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Special Issue "Advances in Mobile Laser Scanning and Mobile Mapping"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2013)

Special Issue Editor

Guest Editor
Prof. Juha Hyyppä

Finish Geospatial Research Institute, Masala, Finland
Website | E-Mail
Interests: laser scanning (airborne, mobile and terrestrial); 3D remote sensing; individual tree detection; virtual forests

Special Issue Information

Dear Colleagues,
The laser scanning market including hardware, software and services is growing roughly at the annual rate of 15 percent. Terms Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS) and Terrestrial Laser Scanning (TLS) are applied in laser scanning (LS) depending whether the platform is an aircraft, moving vehicle or tripod, respectively. The segment of MLS is perhaps the highest growing area within LS. MLS and mobile mapping is also used by large worldwide information technology companies providing street views. The number of ISI Web of Science listed high-quality papers in this emerging area is still quite modest. This special issue proposes to promote innovative scientific contributions, which may include novel data processing approaches, new applications, benchmarking studies, new hardware solutions and multidisciplinary approaches from this important technology area.
Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript of their latest research results in the field of advances in mobile laser scanning and mobile mapping. Also reviews contributions are welcomed. Contributions may be from, but not limited to:

  • Innovative multidisciplinary concepts and applications
  • Techniques for the fusion of MLS with other sensors (including ALS)
  • New methods in information extraction, i.e. automated feature extraction and object recognition, from all kinds of laser or ranging data to all applications
  • forest
  • Mobile terrestrial laser scanning hardware and platform (including UAV/UAS) developments
  • Accuracy and performance evaluations of the systems
  • Use of MLS in indoor 3D mapping
  • Creation of photorealistic models with MLS/mobile mapping
  • Affordable mobile mapping systems and applications
  • Improvement of georeferencing solutions in GNSS shadows

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Keywords

  • laser scanning
  • lidar
  • mobile laser scanning
  • airborne laser scanning
  • terrestrial laser scanning
  • indoor
  • 3D
  • virtual reality
  • photorealistic models
  • feature extraction and data processing methodology
  • accuracy and quality
  • data fusion
  • application
  • UAV/UAS
  • texture mapping
  • GNSS
  • georeferencing

Published Papers (15 papers)

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Research

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Open AccessArticle Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System
Remote Sens. 2013, 5(12), 6611-6646; doi:10.3390/rs5126611
Received: 18 October 2013 / Revised: 25 November 2013 / Accepted: 28 November 2013 / Published: 3 December 2013
Cited by 14 | PDF Full-text (8768 KB) | HTML Full-text | XML Full-text
Abstract
Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting
[...] Read more.
Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Multi-Sensor Platform for Indoor Mobile Mapping: System Calibration and Using a Total Station for Indoor Applications
Remote Sens. 2013, 5(11), 5805-5824; doi:10.3390/rs5115805
Received: 10 September 2013 / Revised: 19 October 2013 / Accepted: 22 October 2013 / Published: 6 November 2013
Cited by 4 | PDF Full-text (19222 KB) | HTML Full-text | XML Full-text
Abstract
This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second
[...] Read more.
This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second part of the calibration, the entire system will be described regarding the interaction of laser scanners and other parts of the system. Finally, the preliminary analysis of the use of a total station is presented in conjunction with the measurement system. The difficulty of time synchronization is also discussed. In multiple tests, a comparison was made versus a reference solution based on GNSS. Additionally, the suitability of the total station was also considered for indoor applications. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning
Remote Sens. 2013, 5(10), 5285-5303; doi:10.3390/rs5105285
Received: 6 September 2013 / Revised: 16 October 2013 / Accepted: 16 October 2013 / Published: 22 October 2013
Cited by 9 | PDF Full-text (4924 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based
[...] Read more.
Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Open AccessArticle Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner
Remote Sens. 2013, 5(10), 4839-4856; doi:10.3390/rs5104839
Received: 8 July 2013 / Revised: 9 September 2013 / Accepted: 25 September 2013 / Published: 8 October 2013
Cited by 11 | PDF Full-text (549 KB) | HTML Full-text | XML Full-text
Abstract
Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically require
[...] Read more.
Accurate vehicle localization in forest environments is still an unresolved problem. Global navigation satellite systems (GNSS) have well known limitations in dense forest, and have to be combined with for instance laser based SLAM algorithms to provide satisfying accuracy. Such algorithms typically require accurate detection of trees, and estimation of tree center locations in laser data. Both these operations depend on accurate estimations of tree trunk diameter. Diameter estimations are important also for several other forestry automation and remote sensing applications. This paper evaluates several existing algorithms for diameter estimation using 2D laser scanner data. Enhanced algorithms, compensating for beam width and using multiple scans, were also developed and evaluated. The best existing algorithms overestimated tree trunk diameter by ca. 40%. Our enhanced algorithms, compensating for laser beam width, reduced this error to less than 12%. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Open AccessArticle Automatic Estimation of Excavation Volume from Laser Mobile Mapping Data for Mountain Road Widening
Remote Sens. 2013, 5(9), 4629-4651; doi:10.3390/rs5094629
Received: 30 July 2013 / Revised: 29 August 2013 / Accepted: 12 September 2013 / Published: 17 September 2013
Cited by 8 | PDF Full-text (2048 KB) | HTML Full-text | XML Full-text
Abstract
Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a
[...] Read more.
Roads play an indispensable role as part of the infrastructure of society. In recent years, society has witnessed the rapid development of laser mobile mapping systems (LMMS) which, at high measurement rates, acquire dense and accurate point cloud data. This paper presents a way to automatically estimate the required excavation volume when widening a road from point cloud data acquired by an LMMS. Firstly, the input point cloud is down-sampled to a uniform grid and outliers are removed. For each of the resulting grid points, both on and off the road, the local surface normal and 2D slope are estimated. Normals and slopes are consecutively used to separate road from off-road points which enables the estimation of the road centerline and road boundaries. In the final step, the left and right side of the road points are sliced in 1-m slices up to a distance of 4 m, perpendicular to the roadside. Determining and summing each sliced volume enables the estimation of the required excavation for a widening of the road on the left or on the right side. The procedure, including a quality analysis, is demonstrated on a stretch of a mountain road that is approximately 132 m long as sampled by a Lynx LMMS. The results in this particular case show that the required excavation volume on the left side is 8% more than that on the right side. In addition, the error in the results is assessed in two ways. First, by adding up estimated local errors, and second, by comparing results from two different datasets sampling the same piece of road both acquired by the Lynx LMMS. Results of both approaches indicate that the error in the estimated volume is below 4%. The proposed method is relatively easy to implement and runs smoothly on a desktop PC. The whole workflow of the LMMS data acquisition and subsequent volume computation can be completed in one or two days and provides road engineers with much more detail than traditional single-point surveying methods such as Total Station or GPS profiling. A drawback is that an LMMS system can only sample what is within the view of the system from the road. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Open AccessArticle 3D Modeling of Coarse Fluvial Sediments Based on Mobile Laser Scanning Data
Remote Sens. 2013, 5(9), 4571-4592; doi:10.3390/rs5094571
Received: 28 July 2013 / Revised: 6 September 2013 / Accepted: 6 September 2013 / Published: 16 September 2013
Cited by 9 | PDF Full-text (1320 KB) | HTML Full-text | XML Full-text
Abstract
High quality sedimentary measurements are required for studying fluvial geomorphology and hydrological processes e.g., flood and river dynamics. Mobile laser scanning (MLS) currently provides the opportunity to achieve high precision measurements of coarse fluvial sediments in a large survey area. Our study aims
[...] Read more.
High quality sedimentary measurements are required for studying fluvial geomorphology and hydrological processes e.g., flood and river dynamics. Mobile laser scanning (MLS) currently provides the opportunity to achieve high precision measurements of coarse fluvial sediments in a large survey area. Our study aims to investigate the capability of single-track MLS data for individual particle-based sediment modeling. Individual particles are firstly detected and delineated from a digital surface model (DSM) that is generated from the MLS data. 3D MLS points of each detected individual particle are then extracted from the point cloud. The grain size and the sphericity as well as the orientation of each individual particle are estimated based on the extracted MLS points. According to the evaluations conduced in the paper, it is possible to detect and to model sediment particles above 63 mm from a single-track MLS point cloud with a high reliability. The paper further discusses the strength and the challenges of individual particle-based approach for sedimentary measurement. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle SVM-Based Classification of Segmented Airborne LiDAR Point Clouds in Urban Areas
Remote Sens. 2013, 5(8), 3749-3775; doi:10.3390/rs5083749
Received: 4 May 2013 / Revised: 17 July 2013 / Accepted: 18 July 2013 / Published: 31 July 2013
Cited by 17 | PDF Full-text (2370 KB) | HTML Full-text | XML Full-text
Abstract
Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed
[...] Read more.
Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Automatic Removal of Imperfections and Change Detection for Accurate 3D Urban Cartography by Classification and Incremental Updating
Remote Sens. 2013, 5(8), 3701-3728; doi:10.3390/rs5083701
Received: 27 May 2013 / Revised: 12 July 2013 / Accepted: 16 July 2013 / Published: 30 July 2013
Cited by 5 | PDF Full-text (5928 KB) | HTML Full-text | XML Full-text
Abstract
In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified
[...] Read more.
In this article, we present a new method of automatic 3D urban cartography in which different imperfections are progressively removed by incremental updating, exploiting the concept of multiple passages, using specialized functions. In the proposed method, the 3D point clouds are first classified into three main object classes: permanently static, temporarily static and mobile, using a new point matching technique. The temporarily static and mobile objects are then removed from the 3D point clouds, leaving behind a perforated 3D point cloud of the urban scene. These perforated 3D point clouds obtained from successive passages (in the same place) on different days and at different times are then matched together to complete the 3D urban landscape. The changes occurring in the urban landscape over this period of time are detected and analyzed using cognitive functions of similarity, and the resulting 3D cartography is progressively modified accordingly. The specialized functions introduced help to remove the different imperfections, due to occlusions, misclassifications and different changes occurring in the environment over time, thus ncreasing the robustness of the method. The results, evaluated on real data, demonstrate that not only is the resulting 3D cartography accurate, containing only the exact permanent features free from imperfections, but the method is also suitable for handling large urban scenes. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario
Remote Sens. 2013, 5(8), 3662-3680; doi:10.3390/rs5083662
Received: 31 May 2013 / Revised: 17 July 2013 / Accepted: 17 July 2013 / Published: 25 July 2013
Cited by 8 | PDF Full-text (2126 KB) | HTML Full-text | XML Full-text
Abstract
To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are
[...] Read more.
To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Performance Analysis of Mobile Laser Scanning Systems in Target Representation
Remote Sens. 2013, 5(7), 3140-3155; doi:10.3390/rs5073140
Received: 28 April 2013 / Revised: 9 June 2013 / Accepted: 13 June 2013 / Published: 24 June 2013
Cited by 4 | PDF Full-text (578 KB) | HTML Full-text | XML Full-text
Abstract
The technology of mobile laser scanning (MLS) has developed rapidly in recent years. This speedy development is evidenced by the emergence of a variety of MLS systems in commercial market and academic institutions. However, the producers tend to supply the specifications of the
[...] Read more.
The technology of mobile laser scanning (MLS) has developed rapidly in recent years. This speedy development is evidenced by the emergence of a variety of MLS systems in commercial market and academic institutions. However, the producers tend to supply the specifications of the individual sensors in a generic sense, and this is not enough for guiding the choice of a MLS system for a specific application case. So far, the research efforts comparing the efficacy ranges of the existing MLS systems have been little reported. To fill this gap, this study examined the performance of three typical MLS systems (Riegl VMX-250, Roamer and Sensei) in terms of target representation. Retrievals of window areas and lighting pole radiuses served as representative cases, as these parameters correspond to the spatial scales from meter to centimeter. The evaluations showed that the VMX-250 with highest sampling density did best, and thus, it was preferred in the scenario of this study. If both the cost and efficacy were regarded, Roamer was a choice of compromise. Therefore, an application-oriented scheme was suggested for selecting MLS systems to acquire the desired performance. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Frequent Unscheduled Updates of the National Base Map Using the Land-Based Mobile Mapping System
Remote Sens. 2013, 5(5), 2513-2533; doi:10.3390/rs5052513
Received: 29 March 2013 / Revised: 10 May 2013 / Accepted: 13 May 2013 / Published: 17 May 2013
Cited by 3 | PDF Full-text (4488 KB) | HTML Full-text | XML Full-text
Abstract
This paper focuses on the use of the Land-based Mobile Mapping System (LMMS) for the unscheduled updates of a National Base Map, which has nationwide coverage and was made using aerial photogrammetry. The objectives of this research are to improve the weak points
[...] Read more.
This paper focuses on the use of the Land-based Mobile Mapping System (LMMS) for the unscheduled updates of a National Base Map, which has nationwide coverage and was made using aerial photogrammetry. The objectives of this research are to improve the weak points of LMMS surveying for its application to the updates of a National Base Map (NBM), which has rigorous accuracy and quality standards. For this, methods were suggested for the (1) improvement of the accuracy of the Global Positioning System/Inertial Navigation System (GPS/INS) in the long-term exposure of environments with poor GPS reception; (2) elimination of mutual deviations between LMMS data obtained in duplicate to meet resolution standards; (3) devising an effective way of mapping objects using LMMS data; and (4) analysis of updatable regions and map layers via LMMS. To verify the suggested methods, experiments and analyses were conducted using two LMMS devices in four target areas for unscheduled updates of the National Base Map. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Improved Sampling for Terrestrial and Mobile Laser Scanner Point Cloud Data
Remote Sens. 2013, 5(4), 1754-1773; doi:10.3390/rs5041754
Received: 23 January 2013 / Revised: 4 March 2013 / Accepted: 27 March 2013 / Published: 9 April 2013
Cited by 14 | PDF Full-text (1305 KB) | HTML Full-text | XML Full-text
Abstract
We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant
[...] Read more.
We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud. We test the methods in three case studies in which data were collected using a different terrestrial or mobile laser scanning system in each. Two reference methods, uniform sampling and linear point picking, were used for result comparison. The results demonstrate that correctly selected distance-sensitive sampling techniques allow higher point removal than the references in all the tested case studies. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
Open AccessArticle Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation
Remote Sens. 2013, 5(4), 1624-1650; doi:10.3390/rs5041624
Received: 12 January 2013 / Revised: 6 March 2013 / Accepted: 13 March 2013 / Published: 28 March 2013
Cited by 34 | PDF Full-text (3302 KB) | HTML Full-text | XML Full-text
Abstract
Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A
[...] Read more.
Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Open AccessArticle A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data
Remote Sens. 2013, 5(2), 584-611; doi:10.3390/rs5020584
Received: 3 December 2012 / Revised: 22 January 2013 / Accepted: 22 January 2013 / Published: 28 January 2013
Cited by 33 | PDF Full-text (1140 KB) | HTML Full-text | XML Full-text
Abstract
As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning,
[...] Read more.
As an important component of urban vegetation, street trees play an important role in maintenance of environmental quality, aesthetic beauty of urban landscape, and social service for inhabitants. Acquiring accurate and up-to-date inventory information for street trees is required for urban horticultural planning, and municipal urban forest management. This paper presents a new Voxel-based Marked Neighborhood Searching (VMNS) method for efficiently identifying street trees and deriving their morphological parameters from Mobile Laser Scanning (MLS) point cloud data. The VMNS method consists of six technical components: voxelization, calculating values of voxels, searching and marking neighborhoods, extracting potential trees, deriving morphological parameters, and eliminating pole-like objects other than trees. The method is validated and evaluated through two case studies. The evaluation results show that the completeness and correctness of our method for street tree detection are over 98%. The derived morphological parameters, including tree height, crown diameter, diameter at breast height (DBH), and crown base height (CBH), are in a good agreement with the field measurements. Our method provides an effective tool for extracting various morphological parameters for individual street trees from MLS point cloud data. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
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Review

Jump to: Research

Open AccessReview Synthesis of Transportation Applications of Mobile LIDAR
Remote Sens. 2013, 5(9), 4652-4692; doi:10.3390/rs5094652
Received: 3 June 2013 / Revised: 24 August 2013 / Accepted: 9 September 2013 / Published: 18 September 2013
Cited by 30 | PDF Full-text (6243 KB) | HTML Full-text | XML Full-text
Abstract
A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of
[...] Read more.
A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on “what” is being done in practice, but not a lot on “how” and “how well” it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR. Full article
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)

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