scholarly journals Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data

2016 ◽  
Vol XLI-B3 ◽  
pp. 173-180 ◽  
Author(s):  
A. Bellakaout ◽  
M. Cherkaoui ◽  
M. Ettarid ◽  
A. Touzani
Keyword(s):  
Point Cloud ◽  
Geographical Information ◽  
Gis Mapping ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Second Phase ◽  
Variation Analysis ◽  
Height Variation ◽  
Topological Relationship

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. <br><br> This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. <br><br> This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.

scholarly journals Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data

2016 ◽  
Vol XLI-B3 ◽  
pp. 173-180
Author(s):  
A. Bellakaout ◽  
M. Cherkaoui ◽  
M. Ettarid ◽  
A. Touzani
Keyword(s):  
Point Cloud ◽  
Geographical Information ◽  
Gis Mapping ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Second Phase ◽  
Variation Analysis ◽  
Height Variation ◽  
Topological Relationship

Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is greatly required to handle the large amount of data collected because the manual process is time consuming and very expensive. <br><br> This paper is presenting an approach for automatic classification of aerial Lidar data into five groups of items: buildings, trees, roads, linear object and soil using single return Lidar and processing the point cloud without generating DEM. Topological relationship and height variation analysis is adopted to segment, preliminary, the entire point cloud preliminarily into upper and lower contours, uniform and non-uniform surface, non-uniform surfaces, linear objects, and others. <br><br> This primary classification is used on the one hand to know the upper and lower part of each building in an urban scene, needed to model buildings façades; and on the other hand to extract point cloud of uniform surfaces which contain roofs, roads and ground used in the second phase of classification. A second algorithm is developed to segment the uniform surface into buildings roofs, roads and ground, the second phase of classification based on the topological relationship and height variation analysis, The proposed approach has been tested using two areas : the first is a housing complex and the second is a primary school. The proposed approach led to successful classification results of buildings, vegetation and road classes.

Cartography and GIS

2019 ◽  
Author(s):  
Вячеслав Раклов ◽  
Vyacheslav Raklov
Keyword(s):  
Real Estate ◽  
Land Management ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Gis Mapping ◽  
History Of ◽  
Gis Environment ◽  
Composition Structure ◽  
Classification Of Maps

The textbook considers the basic concepts of cartography, the history of its development, as well as the classification of maps and the main elements of the map, the issues of mathematical cartography, the main stages of creating maps, the factors, types and methods of cartographic generalization. Separate sections of the manual are devoted to cartographic signs and methods of image on maps of thematic content, the development of cartographic scales and methods of use of maps in land management and cadastre. Separately, the issues of the functioning of geographical information systems (GIS): their composition, structure, technology for creating thematic maps in the GIS environment. The manual concludes with a section on GIS mapping for real estate cadastre, environmental protection and land monitoring, as well as recommendations on the choice of GIS and requirements for cartographic documentation of real estate cadastre. Recommended for students studying in the field of "land Management", "Land cadastre", "Urban cadastre".

scholarly journals CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

2016 ◽  
Vol XLI-B3 ◽  
pp. 441-445 ◽  
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Data Set ◽  
Cloud Data ◽  
Rule Sets

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

scholarly journals Spectral Pattern Classification in Lidar Data for Rock Identification in Outcrops

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Leonardo Campos Inocencio ◽  
Mauricio Roberto Veronez ◽  
Francisco Manoel Wohnrath Tognoli ◽  
Marcelo Kehl de Souza ◽  
Reginaldo Macedônio da Silva ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Lidar Data ◽  
Spectral Signatures ◽  
Physical Chemical ◽  
Compositional Changes ◽  
Number Of Classes ◽  
Digital Outcrop

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

scholarly journals Classification Airbrone LiDAR Point cloud Data

2021 ◽  
Vol 7 (01) ◽  
pp. 36-39
Author(s):  
Naga Madhavi lavanya Gandi
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Point Cloud ◽  
Near Infrared ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Mid Infrared ◽  
Cloud Data ◽  
Classification Information

Land cover classification information plays a very important role in various applications. Airborne Light detection and Ranging (LiDAR) data is widely used in remote sensing application for the classification of land cover. The present study presents a Spatial classification method using Terrasoild macros . The data used in this study are a LiDAR point cloud data with the wavelength of green:532nm, near infrared:1064nm and mid-infrared-1550nm and High Resolution RGB data. The classification is carried in TERRASCAN Module with twelve land cover classes. The classification accuracies were assessed using high resolution RGB data. From the results it is concluded that the LiDAR data classification with overall accuracy and kappa coefficient 85.2% and 0.7562.

scholarly journals EFFICIENT LARGE-SCALE AIRBORNE LIDAR DATA CLASSIFICATION VIA FULLY CONVOLUTIONAL NETWORK

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 527-532
Author(s):  
E. Maset ◽  
B. Padova ◽  
A. Fusiello
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Ground Vegetation ◽  
Lidar Data ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Two Dimensional Image

Abstract. Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700 km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (ground, vegetation, roof, overground and power line), with an overall accuracy of 92.9%.

scholarly journals Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation

2021 ◽  
Vol 13 (18) ◽  
pp. 3569
Author(s):  
Daniel Tamburlin ◽  
Michele Torresani ◽  
Enrico Tomelleri ◽  
Giustino Tonon ◽  
Duccio Rocchini
Keyword(s):  
Species Diversity ◽  
Tree Species ◽  
Point Cloud ◽  
Forest Ecosystems ◽  
Coniferous Forest ◽  
Lidar Data ◽  
Height Distribution ◽  
Tree Species Diversity ◽  
Height Variation ◽  
Q Index

Forest biodiversity is a key element to support ecosystem functions. Measuring biodiversity is a necessary step to identify critical issues and to choose interventions to be applied in order to protect it. Remote sensing provides consistent quality and standardized data, which can be used to estimate different aspects of biodiversity. The Height Variation Hypothesis (HVH) represents an indirect method for estimating species diversity in forest ecosystems from the LiDAR data, and it assumes that the higher the variation in tree height (height heterogeneity, HH), calculated through the ’Canopy Height Model’ (CHM) metric, the more complex the overall structure of the forest and the higher the tree species diversity. To date, the HVH has been tested exclusively with CHM data, assessing the HH only with a single heterogeneity index (the Rao’s Q index) without making use of any moving windows (MW) approach. In this study, the HVH has been tested in an alpine coniferous forest situated in the municipality of San Genesio/Jenesien (eastern Italian Alps) at 1100 m, characterized by the presence of 11 different tree species (mainly Pinus sylvestris, Larix decidua, Picea abies followed by Betula alba and Corylus avellana). The HH has been estimated through different heterogeneity measures described in the new R rasterdiv package using, besides the CHM, also other LiDAR metrics (as the percentile or the standard deviation of the height distribution) at various spatial resolutions and MWs (ALS LiDAR data with mean point cloud density of 2.9 point/m2). For each combination of parameters, and for all the considered plots, linear regressions between the Shannon’s H′ (used as tree species diversity index based on field data) and the HH have been derived. The results showed that the Rao’s Q index (singularly and through a multidimensional approach) performed generally better than the other heterogeneity indices in the assessment of the HH. The CHM and the LiDAR metrics related to the upper quantile point cloud distribution at fine resolution (2.5 m, 5 m) have shown the most important results for the assessment of the HH. The size of the used MW did not influence the general outcomes but instead, it increased when compared to the results found in the literature, where the HVH was tested without MW approach. The outcomes of this study underline that the HVH, calculated with certain heterogeneity indices and LiDAR data, can be considered a useful tool for assessing tree species diversity in considered forest ecosystems. The general results highlight the strength and importance of LiDAR data in assessing the height heterogeneity and the related biodiversity in forest ecosystems.

scholarly journals CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS

2016 ◽  
Vol XLI-B3 ◽  
pp. 441-445
Author(s):  
N. Yastikli ◽  
Z. Cetin
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Classification Methods ◽  
Data Set ◽  
Cloud Data ◽  
Rule Sets

LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar
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