. The Earth topography has been measured predominantly with photogrammetry, terrestrial/aerial laser scanning or other traditional measurement techniques. The stereo photogrammetry necessitates greater effort to obtain a three-dimensional (3D) model of the imaged surface. Meanwhile, terrestrial or aerial laser scanning can collect high-density measurements of spatial data in a short time. However, the costs of implementing laser scanning instruments are very high. Furthermore, conventional measurement techniques that use total stations require immense effort to collect complete 3D measurements of cliffs. On the other hand, dense image based point cloud using multi-view photogrammetry based on structure from motion (SfM) algorithm is much more effective than the others for measuring the Earth topography. In this study, the cliff topography of an old quarry located in the state of Selcuklu of Konya Province in Turkey was measured by multi-view photogrammetry. The cliff has a continuous length of approximately 600 metres and a height of 25 metres in some places. The 3D model of the cliff was generated with the image based dense point cloud of multi-view photogrammetry. Then 3D dense point cloud model was registered into a local georeference system by using control points (CPs). Because of the long line measurement area, number and localization of the CPs is very important for achieving a high-accuracy to registration into georeferenced system. The registration accuracies were evaluated for different number and distribution of the CPs with the residuals on the check points (ChPs). The high accuracy registration was acquired with uniform distributed 3 and 8 CPs as the residuals of 24.08&amp;thinsp;cm and 23.03&amp;thinsp;cm on the ChPs respectively. The results indicated that 3D measurement of long line cliffs can be performed using multi-view photogrammetry, and the registration should be made with the uniform distributed CPs. In addition, a texture-mapped 3D model and orthophoto images of the cliff surfaces were created for detailed visualization.</p>

scholarly journals DETECTION OF BUILDING ROOFS AND FACADES FROM AERIAL LASER SCANNING DATA USING DEEP LEARNING

2019 ◽  
Vol XLII-2/W11 ◽  
pp. 975-980 ◽  
Author(s):  
F. Pirotti ◽  
C. Zanchetta ◽  
M. Previtali ◽  
S. Della Torre
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Remote Sensing Data ◽  
Automatic Extraction ◽  
3D Mapping ◽  
Aerial Laser Scanning ◽  
Semantic Classes

<p><strong>Abstract.</strong> In this work we test the power of prediction of deep learning for detection of buildings from aerial laser scanner point cloud information. Automatic extraction of built features from remote sensing data is of extreme interest for many applications. In particular latest paradigms of 3D mapping of buildings, such as CityGML and BIM, can benefit from an initial determination of building geometries. In this work we used a LiDAR dataset of urban environment from the ISPRS benchmark on urban object detection. The dataset is labelled with eight classes, two were used for this investigation: roof and facades. The objective is to test how TensorFlow neural network for deep learning can predict these two classes. Results show that for “roof” and “facades” semantic classes respectively, recall is 84% and 76% and precision is 72% and 63%. The number and distribution of correct points well represent the geometry, thus allowing to use them as support for CityGML and BIM modelling. Further tuning of the hidden layers of the DL model will likely improve results and will be tested in future investigations.</p>

SFM-Forest-Benchmark project: The benchmarking of image-based point cloud for forest inventory

2020 ◽  
Author(s):  
Martin Mokros ◽  
Markus Hollaus ◽  
Yunsheng Wang ◽  
Xinlian Liang
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
State Of The Art ◽  
Point Clouds ◽  
The State ◽  
Image Point ◽  
Silver Fir ◽  
Stem Density

&lt;p&gt;The benchmarking project of image-based point cloud for forest inventory (SFM-Forest-Benchmark) was initiated in 2019 and supported by ISPRS Scientific Initiative 2019. The main goal of the project was the evaluation of the applicability of terrestrial image-based point clouds for forest inventories, the clarification of the potential and limitations of the state-of-the-art techniques, and the exploration of the best practices in practical field inventories. In the project, related tree parameter (i.e. tree position diameter at breast height - DBH) were derived from 14 algorithms and evaluated using field inventory data as a reference. In order to clarify the potential of terrestrial image-based point clouds, the results from the image-based point clouds were also compared to results derived from the best available point clouds obtained by terrestrial laser scanning (TLS).&lt;/p&gt;&lt;p&gt;The project is consisted of two phases. In the first phase, we established two research plots in each country (Austria, China, Czech, Finland and Slovakia), ten plots in total. The stem density ranged from 272 to 875 stems/ha and plot size ranged approximately from 700 to 2500 m&lt;sup&gt;2&lt;/sup&gt;. Dominant tree species across research plots were Norway spruce, European beech, bald cypress, Chinese tulip poplar, Scots pine, European silver fir and sessile oak. TLS, images and reference data acquisition were performed on each study site, where TLS data were acquired through multi-scan approach, images were taken in the stop-and-go mode, and tree positions and the DBHs were measured with a tachymeter and a calliper as field references. Images were processed with structure from motion algorithm within Agisoft Metashape software to final point clouds. The TLS data was pre-processed with RiProcess software. And, the co-registration of all three data sources (TLS, SFM, and reference data) was done with OPALS software.&lt;/p&gt;&lt;p&gt;In the benchmarking phase, we distributed point clouds to participants of the benchmark. Altogether 14 different research groups processed the data with own algorithms. The individual results are evaluated through the reference to clarify the applicability of the image-point clouds in deriving tree parameters, were compared to each other to reveal the state-of-the-art of technologies, and were benchmarked to the up-to-data the most accurate data from TLS to explore the strength and weakness of the image-based point cloud. In this presentation the first benchmark results will be presented and discussed.&lt;/p&gt;&lt;p&gt;All images and point clouds collected for this project will be available as open access data for non-commercial uses.&lt;/p&gt;

Urban vehicle extraction from aerial laser scanning point cloud data

2020 ◽  
Vol 41 (17) ◽  
pp. 6664-6697 ◽  
Author(s):  
Tonggang Zhang ◽  
Yuhui Kan ◽  
Hailong Jia ◽  
Chuan Deng ◽  
Tingsong Xing
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Aerial Laser Scanning

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Revealing Changes in the Stem Form and Volume Allocation in Diverse Boreal Forests Using Two-Date Terrestrial Laser Scanning

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 835
Author(s):  
Ville Luoma ◽  
Tuomas Yrttimaa ◽  
Ville Kankare ◽  
Ninni Saarinen ◽  
Jiri Pyörälä ◽  
...  
Keyword(s):  
Tree Growth ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Stem Volume ◽  
Accurate Information ◽  
Stem Form ◽  
Monitoring Period ◽  
Significant Difference

Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.

scholarly journals Comparing High Accuracy t-LiDAR and UAV-SfM Derived Point Clouds for Geomorphological Change Detection

2021 ◽  
Vol 10 (6) ◽  
pp. 367
Author(s):  
Simoni Alexiou ◽  
Georgios Deligiannakis ◽  
Aggelos Pallikarakis ◽  
Ioannis Papanikolaou ◽  
Emmanouil Psomiadis ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Change Detection ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Value ◽  
Detection Methods ◽  
Mountainous Catchments ◽  
Aerial Vehicle ◽  
Soil Erosion Assessment

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

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