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Three dimensional (3D) information about ground and above-ground features such as buildings and trees is important for many urban and environmental applications. Recent developments in Light Detection And Ranging (LiDAR) technology provide promising alternatives to conventional techniques for acquiring such information. The focus of this dissertation research is to effectively and efficiently filter massive airborne LiDAR point cloud data and to extract main above-ground features such as buildings and trees in the urban area. A novel segmentation algorithm for point cloud data, namely the 3D k mutual nearest neighborhood ( k MNN) segmentation algorithm, was developed based on the improvement to the k MNN clustering algorithm by employing distances in 3D space to define mutual nearest neighborhoods. A set of optimization strategies, including dividing dataset into multiple blocks and small size grids, and using distance thresholds in x and y, were implemented to improve the efficiency of the segmentation algorithm. A segmentation based filtering method was then employed to filter the generated segments, which first generates segment boundaries using Voronoi polygon and dissolving operations, and then labels the segments as ground and above-ground based on their size and relative heights to the surrounding segments. An object-based feature extraction approach was also devised to extract buildings and trees from the above-ground segments based on object-level statistics derived, which were subject to a rule based classification system developed by either human experts or an inductive machine-learning algorithm. Case studies were conducted with four different LiDAR datasets to evaluate the effectiveness and efficiency of the proposed approaches. The proposed segmentation algorithm proved to be not only effective in separating ground and above-ground measurements into different segments, but also efficient in processing large datasets. The segmentation based filtering and object based feature extraction approaches have also demonstrated effectiveness in labeling the segments into ground and above-ground and in extracting buildings and trees from the above-ground segments. When incorporating spectral information from remote sensing imagery with the LiDAR data, the accuracy for feature extraction was further increased.
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.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.
Image registration is a major field within computer vision and is often a required step in properly fulfilling other computer vision and pattern recognition tasks such as change detection, scene classification and image segmentation. Recent advances in 3D computer vision and lowered costs in Light Detection and Ranging devices, better known as LiDAR, have given way to an increase in readily available 3D image datasets. These 3D captures give an extra dimension to computer vision data and allow for improvements in a multitude of tasks when compared to their 2D counterparts. However, due to the large scale and complex nature of 3D point cloud data, classical methods for registration often require increased hardware usage and time and can fail to proper register data with a low degree of error. The strategy presented in this paper aims to reduce the number of points representing a point cloud in order to reduce time and hardware overhead needed to perform registration while allowing the algorithm to improve registration accuracy and reduce error between registered clouds. This is done by extracting key edge features from the point clouds using eigenvector analysis to remove ground planes and large normal planes within the point cloud. The algorithm is further improved by performing set differencing on two separate edge extractions to remove large clusters of points representing natural objects that can often cause confusion for registration of outdoor LiDAR scenes. The method for key point registration is evaluated on large scale, complex LiDAR point clouds obtained from aerial sensors. Tests are performed on both fully overlapping and partially overlapping clouds to ensure that the method increases performance on full and partial registration tasks. The tests are also performed on clouds of varying resolution to test the algorithms ability to maintain integrity regardless of cloud resolution. Point reduction results, registration statics and visual results are presented for comparison. A brief look into possible applications of the method and future improvements to the algorithm are included.
Written by a team of international experts, this book provides a comprehensive overview of the major applications of airborne and terrestrial laser scanning. It focuses on principles and methods and presents an integrated treatment of airborne and terrestrial laser scanning technology. After consideration of the technology and processing methods, the book turns to applications, such as engineering, forestry, cultural heritage, extraction of 3D building models, and mobile mapping. This book brings together the various facets of the subject in a coherent text that will be relevant for advanced students, academics and practitioners.
Efficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert system and IoT applications. It provides novel survey and case study report on recent industrial approach towards Smart City development.
Light Detection and Ranging, (LiDAR) presents a series of unique challenges, the foremost of these being object identification. Because of the ease of aerial collection and high range resolution, analysts are often faced with the challenge of sorting through large datasets and making informed decisions across multiple square miles of data. This problem has made automatic target detection in LiDAR a priority. A novel algorithm is proposed with the overall goal of automatic identification of five object classes within aerially collected LiDAR data: ground, buildings, vehicles, vegetation and power lines. The objective is divided into two parts: region segmentation and object classification. The segmentation portion of the algorithm uses a progressive morphological filter to separate the ground points from the object points. Next, the object points are examined and a Normal Octree Region Merging (NORM) segmentation takes place. This segmentation technique, based on surface normal similarities, subdivides the object points into clusters. Next, for each cluster of object points, a Shape-based Eigen Local Feature (SELF) is computed. Finally, the features are used as the input to a cascade of classifiers, where four individual support vector machines (SVM) are trained to distinguish the object points into the remaining four classes.The ability of the algorithm to segment points into complete objects and also classify each point into its correct class is evaluated. Both the segmentation and classification results are compared to datasets which have been manually ground-truthed. The evaluation demonstrates the success of the proposed algorithm in segmenting and distinguishing between five classes of objects in a LiDAR point cloud. Future work in this direction includes developing a method to identify the volume changes in a scene over time in an effort to provide further contextual information about a given area.
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world