Investigation and Implementation of New Technology Wearable Mobile Laser Scanning (WMLS) in Transition to an Intelligent Geospatial Cadastral Information System

Abstract

The human population is constantly increasing throughout the world, and accordingly, construction is increasing in the same way. Therefore, there is an emergence of irregular and unplanned urbanization. In order to achieve the goal of preventing irregular and unplanned urbanization, it is necessary to monitor the cadastral borders quickly. In this sense, the concept of a sensitive, up-to-date, object-based, 3D, and 4D (4D, 3D + time) cadastral have to be a priority. Therefore, continuously updating cadastral maps is important in terms of sustainability and intelligent urbanization. In addition, due to the increase in urbanization, it has become necessary to update the cadastral information system and produce 3D cadastral maps. However, since there are big problems in data collection in urban areas where construction is rapid, different data-collection devices are constantly being applied. While these data-collection devices have proven themselves in terms of accuracy and precision, new technologies have started to be developed in urban areas especially, which is due to the increase in human population and the influence of environmental factors. For this reason, LiDAR data collection methods and the SLAM algorithm can offer a new perspective for producing cadastral maps in complex urban areas. In this study, 3D laser scanning data obtained from a portable sensor based on the SLAM algorithm are tested, which is a relatively new approach for cadastral surveys in complex urban areas. At the end of this study, two different statistical comparisons and accurate analyses of the proposed methodology with reference data were made. First, WMLS data were compared with GNSS data and RMSE values for X, Y, and Z, and were found to be 4.13, 4.91, and 7.77 cm, respectively. In addition, WMLS length data and cadastral length data from total-station data were compared and RMSE values were calculated as 4.76 cm.

1. Introduction

Soil and land management have been important since the dawn of urban planning. Accordingly, studies for the determination of borders, establishment, and protection of property have arisen [1,2]. This has progressed in the direction of measuring the pieces of land and evaluating the measured data to offer the owner the right to use, benefit and dispose of their property. Today, these studies are called cadastral activities or cadastral. Modern cadastral systems are an unquestionable instrument for sustainable land management since they are a component of land administration systems. Cadastral has evolved dramatically over the past few decades both as a concept and system, growing from a straightforward land register to a technologically sophisticated multidimensional and multifunctional system that supports efficient and sustainable land management [3,4]. Additionally, as human strain on the environment increases, so does the growth of land administration systems, which include cadastral as a component of such systems. In particular, processes such as economic and political reform, urbanization, agricultural intensification, and deforestation, as well as concern for nature conservation, human well-being, and sustainable development are what drive and strongly depend on change [5].

Social and environmental developments have gained a new dimension with the aspects of human–land relationships and the differentiation of land use. The study Cadastral 2014 published by the Federation Internationale des Geometres/The International Federation of Surveyors (FIG) is aimed to set standards concerning cadastral, ensure public–private cooperation, and provide a digital transformation rather than classical measurements. With the prediction of the nature of land management in the next 20 years, the Cadastral 2034 vision has started to be discussed. In this direction, the concepts of sensitive, current, object-based, 3D, and 4D (4D, 3D + time) cadastral have been the priority targets [5,6,7,8,9]. The most significant of these are Agenda 21 and Agenda 2030, which seek to broadly define environmental protection and sustainable development at an early stage of planning. The monitoring of the 2030 Agenda’s Sustainable Development Goals (SDGs) requires the availability of high-quality, timely, and disaggregated data, which are crucial for making evidence-based decisions and guaranteeing accountability for the 2030 Agenda’s implementation. Unprecedented urban expansion is a factor in several environmental issues expressed in the SDGs. Although it has been happening for centuries, urbanization is a complicated and ongoing process that has greatly sped up in the previous few decades [10,11].

According to the United Nations’ Secretary-General, between 2000 and 2015, urban land expansion surpassed urban population increase in every part of the world. This caused unchecked urban expansion and a drop in urban density. Given that remote sensing data provide information on not only the location but also some attributes of buildings and related artificial infrastructure, they are unquestionably one of the most crucial data sources for tracking urban sprawl and updating data in cadastral systems. In recent years, with the acceleration of the renewal cadastral under the sustainable land method, studies in this field have started to be carried out with new methods and equipment. It is known that especially in Turkey, the rapidly increasing housing stock and development of cities play an important role in these studies. With the pressure of urbanization, the feasibility of cadastral surveys with classical measurement methods is insufficient due to changing land conditions. Therefore, the feasibility of cadastral studies has started to be tested with laser scanning technology, which offers a new and fast solution in cadastral update studies with technology [12,13,14].

Popović et al. (2017) used Light Detection and Ranging (LiDAR) technology for the 3D measurement of power lines, along with the latest developments in 3D data technology in their study in Serbia [15]. There have also been attempts to convert point clouds from LiDAR measurements to city models in a CityGML format and to use this data for 3D real estate cadastral purposes. At the end of the study, it was concluded that the use of Building Information Modeling (BIM) technology in Serbia increased in recent years, but that there was not much research on 3D LiDAR measurement methods including BIM in Serbian cadastral workflows. Wierzbicki et al. (2021), in their study, developed a hybrid approach for cadastral mapping using high-resolution aerial ortho imagery and LiDAR data. At the end of the study, they stated that modern technologies are very promising for cadastral modernization in Poland [16].

It is known that 99% of the cadastral works in Turkey have been completed [17]. However, the methods and equipment used are insufficient to meet the expected (±8 cm) precision regarding cadastral as some measurements were made incorrectly and incompletely, and these methods and equipment were also insufficient in providing solutions to existing problems such as digitizing the current cadastral data and international standards accepted by Turkey (ISO 19152, 2012) [18] as well as visionary studies such as the Land Administration Domain Model, INSPIRE, Cadastral 2014 and beyond to create the need for cadastral renewal [17,19].

With the development of technology, there are also continuous developments in measuring devices. Measuring devices, which started with meters in the historical process, continued to develop with the Electronic Length Meter (Total Station) and the Global Navigation Satellite System (GNSS) until recently. Although these technologies have proven themselves in terms of accuracy and precision, they are affected by environmental factors as a result of the increase in the human population. New technologies have started to be developed, especially in residential areas, due to the inadequacy of GNSS and the disadvantage of total-station and measurement technology in terms of time [20]. For this purpose, the performance of new technology WMLS, which can be an alternative to the geodetic survey, has been investigated by many disciplines in different studies. This study presents a comparative analysis using data from LiDAR sensors (Mobile LiDAR sensors), which are remote-sensing sensors. However, comparative analysis of this technology with traditional methods in large areas such as our study has been involved in very little research. The fact that the mobile LiDAR system is both a new technology and a complex structure make it more valuable to investigate the proposed approach in complex urban areas. In the research study, the complex urban area was preferred and the usability of WMLS LiDAR sensors in both accuracy analysis and cadastral studies was investigated.

1.1. Motivation

In residential areas where construction is common, it is difficult to collect spatial and geometric information between two buildings with cars and similar mobile LiDAR systems. In addition, it is difficult for mobile carriers such as cars to reach areas where new construction areas are abundant and where there is frequent construction. Therefore, the fact that WMLS platforms, which are rarely used in cadastral studies in the regions where this study was conducted, offer a new approach that make this article interesting. To elaborate, the motivation of this study is to produce a 3D cadastral map with Wearable Mobile LiDAR Scanning (WMLS), which is one of the LiDAR data collection methods of the Turkey cadastral and has been used in recent years.

1.2. Contribution

The study is a comprehensive analysis of the possibilities and limitations of the verification and modernization of the use of WMLS, one of the remote sensing data collection methods in 3D cadastral map production studies in terms of precision. In addition, the most important feature and originality of this study lie in that the WMLS system is carried out in a large area and tested on structures with different geometric properties. Therefore, the research provides both a scientific and practical contribution. In the study, scans were performed with WMLS, and point cloud data of the region were obtained. Using the orthophoto previously produced by the study team including the authors, vector drawings of the area were made in light of the data obtained from two methods. Afterward, ground measurements were carried out and the accuracy of the study was examined. The applicability of the laser scanning method in cadastral studies was tested by examining the results.

This study aims to answer three research questions: (1) Is the suggested approach regarding WMLS data sufficient for accuracy rather than a Geodetic survey method? (2) How successful is the accuracy of WMLS data in cadastral maps? (3) What is the use of the new technology WMLS LiDAR sensor for cadastral research in complex urban areas? To answer these questions, GNSS and total-station survey data were accepted as a reference and WMLS LiDAR data were compared in the cadastral test area. The main purpose of the study is to investigate the effect of the new technology WMLS LiDAR sensor on accuracy. This study differs from previous studies on LiDAR sensors in four respects: (a) the data of WMLS sensors obtained in this study were compared to reference data, (b) the effects of the cadastral test area on WMLS LIDAR sensor data, (c) the effects of urban areas during the data collection process on the sensors, and (d) the strength and weakness of WMLS data.

1.3. Organization

The article is organized as follows: In Section 1, a general introduction to the article and the introduction of the subject are made. Related work and background on the paper, the LiDAR system data, methods, GNSS, and total station reference data employed in this study are presented in Section 2. In this section, the materials and methods first used are explained in detail. Then, comparative analyses were made for the study area by giving all survey stages and accuracy analyses. Section 3 provides information on registration and referencing of the study area and accuracy analysis of WMLS data. Section 4 presents the summary and conclusions of the present study.

2. Materials and Methods

2.1. Related Work

The study’s ultimate objective is to provide a method to enable the creation of a cadastral map, hence understanding the idea of a cadaster is crucial. A cadaster, which is a methodically organized public register that depends on survey measures, is a complete official record of the real property’s border, ownership, and value. The cadaster and land register both belong to the same system and are interconnected [21]. Cadastral records serve as a complement to other documents and are crucial for legal or financial reasons [22]. Considering the pressing requirement for a flexible cadaster, the “Fit for purpose” notion is raised. A cadastral survey creates the geographic portion of a cadaster, which is typically represented as maps or plans [21]. Bruce [23] and Robillard et al. [24] both examined the legal and financial aspects of a cadastral survey. Depending on how carefully the border is surveyed and defined, the aim of a cadastral survey—cadastral boundaries—can be either fixed or general [25]. The focus of this study is to determine the general boundaries in areas with narrow streets and buildings that are close to each other with the WMLS method. This is due to how the determination of such areas with classical measurement methods creates significant problems in terms of time and cost. In addition, the latest advanced methods such as UAV photogrammetry and LiDAR technology have great shortcomings in these areas. In such areas, the fastest method currently seems to be a portable LiDAR sensor. Therefore, the focus is on studies using the LiDAR sensor in cadastral studies. The summary information of the sample case studies is provided below and explained in detail in Table 1.

Zhong et al. (2016) [26] investigated the performance of a laser scanning system in a village of 157 residences in an area of 7 ha. They compared the performance of their system with the results of the building contours extracted from the laser data against the generated building vector map. They stated that during data collection, only 15 out of a total of 135 residences located close to each other on both sides of the routes were covered with very dense spots for 3D modeling. They claimed this especially concerning the parts with facades that contain denser points facing the roads. The same was said regarding some houses covered with dense vegetation such as trees and bushes; the point density is quite low, and the density information contains a lot of noise. This situation leads to unreliability in the positioning of building corners from point clouds for land-use analysis. They used 2 types of laser scanning in the study, and the planimetric accuracy for the 2 types of laser scanning systems was 3.3 cm and 5.7 cm, respectively [26]. Luo et al. (2017) explore the application of aerial laser scanning (ALS) techniques in cadastral surveys. They developed a strategy for semi-automatic extraction of parcel boundaries from ALS point cloud data and evaluated it for accuracy, completion, and degree of automation by comparing it to existing cadastral maps. An object-based workflow was developed to semi-automatically extract cadastral boundaries from ALS point clouds. They described the outcome of the improved workflow as promising, with about 50% of parcel boundaries being successfully removed. They stated that this study, which investigated the feasibility of point cloud data for cadastral research in general, achieved its main purpose. They also stated that the system may not be suitable for areas with irregularly shaped land such as dense slum areas. Workflow performed better in the more organized suburban area [25]. He and Li (2020) are investigating the system integrating UAV and vehicle LiDAR for cadastral surveying and mapping in an area of 50 hectares. This paper focuses on the data acquired by UAV LiDAR, supplemented by the data acquired by vehicle LiDAR, and the implementation of a rural cadastral survey. This paper analyzes the whole process and accuracy evaluation of cadastral surveys with LiDAR survey technology and verifies the feasibility of the technical route and the reliability of product accuracy. In this experiment, the in-plane error of a cadastral survey combining UAV LiDAR and vehicle-borne LiDAR is about 0.047 m, which is better than the 0.050 m required by the specification and meets the requirements of cadastral surveying and mapping. In addition, compared with the traditional cadastral survey method (GNSS + total station), the overall operation efficiency of cadastral survey using LiDAR surveying technology in this experiment is improved by more than 7 times. This experiment can provide theoretical and practical references for the application of LiDAR scanning technology in related fields. The article also discusses the challenges and limitations of LiDAR technology in cadastral surveying and mapping. One of the primary challenges is the cost of LiDAR technology, which can be expensive. Additionally, LiDAR data processing can be time-consuming and requires specialized software and expertise. The article suggests that these challenges can be addressed through collaboration and partnerships between government agencies and private companies. In conclusion, the article emphasizes the importance of LiDAR technology in cadastral surveying and mapping, highlighting its various applications and benefits. The article suggests that LiDAR technology will continue to play a significant role in cadastral surveying and mapping and calls for further research and development in this field [27]. Šafář et al. (2021) discuss 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 for over twelve test areas. In this study, details of seven test localities in six cadastral units are presented. Point clouds obtained by image matching or from LiDAR and orthoimages were used for detailed cadastral mapping. All the tested UAV measurement methods (image matching, intersection photogrammetry, and laser scanning) met the requirements for the accuracy of point measurements in the Cadastre of the Czech Republic. The accuracy of the points determined from the point cloud obtained by laser scanning was about 18% higher than from the point cloud obtained by matching images with a GSD of 0.02 m. 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 organizational and technological processes in order to utilize UAV-based technologies [28]. Chio and Hou (2022) investigated the feasibility of a handheld LiDAR scanner to efficiently collect 3D point clouds for detailed investigation in urban environments with narrow and winding streets. Point clouds were transformed into the cadastral coordinate system using control points. Terrain feature line data were artificially digitized, and results showed that approximately 97% of errors of digitized feature locations were less than 15 cm compared to control points surveyed by a total station. The results demonstrated the feasibility of using a handheld LiDAR scanner to conduct an urban cadastral detail survey in digitized graphic areas [29]. Teicu et al. (2022) carried out a cadastral survey in an area of 19 ha with a total station, GNSS, UAV photogrammetry, and Mobile LiDAR technologies. Position and altitude were achieved from GNSS data and IMU data using the RTK (Real Time Kinematic) integration algorithm. Mobil LiDAR, which uses the SLAM algorithm, has been preferred as the basic sensor to measure information in the 3D environment. Other methods have been complementary. The accuracy of point clouds and the efficiency of 3D information is 2–4 cm. The authors recognized that the mobile LiDAR system integrated into the backpack could make fast and accurate decisions in emergencies due to fast access to accurate data about real conditions (locations, tunnels, passages, mine galleries, bridges, etc.). As a result, they said that modern technology has improved the accuracy of the data collected for the general cadastre. They stated that the use of Backpack LiDAR and UAV photogrammetry provides fast and high-quality results [30].

2.2. Study Area

The Republic of Turkey’s Ministry of Environment, Urbanization and Climate Change GDLRC (General Directorate of Land Registry and Cadastral), which has a rooted institutional structure, has created the cadastral and topographic cadastral map of the country according to the country-coordinate system by Law No. 3402. The cadastral maps comprise geometry data and land use components visualized by topographic symbols in two-dimension (2D) [19,31]. The study area of this research is located in the city center of Bitlis, Turkey (Figure 1). The province of Bitlis is in the east of Türkiye and receives immigration, so new houses are constantly being built. Bitlis is divided into large rectangular residential blocks that are separated by narrow boulevards. The study area and residential block are located on the left bank of the Van Lake and cover an area of approximately 40 ha, of which 30 ha are built-up areas (30 ha—buildings, and 10 ha—roads, sidewalks, green area, park, etc.). In Bitlis, work has begun building new houses and renewing the current cadastral map. The cadastral mapping studies with WMLS, which were tried in some test regions in Turkey before, were intended to be tested in Bitlis by the Mersin University Geomatics Engineering Department. The purpose of this research is to collect technically detailed building information and transform a realistic 3D city model into a 2D cadastral map. In this sense, data collection was carried out by applying the WMLS method for building wall detection, a cadastral boundary, a park, and a road.

2.3. Simultaneous Localization and Mapping (SLAM) Based WMLS

The advancement of navigation systems over the last two decades has pushed the industrial and scientific communities toward the usage of various types of sensors that are extensively employed in the geomatic community [32,33]. These advancements have aided the development of WMLS technologies, which enable speedy and efficient acquisition of the surrounding environment via data localization, with sensors capable of performing in various weather conditions [28,34,35]. Commercial WMLS instruments are equipped with a variety of navigation sensors, including LiDAR and an Inertial Measurement Unit (IMU). It is optionally equipped with GNSS navigation sensors. Among these components, IMU and GNSS ensure that 3D laser scan data are accurately georeferenced [36,37,38]. In WLMS works without a GNSS navigation system, both local and geographical referencing can be made using data obtained from external control points of GNSS rover receivers.

GNSS refers to a set of satellites that send space signals and transmit location and time data to GNSS rover receivers [39]. The IMU, a low-cost motion measurement method, records linear accelerations (three-axis accelerometer) and rotational speeds (three-axis gyroscope), which are then computationally integrated to provide the three-dimensional position and orientation of an element [40,41]. When combined with location data, the IMU enables the point data generated by the WMLS local framework to be converted into ground-centric-ground-fixed. Therefore, all points are projected into a common frame, and any uncompensated IMU error has a direct impact on the geometric quality of the point cloud [42,43]. The accuracy of the GNSS/IMU navigation system is affected, especially if it consists of low-cost sensors and the signal detection quality of the GNSS is affected [44]. Therefore, significant advances have been made in data-driven [45] and model-driven strategies to improve the position accuracy of the GNSS/IMU navigation system and to eliminate WMLS errors in cases rejected by GNSS. Data-driven methods can be used directly to correct point cloud data, starting with the basics known and using multiple existing correction algorithms. Model-based approaches, on the other hand, establish mathematical models for WMLS systems and analyze error factors to calibrate the resulting deviations [42,46,47]. In addition, Simultaneous Localization and Mapping (SLAM) algorithms in robotics have been investigated in recent years. SLAM algorithms create a map of an environment while locating the mobile platform. The SLAM system requires measuring in a closed loop to increase ultimate accuracy. Different studies have stated that each scan must start and end at a fixed point in order to maintain a closed loop and properly position the data collected in the unknown environment and record the entire point cloud obtained without a GNSS signal [48,49,50].

2.3.1. SLAM Algorithm

The WMLS data collection system works with the SLAM algorithm principle. This technique enables the creation of a map of an unknown environment by passing through distance sensors while simultaneously determining the system on the map [51,52]. Thus, it provides the opportunity to obtain point clouds by moving from different areas and scanning from different locations [53,54]. Working with the SLAM algorithm, the device receives data from sensors (Velodyne Puck LITE LiDAR sensor, IMU sensor) to discover where it is in the environment. The SLAM algorithm uses this data to detect the variability of geometric (wall, floor, column, etc.) objects around a system to create a locally coordinated environment map and calculate the best estimate of where it is when determining its location [16,55,56].

In order to determine the location of the area measured with SLAM, two main strategies are applied: “absolute positioning with feature matching” and “relative positioning with scan matching”. The first strategy maps the detected feature (lines, corners, circles, etc.) to a generated feature map that allows location recognition. In other words, it provides integrated data by combining laser data collected from different angles using geometric variability and conjugate points. The second strategy uses the Iterative Closest Point (ICP) algorithm to match two or more scan points to obtain the movement performed by WMLS. The point clouds obtained by the orbit are mapped and sequentially predicted and produced. In the ICP method, data association is made by searching for the closest point between two different point clouds. Therefore, the SLAM algorithm performs better when applied indoors with regular and repetitive features, while performing poorly when applied outdoors due to complex and irregular features detected by the laser scanner. However, when GNSS data and other supporting data collection methods (such as total-station data) are integrated, it also performs well outdoors [57,58].

Irregularities in outdoor spaces create difficulties in detecting sudden movements or the entire area, increasing the computational load and complexity of algorithm design [59]. The combination between the GNSS/IMU navigation system and SLAM will successfully reduce navigation bias when the GNSS signal is not clear and provides absolute navigation information not provided by the SLAM algorithms [60,61,62,63,64].

2.3.2. WMLS Equipment

The Heron Lite Color laser scanner used in the study works with the time-of-flight method. The WMLS device, the technical specifications of which are given in Table 2, works with the principle of calculating the time it takes for the laser pulse sent to an object to reach the object, reflect on the object, and return to the sensor. Due to its ability to provide distance information, this has important implications in current research areas such as instant object detection and object reconstruction. Sometimes the method used in this device contains built-in cameras and a source of error that affects the accuracy of the measured distance information. Heron Lite Color minimizes errors by applying this method with calibrated reflectors. First, the trajectories for the wearable mobile scanning process were determined by a reconnaissance in the study area. While determining the trajectory, attention was paid to how the starting point and end point were at the same location. That is, the measurement trajectory has been created to start at the determined point and end at the starting point after the line is completed.

2.3.3. WMLS Process Parameters

The processing of data obtained from WMLS scanning can be grouped under three main parameters: “odometer”, “create maps”, and “global optimization”.

The first section odometer’s purpose is to minimize distortions due to the structure of the land, location of objects, and environmental factors. The odometer section consists of cloud filtering, local-map, and registration sub-parameters.

The first parameter entry of the odometer section is the cloud filtering settings. The purpose of the odometer section is to minimize distortions due to the structure of the land, the location of objects, and environmental factors.

The second parameter entry is the local-map settings. The purpose of the local-map section is to determine the geometric criteria of the 3D point cloud map for it to be created.

The third parameter input is the registration aspect. In the laser scanning process, the scans are superimposed. In the obtained data, one of the measurements for the same object is taken as a reference (fixed) and all other scans are converted to the coordinate system of the reference point cloud. In the registration part, the conversion parameters between the two-point clouds are calculated with the points in the common scan area. The purpose of the registration section is to reduce the minimum error and obtain a precise result.

The second section that creates a series of maps by separating the scanned data into map sets is the “create map” section. The purpose of the create map section is to align the separated sets of maps and produce a more precise 3D model. In order to produce a more precise 3D model, the sharpness of the building corners must be determined in detail. The Harris Detector algorithm was used to detect the corners and spikes of the structures in the study area. The Harris corner detector is a corner detection operator commonly used in computer vision algorithms to extract corners and extract features from an image. Due to the algorithm. more precise results can be obtained by converting a 3D model to a 2D orthophoto with parameter values.

The third section of the global optimization part is to increase the precision of alignment and accuracy of segmented maps. In this section, the segmented maps are connected together by a tie. Here, when connecting the two maps, overlap and Root-mean-square deviation (RMSD) values were taken into account. The data of the common areas of the two segmented point clouds determined the overlap rate. The smaller the overlap ratio, the higher the RMSD value between the two-point cloud data, while an increase in the overlap ratio will decrease the RMSD value. For this reason, the default settings of the overlap ratio between two-point clouds are used in the study. Finally, all segmented maps have been tied and aligned accordingly.

The final step for stable point cloud data is referencing. The workflow used in the study is provided in Figure 2.

2.3.4. Referencing

In the case of WMLS point clouds, indirect reference is usually performed with targets specifically designed for the purpose of Target/Building Control Point/Ground Control Point (TAG/BCP/GCP). The positions of these targets are typically measured with conventional measuring devices, such as a total station. This information is then integrated into the WMLS point cloud. The TAG/BCP measured by the total-station device and GNSS was transferred to the Reconstructor software for georeferencing. By using these TAG points, the transformation from the local coordinate to the desired coordinate system was performed.

Point clouds collected with WMLS are referenced with points from GNSS belonging to the same ground. For this purpose, the coordinates (International Terrestrial Reference Frame 96/ITRF96) of 120 points from the field (~40 hectares) were taken with the Topcon Hiper SR series GNSS rover. In addition, since the point clouds obtained are composed of 3D data, the Topcon ES-60 Series total-station device was used from the high and sharp points of the buildings. This device can measure up to 4000 m in outdoor environments using a standard reflector. As the upper sides of the buildings were measured, the none-prism mode was used. With the Topcon ES-60 Total-station survey device, 3 mm + 2 ppm sensitivity can be measured for up to 500 m in none-prism mode. Since the maximum distance between the fixed device and the building control points in the study area is 30 m, the coordinates were measured with an accuracy of 3.60 mm and this sensitivity played an active role in the preference of this device. A total of 106 pieces of data were collected from the building’s façades with a total station in the study area. All of the data from the total station and 80 of the GNSS data were used for reference. The remaining 40 GNSS data and WMLS data from the same location were used for accuracy analysis. In addition, a line-based accuracy analysis of the building and border corners was made. For this, 60 points (30 lines) were determined. Total-station data, which is none-used in reference, was preferred in the determination of these points and lines.

2.4. Data Collection

In this study, WMLS data were collected in an area of approximately 40 hectares. In the process of collecting WMLS data, coordinate data were collected from the study area with simultaneous GNSS and total-station devices (Figure 3).

Considering the size of the study area, it was divided into seven parts during the planning phase and eight parts during the data collection process. Before scanning, areas of 4–6 hectares were determined for an average of 25 min to ensure the quality of the data and optimum sensitivity. For this purpose, firstly, routes were determined for the wearable mobile scanning process applied in the study. While determining the route, attention was paid that the starting point and end point were at the same location. That is, the measurement route was created to start at the determined point and end at the starting point after the line is completed (Figure 4).

The fact that the study area is completely outdoors leads to the excess of environmental factors (car, vegetation, etc.) and the density of living things (human, animal, etc.). Therefore, some precautions should be taken to avoid movement and stationary objects that may generate noise during the data collection process. First, scans were performed at the time of minimum mobility throughout the trajectory. During the scanning, an assistant operator was found beside the scanning operator. Before the scanning on the trajectory took place, the assistant operator moved about fifty meters in advance and attempted to take measures to minimize environmental impacts. Therefore, data outside 1.5–30 m from the scanning device were not included in the process to eliminate noise data during the scanning-data processing stage. Since the mobile laser scanning device used in this study is designed to be wearable, carrying apparatuses are placed on the operator’s body. The pole, on which the laser sensor is mounted, is held by hand and exposed to operator-induced oscillations. This leads to errors in the IMU that records the trajectory. For this reason, trajectories were determined in such a way that there would be minimum vibration throughout the scanning, and especially bumpy areas were avoided.

The locations of the coordinate data collected simultaneously during the WMLS data collection were determined by exploration beforehand (Figure 5). These data were used for both referencing and accuracy assessment. In the selection of the location of the GNSS points taken over the ground, attention was paid to ensuring that the points see each other and create a closed polygon. Although it was planned to collect point data in optimum numbers for each scanning session during the exploration, it could not be obtained due to the current condition of the structures and the inaccessibility of unused lands. Since the coordinate data taken from the building is of great importance in referencing the 3D WMLS data, the same points were taken with the repeated measurement method at in a 2-day meantime.

2.5. Accuracy Analysis

Since the study was carried out in cooperation with the institution that carries out cadastral applications in Turkey, the articles of the regulation were taken as references for the accuracy of the final products. In order to produce cadastral maps on a 1/1000 scale to be made in Turkey, the accuracy values specified in the Large Scale Map and Map Information Production Regulation were taken into account. The relevant articles of this regulation are provided below.

Detail measurement accuracy (Article 45): horizontal position accuracy ${\left({\sigma }_x^2+{\sigma }_y^2\right)}^{1/2}$ determined by the projection coordinates of the detail points is ±7 cm and Helmert orthometric height accuracy ${\sigma }_H$ is ±7 cm; electronic tachometer, leveling with prismatic reception, detail measurements with GNSS or techniques and methods that provide similar accuracy can be used.

σ = Standard deviation

Facade control in detail measurements (Article 47): The locations of the main points of the parcel, island, building, and engineering facilities should be determined by drawing the facade or by measurements from another point in cases where it is not possible to draw the facade. The difference between the amount calculated from the dimensions and the measured value of the facades is defined as d.

This cannot be more than the amount found by the formula $d=0.03+0.0005$ S. Here, S is the length of the facade in meters and d is in meters as well. Differences between projection coordinates were calculated from two independent measurements. Differences between dx, dy, and Helmert orthometric heights $dH; dx, dy, dH\le 8 \mathrm{cm}$.

Two separate accuracy analyses were performed in this study.

First, the spatial and geometric accuracy of the WMLS point cloud was investigated. At this point, analyses were carried out using Equations (1) and (2), and the TAG (40) measurements were taken from GNSS, and coordinate data was obtained from the WMLS data. Here, GNSS measurements were accepted as a reference, and X, Y, and Z spatial accuracy analyses of WMLS data were performed.

In addition, for the accurate analysis of the data, a coordinate-based line comparison was made by taking the same points from the total station data with 60 points taken from the building and parcel border corners from the WMLS data. The aim here was to find the difference between the reference data and the WMLS data. For this comparison, the Euclidean distance of the lines consisting of points was calculated with Equation (3). The Euclidean distance between 2 points in a 2-dimensional or 3-dimensional space is the straight length of a line connecting 2 points and the most obvious way to represent the distance between 2 points [66,67,68]. Since the Euclidean distance is defined in Euclidean space as a function that determines the straight-line distance, it is considered a metric space. The 3D distance formula is used to calculate the distance between 2 points in a 3-dimensional space, also known as the Euclidean distance formula. For 3-dimensional space, the Euclidean distance between the 2 points p and q with coordinates (x₁, y₁, z₁) and (x₂, y₂, z₂) is determined as Equation (3) [69]. Firstly, using Equation (3), 3D distances between 30 lines (60 points) were calculated with values from both total station (Ts) and WMLS coordinate (x, y, z) data. Each p and q value given in Equation (3) represents the first and second points for each line. After the 3D distances were found for both data, the difference between Ts and WMLS data for each line was calculated by Equation (4). Here Ts data was accepted as a reference. As a result, Ts data and WMLS data were compared on a 3D line basis. As a result of this comparison, for the accuracy of both the coordinate data from GNSS tag data (${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}$) and lengths data from Ts data (${\mathrm{RMSE}}_{\mathrm{l}}$), the root means square errors (RMSE) were calculated.

(1)${\mathrm{V}}_{{\mathrm{x},\mathrm{y},\mathrm{z},}_{\mathrm{i}}}{=\mathrm{X},\mathrm{Y},\mathrm{Z}}_{{\mathrm{GNSS}}_{\mathrm{i}}{,\mathrm{CD}}_{\mathrm{i}}}-{\mathrm{X},\mathrm{Y},\mathrm{Z}}_{{\mathrm{WMLS}}_{\mathrm{i}}}$

(2)${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}=\sqrt{\frac{\left[\mathrm{VV}\right]}{\mathrm{n}-1}}$

(3)${3\mathrm{Ddistance}}_{\mathrm{Eucl}(\mathrm{Ts},\mathrm{WMLS})}(\mathrm{p},\mathrm{q})=\sqrt{{{(\mathrm{X}}_{\mathrm{p}}-{\mathrm{X}}_{\mathrm{q}})}^2{+(\mathrm{Y}}_{\mathrm{p}}-{\mathrm{Y}}_{\mathrm{q}}{)}^2{+(\mathrm{Z}}_{\mathrm{p}}-{\mathrm{Z}}_{\mathrm{q}}{)}^2}$

(4)${RMSE}_l=\sqrt{\frac{{\left|{\mathrm{l}}_{\mathrm{i}}\right|}^2}{\mathrm{n}}}$

where

GNSS: TAG coordinates measured with GNSS.

WMLS: local coordinates taken from wearable mobile laser scanner data.

Ts: Coordinates taken from the total station.

li: differences between actual length and length are taken from the model.

n: number of measures [69].

3. Results

3.1. WMLS Data Registration

The processing of the data obtained as a result of the WMLS scanning was carried out in three steps: an odometer, ICP, and a create map. First, the distance limit is set to remove noise from moving objects. Threshold values were determined as 0.20 m < threshold < 1.00 m. The laser pulse distance was extended to reduce noise due to surrounding moving objects. In addition, the threshold value was increased as much as possible due to the increase in the distance between the device and the target object in order to perform as many laser pulses as possible on the upper parts of the structures. The second part, the ICP algorithm, is aimed at finding the transformation between a point cloud and some reference surface (or another point cloud) by minimizing the frame errors between the corresponding entities. At this stage, the “local map” settings were made and the geometric criteria of the 3D point cloud map to be created were determined. After the odometer, the “create map” section was started. Here, a series of maps were created by dividing the entire trajectory into a series of maps. After the map creation process, the global optimization part was started (Figure 6). In this step, the segmented maps are aligned. In the parameter setting in this section, the default value of the ICP iteration is selected. The main purpose of this section is to increase accuracy. After parameter setting, the segmented maps are connected together with a tie (Figure 6, green and blue frame).

After tying, the segmented point clouds created were referenced with the data collected in the field. For this process, 186 (GNSS/Total-station: 80/106) pieces of data were used. GNSS data are designed to be two per hectare. For total-station point data, it is designed to be taken in both structures. In the study, the merging precision of WMLS data before referencing was 4.8 mm. The most important factor affecting WMLS joining precision is noise caused by environmental factors and errors caused by trajectory. As a result of the referencing, the error of bringing the WMLS data to the ground truth was found to be 2.3 cm. As a result of the referencing, a 3D WMLS point cloud was created in the spherical coordinate system of the study area (Figure 7).

Then, a 2D cadastral map was produced using WMLS data (Figure 8). Drawings were made on the produced map. However, as seen in Figure 8, there are gaps in the produced orthophoto map due to WMLS data. Therefore, both orthophoto and 3D point clouds should be used in the drawing map process. The orthophoto map produced was produced at 1 cm/pix resolution. These values can be determined by the user, and the reason for choosing 1 cm/pix in the study is that the same resolution air orthophoto of the area is used in the drawings. In such an application, drawing only from the point cloud causes data confusion and noise. Therefore, as the authors, it is recommended to use point cloud data and orthophoto data in an integrated manner, as applied in the study.

3.2. Accuracy Analysis WMLS Data

Accuracy analysis is the final step to measure the accuracy and reliability of the study. For this purpose, firstly, GNSS TAG (40) data and WMLS data were compared. In addition, total-station (60) data were used for length-based accuracy analyses. As a result of the comparison of 3D WMLS point data, the square mean errors of the X, Y, and Z coordinates ${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}=4.1324,4.9095,7.7660$ are given in Table 3. The accuracy of ${\mathrm{RMSE}}_{\mathrm{l}}=4.7589$ calculated from the comparison of the lines based on the lengths given in Table 4.

In addition, when the points were analyzed, the min error values were 0.03, 0.52, and 0.41 cm for X, Y, and Z, respectively. Max error values were 8.07, 9.96, and 13.24 cm for X, Y, and Z, respectively. Mean error values were calculated as 3.49, 4.51, and 6.69 cm for X, Y, and Z, respectively. Finally, the standard deviation values for X, Y, and Z were calculated as 2.14, 1.81, and 3.80 cm, respectively. According to these values, X, Y standard deviation was found to be 2.80 cm without removing outliers for Horizontal position accuracy within the scope of Large-Scale Map and Map Information Production Regulation. According to the regulation, these values are below the error limit. In addition, when outliers were removed, mean square errors of X, Y, and Z coordinates for 18 points were calculated as ${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}=3.81,4.27,4.93$, respectively. In addition, after the outliers were removed, the standard deviation values were calculated as 2.03, 1.29, and 2.06 for X, Y, and Z, respectively. All points were included in the accuracy analysis because the results including outliers and the results after subtraction were both in compliance with the regulation and the study included a field-based study rather than a point-based study.

4. Conclusions

With recent advances in remote sensing technology, WMLS point clouds have received more attention and are increasingly becoming used in various urban management and planning applications. A relatively new approach for updating the cadastral can be provided using 3D laser scanning data obtained from a portable sensor. Despite the incredible potential of the WMLS Mapping System, it has been seen that human intervention is still essential and post-processing actions are necessary to achieve desired results regardless of domain. This study provides insight into the technical and methodological limitations of WMLS for 3D measurement, which casts general doubt, and highlights that in most cases additional data are required to make the final result reliable. WMLS has proven to be a valuable alternative to the combination of heterogeneous data, which is useful in different scenarios. In the field of natural land monitoring, portable WMLS has not yet found widespread application and a limited number of studies have evaluated the technology in this context. In this study, results were obtained conveying that a portable WMLS could be a viable alternative to photogrammetry, especially for outdoor mapping when a flight with an unmanned aerial vehicle is not allowed or a GNSS survey of the ground control point is not possible.

Considering the previous case studies, Luo et al., 2017 [25] stated that the overlapping of most of the cadastral boundaries with topographic objects in the study area contributed to the proposed method. They also stated that the system would not be suitable for areas with irregularly shaped land such as dense slum areas. They reported that the system performed better in a more organized suburban area. They urged more research on the relationship between airborne laser systems and topographic objects and parcel boundaries. Zhong et al., 2016 [26] mentioned that they had problems, especially with GNSS signals in the LiDAR data they collected by integrating the classical terrestrial laser scanning system into a vehicle in the urban working area. Various obstacles on the ground, especially tall buildings, and dense trees reported that the signals were not available to assist the navigation solution while the satellite was operating. He and Li, 2020 [27] focused on the data obtained by the UAV LiDAR and vehicle LiDAR in the rural cadastral survey. In this experiment, the in-plane error of cadastral survey combining UAV LiDAR and vehicle-borne LiDAR is about 0.047 m, which is better than the 0.050 m required by the specification, which meets the requirements of cadastral surveying and mapping. Chio and Hou (2022) [29] examined the feasibility of using a handheld LiDAR scanner for an urban cadastral detail survey. They conveyed that being a handheld LiDAR scanner in an urban area is easy to use in narrow lanes where it is not easy for GNSS to receive satellite signals and a total station or a ground LiDAR. When using detail points examined by a total station to validate line data digitized from the handheld LiDAR point cloud, they found that 97% of the digitized data had an error of less than 15 cm. They stated that the digitized detail data is sufficient for the urban cadastral detail survey in the cadastral graphic digitization area in Taiwan. Teicu et al. (2022) carried out a cadastral survey in an area of 19 ha with a total station, GNSS, UAV photogrammetry, and Mobile LiDAR technologies. Mobil LiDAR, which uses the SLAM algorithm, has been preferred as the basic sensor to measure information in a 3D environment. The accuracy of point clouds and the efficiency of 3D information is 2–4 cm. To examine other studies, it is seen that LiDAR sensors are an alternative, especially at points where known measurement methods are insufficient. Regarding narrow urban areas, as in this study, satellite signals were weak and measurements were negatively affected in terms of time and cost. In addition, full data on parcel and building boundaries cannot be collected, especially due to plants and trees, in UAV or airborne photogrammetry/LiDAR methods. In these and similar cases, MLS methods working with the SLAM algorithm create important solutions. The WMLS system should allow different sensors to be easily installed and used to meet users’ demands for mapping different environments. Deep learning solutions should be integrated into the WMLS system at all processing levels, from navigation and device calibration to 3D scene reconstruction and interpretation.

In this study, the performance of HERON Lite Color portable WMLS was investigated in measuring cadastral areas. The results in this study confirm the results described in previous studies reporting an accuracy ranging from 5 to 10 cm for the investigation of civil structures or urban areas with portable systems. Most studies with values (5–10 cm) that we have confirmed for accuracy involve a single structure or small areas. In such a large field as this study, these accuracies are promising. Currently, this technology does not appear to be suitable for applications requiring high-precision measurements; however, it offers a valuable and cost-effective solution for the rapid measurement of large sites. One of the best ways to achieve this is to combine different Earth observation data into an intelligent geospatial information system. In particular, data from WMLS, which is one of the range-based LiDAR data collection methods, can be used for future sustainable solutions for cadastral studies.

Future research should focus more deeply on evaluating portable systems for cadastral applications and identifying best practices for data collection and processing for these scenarios. This is due to how cadastral renewal works are frequently carried out in countries such as Turkey, where construction work is fast and constant. In addition, other alternative mobile devices cannot be efficient for areas with dense and narrow construction. Therefore, one of the aims of this study is to show that the use of WMLS can be effective in such areas. In addition, more advanced cameras should be used as visual sensors for navigation.

Author Contributions

Conceptualization, M.Y., A.U., A.Y.Y. and S.N.G.H.; investigation, S.N.G.H. and A.Y.Y.; writing—original draft preparation, A.Y.Y. and S.N.G.H.; writing—review & editing, A.Y.Y., S.N.G.H. and A.U.; project administration, M.Y. and A.U. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The responsible author can be contacted for all data used in the article.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Location of the study area in Bitlis/Türkiye.

Figure 2. WMLS point cloud data collection procedure and general workflow diagram.

Figure 3. Equipment used in the study area (GNSS cover—WMLS devices—total-station).

Figure 4. The trajectory for collecting WMLS point cloud data (8 sections). Each color shows a section.

Figure 5. Geodetic point data survey together with WMLS point cloud data. Simultaneously measured point data (GNSS/total station) along with WMLS point cloud data. Red points, GNSS TAG points for reference. Blue points, total-station points for reference. Yellow points for WMLS point cloud accuracy analysis from GNSS points.

Figure 6. Segmented trajectory (global optimization) and tying of segmented trajectory together (blue and green frame).

Figure 7. 3D WMLS point cloud (left), general view of the study area. On the color scale, elevation increases from blue to red (right).

Figure 8. Top view of raster map (top) produced from WMS point cloud. 2D cadastral map (middle) drawn from raster map. Top view of raster map, and 2D cadastral map (bottom).

Table 1

Research articles for related work summary table.

Research Article for Related Work		LiDAR				UAV-Airborne Photogrammetry	Traditional Method GNSS Total Station
		Caretc.	UAVAirborne	Terrestrial	WearableHand-HeldBackpack
Luo et al., 2017	[25]		✓
Zhong et al., 2016	[26]	✓					✓
He and Li, 2020	[27]	✓	✓				✓
Šafář et al., 2021	[28]		✓			✓	✓
Chio et al., 2021	[29]			✓	✓		✓
Teicu et al., 2022	[30]				✓	✓	✓

The methods and equipment used in the studies are indicated with ✓.

Table 2

Technical Performance Specifications of mobile LiDAR Gexcel Heron Lite Color [65].

Technical Characteristic of Gexcel Heron Lite Color	Value
Brand and type	Velodyne VLP 16
Measurement range	0.4–100 m indoor or outdoor
Measurement speed	Up to 300,000 points/per second
Ranging Accuracy (for measurements of 10–100 m)	±3 cm
Absolute accuracy [1 sigma in cm]	±3 cm
Max survey resolution	~2 cm
Field of view (vertical/horizontal)	360° V/360° H
Laser class	Laser class 1

Table 3

Accuracy evaluation of WMLS Data for the TAG of GNSS Data.

No	Vx	Vy	Vz	No	Vx	Vy	Vz	No	Vx	Vy	Vz
1	−3.41	3.61	8.31	15	−4.95	5.21	7.09	29	−2.84	4.66	−2.20
2	3.88	−4.62	−0.88	16	4.37	4.67	12.46	30	3.60	−7.12	2.77
3	−5.91	2.54	7.49	17	−5.53	−2.37	9.08	31	−3.78	−4.20	8.27
4	0.14	3.96	−13.24	18	−0.29	8.02	−7.37	32	2.94	9.96	9.82
5	0.32	4.64	10.23	19	2.14	5.19	−2.62	33	−0.71	0.52	−6.84
6	−3.80	2.32	12.64	20	6.82	−5.35	7.24	34	1.98	−8.74	2.51
7	4.55	−3.71	−4.92	21	−8.07	3.08	−0.80	35	5.48	5.47	−4.45
8	3.01	5.33	8.40	22	2.29	3.43	12.64	36	−2.87	−2.48	4.40
9	1.57	4.10	2.07	23	5.37	−3.67	−11.70	37	−5.32	−3.77	5.72
10	3.81	5.02	−11.42	24	4.21	4.85	−10.11	38	0.98	4.27	6.03
11	−4.55	−2.84	11.48	25	3.02	5.13	2.88	39	7.76	7.07	−6.85
12	0.54	−2.36	1.98	26	3.40	−4.62	9.45	40	3.43	4.87	−4.65
13	−7.30	5.14	0.41	27	0.58	−3.34	1.82	${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}$ = 4.1324, 4.9095, 7.7660
14	0.03	3.42	−4.67	28	4.20	4.59	9.49

${\mathrm{V}}_{\mathrm{x},\mathrm{y},\mathrm{z}}$ and ${\mathrm{RMSE}}_{\mathrm{x},\mathrm{y},\mathrm{z}}$ values are in cm. Outliers are those underlined.

Table 4

Accuracy evaluation of the WMLS length values from the total-station-length values.

No	${\mathit{l}}_{\mathit{i}}$ (cm)	No	${\mathit{l}}_{\mathit{i}}$ (cm)	No	${\mathit{l}}_{\mathit{i}}$ (cm)
1	1.616	11	2.988	21	3.044
2	6.608	12	4.775	22	7.498
3	4.376	13	2.444	23	4.573
4	1.004	14	8.209	24	6.548
5	7.408	15	3.872	25	4.611
6	1.349	16	4.361	26	7.348
7	0.002	17	1.047	27	5.165
8	3.185	18	4.604	28	1.450
9	4.292	19	4.151	29	7.676
10	6.395	20	0.754	30	3.927
$RMS{E}_l=$ 4.7589 cm

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