Comparative analysis of classification techniques for building block extraction using aerial imagery and LiDAR data

Bratsolis, E.; Gyftakis, S.; Charou, Eleni; Vassilas, Nikolaos

doi:10.1109/isspit.2013.6781858

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…According to our previous investigation [23,26] automatic segmentation into buildings-nonbuildings could be efficiently performed using a MFNN (multilayer feedforward neural network) having as input the result of the Mean-Shift-based preprocessing step. The input layer consists of four nodes: 3 nodes for the 3 color channels (red, green, blue) and 1 node for the thresholded elevation values (i.e.…”

Section: Neural Network Based Classificationmentioning

confidence: 99%

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

Bratsolis¹,

Charou²,

Tsenoglou³

et al. 2016

JEASE

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This paper examines the utility of high-resolution airborne RGB orthophotos and LiDAR data for mapping residential land uses within the spatial limits of suburb of Athens, Greece. Modern remote sensors deliver ample information from the AOI (area of interest) for the estimation of 2D indicators or with the inclusion of elevation data 3D indicators for the classification of urban land. In this research, two of these indicators, BCR (building coverage ratio) and FAR (floor area ratio) are automatically evaluated. In the pre-processing step, the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an nDSM (normalized digital surface model) comprised of upsampled elevation data with considerable improvement regarding region filling and "straightness" of elevation discontinuities. Following this step, a MFNN (multilayer feedforward neural network) is used to classify all pixels of the AOI into building or non-building categories. The information derived from the BCR and FAR building indicators, adapted to landscape characteristics of the test area is used to propose two new indices and an automatic post-classification based on the density of buildings.

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Section: Neural Network Based Classificationmentioning

confidence: 99%

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

Bratsolis¹,

Charou²,

Tsenoglou³

et al. 2016

JEASE

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“…Step 4 Automatic classification in Building-Non Buildings According to our previous investigation (Bratsolis et al, 2013, Gyftakis et al, 2014) automatic segmentation in Buildings-Non Buildings could be efficiently performed when using Multilayer Feedforward Neural Network (MFNN). The input layer of the MFNN consists of four nodes: 3 nodes for the 3 channels (Red, Green, and Blue) of the orthophoto and one node for values of the Mean-Shifted and thresholded nDSM.…”

Section: Step2 Mean Shift Improvement Of Ndsmmentioning

confidence: 99%

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

Charou

Gyftakis

Bratsolis

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and "straightness" of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.

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“…Some mapping and modeling tasks require airborne scanning where Unmanned Aerial Vehicle (UAV) is widely employed regarding easy operation and cheap cost compared to conventional airplanes, although the regulations of UAV flight and mapping specifications are still a research topic [5,6] and may vary among different applications. Nevertheless, whichever LiDAR data measurement tool is employed, one of the first steps of data processing is data classification [7,8,9], Manuscript received September 5, 2023; revised September ------; accepted September --------. Date of publication -------; date of current version ------.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Modeling and Visualization of Single Tree LiDAR Point Cloud Using Matrixial Form

Tarsha Kurdi,

Lewandowicz,

Shan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Tree modeling and visualization still represent a challenge in the Light Detecting And Ranging (LiDAR) area. Starting from the segmented tree point clouds, this paper presents an innovative tree modeling and visualization approach. The algorithm simulates the tree point cloud by a rotating surface. Three matrixes, X, Y, and Z are calculated by considering the middle of the projected tree point cloud on the horizontal plane. This mathematical form not only allows tree modeling and visualization, but also permits the calculation of geometric characteristics and parameters of the tree. The superimposition of the tree point cloud over the constructed model confirms its high accuracy where all the points of the tree cloud are within the constructed model. The tests with multiple single trees demonstrate an overall average fit between 0.3 and 0.89 m. The built tree models are also compliant with the Open Geospatial Consortium (OCG) City GML standards at the level of a physical model. This approach opens a door to numerous applications for visualization, computation, and study of forestry and vegetation in urban as well as rural areas.

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Comparative analysis of classification techniques for building block extraction using aerial imagery and LiDAR data

Cited by 4 publications

References 13 publications

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

Automated Building Block Extraction and Building Density Classification Using Aerial Imagery and LiDAR Data

Urban Density Indices Using Mean Shift-Based Upsampled Elevetion Data

Three-Dimensional Modeling and Visualization of Single Tree LiDAR Point Cloud Using Matrixial Form

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