Automated Building Footprint Extraction From High Resolution Lidar Dem Imagery

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Automated Building Footprint Extraction From High Resolution Lidar Dem Imagery

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Author : Mandar M. Gadre
language : en
Publisher:
Release Date : 2005

Automated Building Footprint Extraction From High Resolution Lidar Dem Imagery written by Mandar M. Gadre and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Buildings categories.

Geographic Information Systems (GIS) are used in the fields of urban planning, environmental management, agriculture, transportation, utilities etc. because of their ability to provide geospatial information organized in multiple layers such as digital image basemap, land use zoning, political boundaries, parcel maps, land cover, road network, building footprints, utility networks (e.g. water, sewage and electricity), topography, and green space. Some urban features like roads and buildings change with the time and it is therefore necessary to update this information. The goal of this research is to provide a robust automated method to extract commercial buildings from the high resolution DEM data with high quality, accuracy, and detection rates. This processing strategy uses three different detectors which are fused to obtain a final output. Though multi-detector fusion has been used previously for satellite imagery, it is completely new for the DEM data. All three algorithms are developed using a fuzzy logic approach. The results of our algorithm show that we have obtained 82% correctness, 73% completeness and 65% quality pixel wise and 82% correctness, 97% completeness and 65% quality object wise for the tuning images and similar results for the test images. This approach can be expanded for the extraction of residential buildings which is left for future work.

Building Extraction From High Resolution Satellite Imagery And Lidar Data

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Author :
language : en
Publisher:
Release Date : 2004

Building Extraction From High Resolution Satellite Imagery And Lidar Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.

Remote Sensing Based Building Extraction

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Author : Mohammad Awrangjeb
language : en
Publisher: MDPI
Release Date : 2020-03-27

Remote Sensing Based Building Extraction written by Mohammad Awrangjeb and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-27 with Science categories.

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D

Po K Rik Jknorse

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Author : Yovhannês T'owmanean
language : en
Publisher:
Release Date : 1993

Po K Rik Jknorse written by Yovhannês T'owmanean and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with categories.

Point Based And Object Based Building Extractions In Urban Area Applying Airborne Lidar Data

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Author : Lixian Dai
language : en
Publisher:
Release Date : 2012

Point Based And Object Based Building Extractions In Urban Area Applying Airborne Lidar Data written by Lixian Dai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.

In this research, we proposed a novel point-based statistical approach for automatic building segmentation and extraction by analyzing the differences between two LiDAR returns, the change of variance, and density from LiDAR point clouds. Then we applied an object-based supervised classification algorithms namely support vector machine (SVM) with several LiDAR-derived features, such as height texture (NDSM, DEM), and contrast texture from co-occurrence matrix and intensity (amplitude of LiDAR response) to extract the building areas in comparison of the result of the statistical methods. Since the terrain is highly uneven and the normalized DSM was a crucial factor in both methods, we filtered the ground points using a new filtering method, which is a combination of the slope-based algorithm (Vosselman 2003) and statistical analysis of last-return of LiDAR data in order to establish the DEM. Furthermore, the accuracy assessment was tested using a four band high-resolution (one foot) digital aerial ortho-imagery. The results show that LiDAR data could be used as a very reliable and stable data source for building extraction in urban areas.

First International Conference On Artificial Intelligence And Cognitive Computing

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Author : Raju Surampudi Bapi
language : en
Publisher: Springer
Release Date : 2018-11-04

First International Conference On Artificial Intelligence And Cognitive Computing written by Raju Surampudi Bapi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-04 with Technology & Engineering categories.

This book presents original research works by researchers, engineers and practitioners in the field of artificial intelligence and cognitive computing. The book is divided into two parts, the first of which focuses on artificial intelligence (AI), knowledge representation, planning, learning, scheduling, perception-reactive AI systems, evolutionary computing and other topics related to intelligent systems and computational intelligence. In turn, the second part focuses on cognitive computing, cognitive science and cognitive informatics. It also discusses applications of cognitive computing in medical informatics, structural health monitoring, computational intelligence, intelligent control systems, bio-informatics, smart manufacturing, smart grids, image/video processing, video analytics, medical image and signal processing, and knowledge engineering, as well as related applications.

Spatial Variability In Environmental Science

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Author : John P. Tiefenbacher
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-10-21

Spatial Variability In Environmental Science written by John P. Tiefenbacher and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-21 with Science categories.

Spatial Variability in Environmental Science - Patterns, Processes, and Analyses includes eight studies that examine the issue of spatial variability in four areas of the environmental sciences – atmospheric science, geological science, biological science, and landscape science. The topics range from monitoring of wind, the urban heat island, and atmospheric pollution, to coastal geomorphology, landscape planning and forest ecology, the problem of introduced species to regional ecologies, and a technique to improve the identification of human constructions in semi-natural landscapes. A small volume can only offer a small glimpse at the activities of scientists and insights into environmental science, but the array of papers herein offers a unique view of the current scholarship.

Automatic Building Footprint Generation From Airborne Lidar Point Cloud

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Author : Xiao Li
language : en
Publisher:
Release Date : 2020

Automatic Building Footprint Generation From Airborne Lidar Point Cloud written by Xiao Li and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Architectural models categories.

Automatically generating building footprint from remote sensing data is an active research topic because of the widespread usage of building footprint in numerous applications. The invention of airborne LiDAR technology has made it possible to measure the ground objects in a large-scale area with a dense and accurate three-dimensional (3-D) point cloud, and therefore provide a new and promising data source for extracting building footprint. However, due to the fact that the LiDAR point cloud is a set of unordered 3-D point coordinates with tremendous size, many traditional remote sensing algorithms that are designed for processing raster and image data cannot be directly applied on LiDAR point cloud. This research presents an efficient and automated workflow to generate building footprint from pre-classified LiDAR data. In this workflow, the pre-classified LiDAR points that belong to the building category are first segmented into multiple clusters through applying an efficient grid-based segmentation algorithm. Each cluster contains the points of an individual building. Then the recursive convex hull algorithm is designed and applied on each cluster to efficiently generate the initial outline for each building. The LiDAR points are irregularly distributed, which causes the generated vii initial building outline to contained irregular zig-zag shape. The initial building outline needs to be regularized in order to deliver the final building footprint with acceptable linear or curvilinear boundaries. To achieve this, a signal-based regularization algorithm that can analyze the wholistic geometric structure of building outline through a 1-D signal is introduced. The signalbased regularization uses Gaussian Smoothing and unsupervised data clustering as the main techniques to regularize the initial building outline. In order to improve it, the more advanced signal processing technique named Cauchy Norm Decomposition is also proposed for more effective regularization. Furthermore, for the purpose of generating final building footprint for the building that may have curvilinear boundary, a robust regularization algorithm that is able to reconstruct both straight-line and curvilinear boundaries is developed by denoising the cumulative signal transformed from initial building outline. The performance of grid-based segmentation and recursive hull algorithm are evaluated qualitatively using the datasets collected at both Santa Rosa, CA and Toronto Downtown. The performance of all the regularization algorithm is evaluated qualitatively and quantitatively using the same datasets.

Automated Building Information Extraction And Evaluation From High Resolution Remotely Sensed Data

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Author : Chuiqing Zeng
language : en
Publisher:
Release Date : 2014

Automated Building Information Extraction And Evaluation From High Resolution Remotely Sensed Data written by Chuiqing Zeng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

The two-dimensional (2D) footprints and three-dimensional (3D) structures of buildings are of great importance to city planning, natural disaster management, and virtual environmental simulation. As traditional manual methodologies for collecting 2D and 3D building information are often both time consuming and costly, automated methods are required for efficient large area mapping. It is challenging to extract building information from remotely sensed data, considering the complex nature of urban environments and their associated intricate building structures. Most 2D evaluation methods are focused on classification accuracy, while other dimensions of extraction accuracy are ignored. To assess 2D building extraction methods, a multi-criteria evaluation system has been designed. The proposed system consists of matched rate, shape similarity, and positional accuracy. Experimentation with four methods demonstrates that the proposed multi-criteria system is more comprehensive and effective, in comparison with traditional accuracy assessment metrics. Building height is critical for building 3D structure extraction. As data sources for height estimation, digital surface models (DSMs) that are derived from stereo images using existing software typically provide low accuracy results in terms of rooftop elevations. Therefore, a new image matching method is proposed by adding building footprint maps as constraints. Validation demonstrates that the proposed matching method can estimate building rooftop elevation with one third of the error encountered when using current commercial software. With an ideal input DSM, building height can be estimated by the elevation contrast inside and outside a building footprint. However, occlusions and shadows cause indistinct building edges in the DSMs generated from stereo images. Therefore, a building-ground elevation difference model (EDM) has been designed, which describes the trend of the elevation difference between a building and its neighbours, in order to find elevation values at bare ground. Experiments using this novel approach report that estimated building height with 1.5m residual, which out-performs conventional filtering methods. Finally, 3D buildings are digitally reconstructed and evaluated. Current 3D evaluation methods did not present the difference between 2D and 3D evaluation methods well; traditionally, wall accuracy is ignored. To address these problems, this thesis designs an evaluation system with three components: volume, surface, and point. As such, the resultant multi-criteria system provides an improved evaluation method for building reconstruction.

High Resolution Object Based Building Extraction Using Pca Of Lidar Ndsm And Aerial Photos

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Author : Alfred Cal
language : en
Publisher:
Release Date : 2019

High Resolution Object Based Building Extraction Using Pca Of Lidar Ndsm And Aerial Photos written by Alfred Cal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Science categories.

Accurate and precise building extraction has become an essential requirement for various applications such as for impact analysis of flooding. This chapter seeks to improve the current and past methods of building extraction by using the principal components analysis (PCA) of LiDAR height (nDSM) and aerial photos (in four RGB and NIR bands) in an object-based image classification (OBIA). This approach uses a combination of aerial photos at 0.1-m spatial resolution and LiDAR nDSM at 1-m spatial resolution for precise and high-resolution building extraction. Because aerial photos provide four bands in the PCA process, this potentially means that the resolution of the image is maintained and therefore building outlines can be extracted at a high resolution of 0.1 m. A total of five experiments was conducted using a combination of different LiDAR derivatives and aerial photos bands in a PCA. The PCA of LiDAR nDSM and RGB and NIR bands combination has proved to produce the best result. The results show a completeness of 87.644%, and a correctness of 93.220% of building extraction. This chapter provides an improvement on the drawbacks of building extraction such as the extraction of small buildings and the smoothing with a well-defined building outline.