scholarly journals How Can Remote Sensing Reduce Required Human Intervention in Robotic Forest Regeneration

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1802
Author(s):  
Argo Orumaa ◽  
Priit Vellak ◽  
Mait Lang ◽  
Marek Metslaid ◽  
Riho Kägo ◽  
...  
Keyword(s):  
Forest Regeneration ◽  
Laser Scanning ◽  
Large Scale ◽  
Digital Terrain Model ◽  
Observation Data ◽  
Clear Cut ◽  
Terrain Model ◽  
External Data ◽  
Earth Observation Data ◽  
Scale Usage

In this article, we introduce an alternative solution for forest regeneration based on unmanned ground vehicles (UGV) and describe requirements for external data, which could significantly increase the level of automation. Over the past few decades, the global forested area has decreased, and there is a great need to restore and regenerate forests. Challenges such as the lack of labor and high costs demand innovative approaches for forest regeneration. Mechanization has shown satisfactory results in terms of time-efficient planting, although its usage is limited by high operational costs. Innovative technologies must be cost-efficient and profitable for large scale usage. Automation could make mechanized forest regeneration feasible. Forest regeneration operations can be automated using a purpose built unmanned platform. We developed a concept to automate forest planting operations based on mobility platform. The system requires external data for efficient mobility in clear-cut areas. We developed requirements for external data, analyzed available solutions, and experimented with the most promising option, the SfM (structure from motion) technique. Earth observation data are useful in the planning phase. A DEM (digital terrain model) for UGV planter operations can be constructed using ALS (airborne laser scanning), although it may be restricted by the cost. Low-altitude flights by drones equipped with digital cameras or lightweight laser scanners provided a usable model of the terrain. This model was precise (3–20 cm) enough for manually planning of the trajectory for the planting operation. This technique fulfilled the system requirements, although it requires further development and will have to be automated for operational use.

scholarly journals TYPICAL APPLICATIONS OF AIRBORNE LIDAR TECHNOLAGY IN GEOLOGICAL INVESTIGATION

2018 ◽  
Vol XLII-3 ◽  
pp. 2459-2463 ◽  
Author(s):  
X. Zheng ◽  
C. Xiao
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Forest Cover ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Observation Data ◽  
Geological Investigation ◽  
Terrain Model ◽  
Landslide Stability ◽  
Indispensable Tool

The technology of airborne light detection and ranging (LiDAR), also referred to as Airborne Laser Scanning, is widely used for high-resolution topographic data acquisition (even under forest cover) with sub-meter planimetric and vertical accuracy. This contribution constructs the real digital terrain model to provide the direct observation data for the landscape analysis in geological domains. Based on the advantage of LiDAR, the authors mainly deal with the applications of LiDAR data to such fields as surface land collapse, landslide and fault structure extraction. The review conclusion shows that airborne LiDAR technology is becoming an indispensable tool for above mentioned issues, especially in the local and large scale investigations of micro-topography. The technology not only can identify the surface collapse, landslide boundary and subtle faulted landform, but also be able to extract the filling parameters of collapsed surface, the geomorphic parameters of landslide stability evaluation and cracks. This technology has extensive prospect of applications in geological investigation.

scholarly journals DTM-based analysis of the spatial distribution of topolineaments

2020 ◽  
Vol 12 (1) ◽  
pp. 1185-1199
Author(s):  
Mirosław Kamiński
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Research Area ◽  
Tectonic Structures ◽  
Empirical Bayesian ◽  
Scanning Method ◽  
Empirical Bayesian Kriging ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser

AbstractThe research area is located on the boundary between two Paleozoic structural units: the Radom–Kraśnik Block and the Mazovian–Lublin Basin in the southeastern Poland. The tectonic structures are separated by the Ursynów–Kazimierz Dolny fault zone. The digital terrain model obtained by the ALS (Airborne Laser Scanning) method was used. Classification and filtration of an elevation point cloud were performed. Then, from the elevation points representing only surfaces, a digital terrain model was generated. The model was used to visually interpret the course of topolineaments and their automatic extraction from DTM. Two topolineament systems, trending NE–SW and NW–SE, were interpreted. Using the kernel density algorithm, topolineament density models were generated. Using the Empirical Bayesian Kriging, a thickness model of quaternary deposits was generated. A relationship was observed between the course of topolineaments and the distribution and thickness of Quaternary formations. The topolineaments were compared with fault directions marked on tectonic maps of the Paleozoic and Mesozoic. Data validation showed consistency between topolineaments and tectonic faults. The obtained results are encouraging for further research.

scholarly journals TECHNIQUE OF AUTOMATIC MOBILE LASER SCANNING DATA FILTERING

2021 ◽  
Vol 26 (3) ◽  
pp. 5-19
Author(s):  
Maxim A. Altyntsev ◽  
◽  
Hamid Majid Saber Karkokli ◽  
Keyword(s):  
Laser Scanning ◽  
Single Point ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Weather Conditions ◽  
Automatic Generation ◽  
Data Filtering ◽  
Terrain Model ◽  
Digital Terrain ◽  
Filtration Technique

The result of laser scanning is an array of laser points. The generation of a single point cloud in a given coordinate system is carried out during the registration process at the stage of preliminary field data processing. At this stage it is also often necessary to filter the data. Laser points with an erroneous position are eliminated during the data filleting. The number of erroneous laser points is determined by the of the laser scanner characteristics, surveyed area peculiarities and weather conditions. The devel-opment of methods and algorithms for filtering laser scanning data is carried out based on the analysis of the laser point spatial position and a certain set of additional characteristics, such as intensity value, echo signal, color value. The technique of mobile laser scanning data filtering for the territory of the road passing among the forest and close to individual industrial facilities and building. The main goal of the proposed filtration technique is to obtain data for automatic generation of an accurate digital terrain model. The filtration technique was developed for data acquired under the least favorable con-ditions – in wet weather. Accuracy estimation of generating digital terrain model based on filtered data was carried out.

scholarly journals FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 627-633 ◽  
Author(s):  
A. Nascetti ◽  
M. Di Rita ◽  
R. Ravanelli ◽  
M. Amicuzi ◽  
S. Esposito ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Global Scale ◽  
Google Earth ◽  
Observation Data ◽  
Geometric Accuracy ◽  
Computing Platform ◽  
Aster Gdem ◽  
Google Earth Engine ◽  
Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters.<br><br> In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

2021 ◽  
Author(s):  
Edzer Pebesma ◽  
Patrick Griffiths ◽  
Christian Briese ◽  
Alexander Jacob ◽  
Anze Skerlevaj ◽  
...  
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Google Earth ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Repositories ◽  
Software Ecosystem ◽  
Wide Range ◽  
Earth Observation Data

&lt;p&gt;The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine&amp;#8482; (GEE), lets the user focus on the science rather than on data management.&lt;/p&gt;&lt;p&gt;The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.&lt;/p&gt;&lt;p&gt;A follow-up, ESA-funded project &amp;#8220;openEO Platform&amp;#8221; realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (&amp;#8220;pay per compute job&amp;#8221;) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.&amp;#160; The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.&lt;/p&gt;&lt;p&gt;While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA&amp;#8217;s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.&lt;/p&gt;

scholarly journals Automating the creation of channel cross section data from aerial laser scanning and hydrological surveying for modeling flood events / Automatická tvorba geometrie vodních toků na základě syntézy dat z leteckého laserového skenování a hydrologického měření pro modelování povodňových událostí. j. hydrol., hydromech., 60, 2012, 4; 25 lit., 8 obr., 5 tab.

2012 ◽  
Vol 60 (4) ◽  
pp. 227-241 ◽  
Author(s):  
Radek Roub ◽  
Tomáš Hejduk ◽  
Pavel Novák
Keyword(s):  
Flow Rate ◽  
Hydrodynamic Model ◽  
Input Data ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Flood Events ◽  
Hydrodynamic Models ◽  
Terrain Model ◽  
Aerial Laser Scanning ◽  
Digital Terrain

Knowing the extent of inundation areas for individual N-year flood events, the specific flood scenarios, and having an idea about the depths and velocities in the longitudinal or transverse water course profile provided by hydrodynamic models is of key importance for protecting peoples’ lives and mitigating damage to property. Input data for creating the watercourse computational geometry are crucial for hydrodynamic models. Requirements for input data vary with respect to the hydrodynamic model used. One-dimensional (1D) hydrodynamic models in which the computing track is formed by cross-sectional profiles of the channel are characterized by lower requirements for input data. In two-dimensional (2D) hydrodynamic models, a digital terrain model is needed for the entire area studied. Financial requirements of the project increase with regard to the input data and the model used. The increase is mainly due to the high cost of the geodetic surveying of the stream channel. The paper aims at a verification and presentation of the suitability of using hydrological measurements in developing a schematization (geometry) of water courses based on topographic data gained from aerial laser scanning provided by the Czech Office for Surveying, Mapping and Cadastre. Taking into account the hydrological measurement during the schematization of the water course into the hydrodynamic model consists in the derivation of flow rate achieved at the time of data acquisition using the method of aerial laser scanning by means of hydrological analogy and in using the established flow rate values as a basis for deepening of the digital terrain model from aerial laser scanning data. Thus, the given principle helps to capture precisely the remaining part of the channel profile which is not reflected in the digital terrain model prepared by the method of aerial laser scanning and fully correct geometry is achieved for the hydrodynamic model.

scholarly journals Threat of Pollution Hotspots Reworking in River Systems: Case Study of the Ploučnice River (Czech Republic)

2019 ◽  
Vol 8 (1) ◽  
pp. 37 ◽  
Author(s):  
Jitka Elznicová ◽  
Tomáš Matys Grygar ◽  
Jan Popelka ◽  
Martin Sikora ◽  
Petr Novák ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Terrain Model ◽  
Non Invasive ◽  
Digital Terrain ◽  
Quality Of Water ◽  
Lateral Channel ◽  
Floodplain Development ◽  
Channel Deposits

As fluvial pollution may endanger the quality of water and solids transported by rivers, mapping and evaluation of historically polluted fluvial sediments is an urgent topic. The Ploučnice River and its floodplain were polluted by local uranium mining from 1971–1989. We have studied this river since 2013 using a combination of diverse methods, including geoinformatics, to identify pollution hotspots in floodplains and to evaluate the potential for future reworking. Archival information on pollution history and past flooding was collected to understand floodplain dynamics and pollution heterogeneity. Subsequently, a digital terrain model based on laser scanning data and data analysis were used to identify the sites with river channel shifts. Finally, non-invasive geochemical mapping was employed, using portable X-ray fluorescence and gamma spectrometers. The resulting datasets were processed with geostatistical tools. One of the main outputs of the study was a detailed map of pollution distribution in the floodplain. The results showed a relationship between polluted sediment deposition, past channel shifts and floodplain development. We found that increased concentration of pollution occurred mainly in the cut-off meanders and lateral channel deposits from the mining period, the latter in danger of reworking (reconnecting to the river) in the coming decades.

scholarly journals UAV-Based Terrain Modeling under Vegetation in the Chinese Loess Plateau: A Deep Learning and Terrain Correction Ensemble Framework

2020 ◽  
Vol 12 (20) ◽  
pp. 3318 ◽  
Author(s):  
Jiaming Na ◽  
Kaikai Xue ◽  
Liyang Xiong ◽  
Guoan Tang ◽  
Hu Ding ◽  
...  
Keyword(s):  
Deep Learning ◽  
Loess Plateau ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Terrain Correction ◽  
Chinese Loess Plateau ◽  
Chinese Loess ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain

Accurate topographic mapping is a critical task for various environmental applications because elevation affects hydrodynamics and vegetation distributions. UAV photogrammetry is popular in terrain modelling because of its lower cost compared to laser scanning. However, this method is restricted in vegetation area with a complex terrain, due to reduced ground visibility and lack of robust and automatic filtering algorithms. To solve this problem, this work proposed an ensemble method of deep learning and terrain correction. First, image matching point cloud was generated by UAV photogrammetry. Second, vegetation points were identified based on U-net deep learning network. After that, ground elevation was corrected by estimating vegetation height to generate the digital terrain model (DTM). Two scenarios, namely, discrete and continuous vegetation areas were considered. The vegetation points in the discrete area were directly removed and then interpolated, and terrain correction was applied for the points in the continuous areas. Case studies were conducted in three different landforms in the loess plateau of China, and accuracy assessment indicated that the overall accuracy of vegetation detection was 95.0%, and the MSE (Mean Square Error) of final DTM (Digital Terrain Model) was 0.024 m.

scholarly journals A novel automated method for the improvement of photogrammetric DTM accuracy in forests

2018 ◽  
Vol 142 (11-12) ◽  
pp. 576-577 ◽  
Author(s):  
Mateo Gašparović ◽  
Ivan Balenović ◽  
Ante Seletković ◽  
Anita Simic Milas
Keyword(s):  
Quercus Robur ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Light Detection And Ranging ◽  
Mean Square ◽  
Terrain Model ◽  
Digital Terrain ◽  
Automated Method ◽  
Airborne Laser ◽  
Fraxinus Angustifolia

Digitalni model reljefa (DTM, engl. Digital Terrain Model) ima široku i važnu primjenu u mnogim djelatnostima, uključujući i šumarstvo. Međutim, precizno modeliranje terena, odnosno izrada DTM-a u šumama, bilo korištenjem terenskih metoda ili metoda daljinskih istraživanja, izazovan je i vrlo zahtjevan zadatak. U većini razvijenih zemalja svijeta, zračno lasersko skeniranje (ALS, engl. Airborne Laser Scanning) bazirano na LiDAR (engl. Light Detection and Ranging) tehnologiji trenutno predstavlja glavnu metodu za izradu DTM-a. Uslijed mogućnosti laserskog zračenja da penetrira kroz krošnje drveća, LiDAR tehnologija se pokazala kao efektivna i brza metoda za izradu DTM-a u šumskim područjima s vrlo velikom točnošću. Međutim, u mnogim zemljama svijeta, uključujući i Hrvatsku, zračno lasersko skeniranje nije u potpunosti provedeno, tj. samo su manji dijelovi zemlje pokriveni s podacima zračnog laserskog skeniranja. U tim slučajevima, DTM temeljen na stereo-fotogrametrijskoj izmjeri aerosnimaka potpomognut s terenskim podacima najčešće predstavlja glavni izvor informacija za izradu DTM-a. Poznato je da tako izrađen DTM u šumskim predjelima ima manju točnost od DTM-a dobivenog na temelju zračnog laserskog skeniranja zbog pokrivenosti terena vegetacijom. Također, u okviru nedavno provedenog istraživanja (Balenović i dr., 2018) utvrđeno je da takvi službeni fotogrametrijski digitalni podaci terena u šumskim predjelima sadrže određen broj tzv. grubih grešaka, koje mogu značajno utjecati na točnost izrađenog DTM-a. Nakon vizualnog detektiranja i manualnog uklanjanja tih pogrešaka, Balenović i dr. (2018) utvrdili su značajno poboljšanje točnosti fotogrametrijskog DTM-a. Stoga je glavni cilj ovoga rada razviti automatsku metodu za detekciju i eliminaciju vertikalnih pogrešaka u fotogrametrijskim digitalnim podacima terena te na taj način poboljšati točnost fotogrametrijskog DTM-a u nizinskim šumskim područjima Hrvatske. Ideja je razviti brzu, jednostavnu i učinkovitu metodu koja će biti primjenjiva i za druga šumska područja sličnih karakteristika, a za koja ne postoje DTM dobiven zračnim laserskim skeniranjem. Istraživanje je provedeno u nizinskim šumama na području gospodarske jedinice Jastrebarski lugovi, u neposrednoj blizini Jastrebarskog (Slika 1). Istraživanjem je obuhvaćena površina od 2.005,74 ha, na kojoj su u najvećoj mjeri zastupljene jednodobne sastojine hrasta lužnjaka (Quercus robur L.), a u ma­njoj mjeri jednodobne sastojine poljskog jasena (Fraxinus angustifolia L.) te jednodobne sastojine običnoga graba (Carpinus betulus L.). Nadmorska visina područja istraživanja kreće se u rasponu od 105 do 121 m. Fotogrametrijski DTM (DTM<sub>PHM</sub>) je izrađen iz digitalnih vektorskih podataka terena (prijelomnice, linije oblika, markantne točke terena i pravokutne mreže visinskih točaka) nabavljenih iz Državne geodetske uprave (Slika 2). Ti podaci predstavljaju nacionalni standard i jedini su dostupni podaci za izradu DTM-a u Hrvatskoj. Detaljan opis vektorskih podataka dan je u radu Balenović i dr. (2018). Prvo je iz digitalnih terenskih podataka izrađena nepravilna mreža trokuta, koja je potom linearnom interpolacijom pretvorena u rasterski DTM<sub>PHM</sub> prostorne rezolucije (veličine piksela) 0,5 m. Automatska metoda za detekciju i eliminaciju vertikalnih pogrešaka fotogrametrijskog DTM-a u nizinskim šumskim područjima razvijena je u slobodnom programskom paketu Grass GIS (Slika 3). Kombinacijom vrijednosti nagiba i tangencijalne zakrivljenosti terena rasterskog DTM<sub>PHM</sub> (Slika 4), automatskom metodom su detektirane 91 grube greške (engl. outliers). Drugim riječima, utvrđeno je da 91 točkasti vektorski objekt pogrešno prikazuje stvarnu visinu terena. Navedeni broj čini 3,2 % od ukupnog broja točkastih objekata korištenih za izradu DTM<sub>PHM</sub>-a. Nakon eliminacije detektiranih pogrešaka izrađen je novi, korigirani fotogrametrijski DTM (DTM<sub>PHMc</sub>). Za ocjenu vertikalne točnosti izvornog (DTM<sub>PHM</sub>) i korigiranog DTM-a (DTM<sub>PHMc</sub>) korišten je visoko precizni DTM dobiven zračnim laserskim skeniranjem (DTM<sub>LiD</sub>). U tu svrhu su izrađeni rasteri razlika između DTM<sub>PHM </sub>i DTM<sub>LiD</sub>, te između DTM<sub>PHMc </sub>i DTM<sub>LiD</sub>. Kako je preliminarnom analizom utvrđeno da vertikalne razlike između DTM<sub>PHM </sub>i DTM<sub>LiD</sub> nisu normalno distribuirane (Slika 5), za ocjenu točnosti su uz normalne mjere točnosti korištene i tzv. robusne mjere točnosti (Tablica 2). Dobiveni rezultati ukazuju na poboljšanje vertikalne točnosti fotogrametrijskog DTM-a primjenom razvijene automatske metode. To je posebice uočljivo na podpodručjima 2 i 3 (Slika 6 i 7) u kojima se nakon uklanjanja detektiranih grešaka, korijen srednje kvadratne pogreške (RMSE, engl. root mean square error) smanjio za 8 % odnosno 50 % (Tablica 2). Na temelju dobivenih rezultata i usporedbe s DTM<sub>LiD</sub>, može se zaključiti da predložena metoda uspješno detektira i eliminira vertikalne pogreške fotogrametrijskog DTM-a u nizinskim šumskim područjima, te slijedom toga poboljšava njegovu vertikalnu točnost.

scholarly journals Reduction Method for Mobile Laser Scanning Data

2018 ◽  
Vol 7 (7) ◽  
pp. 285 ◽  
Author(s):  
Wioleta Błaszczak-Bąk ◽  
Zoltan Koppanyi ◽  
Charles Toth
Keyword(s):  
Data Reduction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
New Approach ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Short Time ◽  
Mls Method

Mobile Laser Scanning (MLS) technology acquires a huge volume of data in a very short time. In many cases, it is reasonable to reduce the size of the dataset with eliminating points in such a way that the datasets, after reduction, meet specific optimization criteria. Various methods exist to decrease the size of point cloud, such as raw data reduction, Digital Terrain Model (DTM) generalization or generation of regular grid. These methods have been successfully applied on data captured from Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS), however, they have not been fully analyzed on data captured by an MLS system. The paper presents our new approach, called the Optimum Single MLS Dataset method (OptD-single-MLS), which is an algorithm for MLS data reduction. The tests were carried out in two variants: (1) for raw sensory measurements and (2) for a georeferenced 3D point cloud. We found that the OptD-single-MLS method provides a good solution in both variants; therefore, the choice of the reduction variant depends only on the user.

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