Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data

Development of a novel data acquisition and processing methodology applied to the boresight alignment of marine mobile LiDAR systems

Le système LiDAR mobile (SLM) est une technologie d'acquisition de données de pointe qui permet de cartographier les scènes du monde réel en nuages de points 3D. Les applications du SLM sont très vastes, de la foresterie à la modélisation 3D des villes, en passant par l'évaluation de l'inventaire routier et la cartographie des infrastructures portuaires. Le SLM peut également être monté sur diverses plateformes, telles que des plateformes aériennes, terrestres, marines, etc. Indépendamment de l'application et de la plateforme, pour s'assurer que le SLM atteigne sa performance optimale et sa meilleure précision, il est essentiel de traiter correctement les erreurs systématiques du système, spécialement l'erreur des angles de visée à laquelle on s'intéresse particulièrement dans cette thèse. L'erreur des angles de visée est définie comme le désalignement rotationnel des deux parties principales du SLM, le système de positionnement et d'orientation et le scanneur LiDAR, introduit par trois angles de visée. En fait, de petites variations angulaires dans ces paramètres peuvent causer des problèmes importants d'incertitude géométrique dans le nuage de points final et il est vital d'employer une méthode d'alignement pour faire face à la problématique de l'erreur des angles de visée de ces systèmes. La plupart des méthodes existantes d'alignement des angles de visée qui ont été principalement développées pour les SLM aériens et terrestres, tirent profit d'éléments in-situ spécifiques et présents sur les sites de levés et adéquats pour ces méthodes. Par exemple, les éléments linéaires et planaires extraits des toits et des façades des maisons. Cependant, dans les environnements sans présence de ces éléments saillants comme la forêt, les zones rurales, les ports, où l'accès aux éléments appropriées pour l'alignement des angles de visée est presque impossible, les méthodes existantes fonctionnent mal, voire même pas du tout. Par conséquent, cette recherche porte sur l'alignement des angles de visée d'un SLM dans un environnement complexe. Nous souhaitons donc introduire une procédure d'acquisition et traitement pour une préparation adéquate des données, qui servira à la méthode d'alignement des angles de visée du SLM. Tout d'abord, nous explorons les différentes possibilités des éléments utilisés dans les méthodes existantes qui peuvent aider à l'identification de l'élément offrant le meilleur potentiel pour l'estimation des angles de visée d'un SLM. Ensuite, nous analysons, parmi un grand nombre de possibles configurations d'éléments (cibles) et patrons de lignes de balayage, celle qui nous apparaît la meilleure. Cette analyse est réalisée dans un environnement de simulation dans le but de générer différentes configurations de cibles et de lignes de balayage pour l'estimation des erreurs des angles de visée afin d'isoler la meilleure configuration possible. Enfin, nous validons la configuration proposée dans un scénario réel, soit l'étude de cas du port de Montréal. Le résultat de la validation révèle que la configuration proposée pour l'acquisition et le traitement des données mène à une méthode rigoureuse d'alignement des angles de visée qui est en même temps précise, robuste et répétable. Pour évaluer les résultats obtenus, nous avons également mis en œuvre une méthode d'évaluation de la précision relative, qui démontre l'amélioration de la précision du nuage de points après l'application de la procédure d'alignement des angles de visée.A Mobile LiDAR system (MLS) is a state-of-the-art data acquisition technology that maps real-world scenes in the form of 3D point clouds. The MLS's list of applications is vast, from forestry to 3D city modeling and road inventory assessment to port infrastructure mapping. The MLS can also be mounted on various platforms, such as aerial, terrestrial, marine, and so on. Regardless of the application and the platform, to ensure that the MLS achieves its optimal performance and best accuracy, it is essential to adequately address the systematic errors of the system, especially the boresight error. The boresight error is the rotational misalignment offset of the two main parts of the MLS, the positioning and orientation system (POS) and the LiDAR scanner. Minor angular parameter variations can cause important geometric accuracy issues in the final point cloud. Therefore, it is vital to employ an alignment method to cope with the boresight error problem of such systems. Most of the existing boresight alignment methods, which have been mainly developed for aerial and terrestrial MLS, take advantage of the in-situ tie-features in the environment that are adequate for these methods. For example, tie-line and tie-plane are extracted from building roofs and facades. However, in low-feature environments like forests, rural areas, ports, and harbors, where access to suitable tie-features for boresight alignment is nearly impossible, the existing methods malfunction or do not function. Therefore, this research addresses the boresight alignment of a marine MLS in a low-feature maritime environment. Thus, we aim to introduce an acquisition procedure for suitable data preparation, which will serve as input for the boresight alignment method of a marine MLS. First, we explore various tie-features introduced in the existing ways that eventually assist in the identification of the suitable tie-feature for the boresight alignment of a marine MLS. Second, we study the best configuration for the data acquisition procedure, i.e., tie-feature(s) characteristics and the necessary scanning line pattern. This study is done in a simulation environment to achieve the best visibility of the boresight errors on the selected suitable tie-feature. Finally, we validate the proposed configuration in a real-world scenario, which is the port of Montreal case study. The validation result reveals that the proposed data acquisition and processing configuration results in an accurate, robust, and repeatable rigorous boresight alignment method. We have also implemented a relative accuracy assessment to evaluate the obtained results, demonstrating an accuracy improvement of the point cloud after the boresight alignment procedure

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints

LIDAR based semi-automatic pattern recognition within an archaeological landscape

LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Überwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen Gelände oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archäologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwändige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergänzen, können computergestützte Verfahren einige neue Lösungsansätze bieten, die darüber hinaus sogar die Identifizierung von für das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archäologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-Geländemodelle mit archäologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archäologischer Muster zu Bodendenkmalen als digitale „LIDAR-Landschaft“ bewertet. Dabei wird auf möglichst einfache und freie verfügbare algorithmische Ansätze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur großflächigen Erkennung archäologischer Denkmäler zurückgegriffen. Die Dissertation gibt dabei einen umfassenden Überblick über die archäologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer Ansätze den Entwicklungsstand der semiautomatisierten Erkennung archäologischer Strukturen im Rahmen archäologischer Prospektion und Fernerkundungen. Darüber hinaus erläutert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die Qualität der Erkennung von Bodendenkmälern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld für weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren großflächige Untersuchungen zum kulturellen Erbe ermöglicht werden. Resümierend vergleicht sie die analoge und computergestützte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschließend das weitere Potential LIDAR-basierter Mustererkennung in archäologischen Kulturlandschaften

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Strip Adjustment of Airborne LiDAR Data in Urban Scenes Using Planar Features by the Minimum Hausdorff Distance

In Airborne Light Detection and Ranging (LiDAR) data acquisition practice, discrepancies exist between adjacent strips even though careful system calibrations have been performed. A strip adjustment method using planar features acquired by the Minimum Hausdorff Distance (MHD) is proposed to eliminate these discrepancies. First, semi-suppressed fuzzy C-means and restricted region growing algorithms are used to extract buildings. Second, a binary image is generated from the minimum bounding rectangle that covers overlapping regions. Then, connected components labeling algorithm is applied to process the binary image to extract individual buildings. After that, building matching is performed based on MHD. Third, a coarse-to-fine approach is used to segment building roof planes. Then, plane matching is conducted under the constraints of MHD and normal vectors similarity. The last step is the calculation of the parameters based on Euclidean distance minimization between matched planes. Two different types of datasets, one of which was acquired by a dual-channel LiDAR system Trimble AX80, were selected to verify the proposed method. Experimental results show that the corresponding planar features that meet adjustment requirements can be successfully detected without any manual operations or auxiliary data or transformation of raw data, while the discrepancies between strips can be effectively eliminated. Although adjustment results of the proposed method slightly outperform the comparison alternative, the proposed method also has the advantage of processing the adjustment in a more automatic manner than the comparison method

8th. International congress on archaeology computer graphica. Cultural heritage and innovation

El lema del Congreso es: 'Documentación 3D avanzada, modelado y reconstrucción de objetos patrimoniales, monumentos y sitios.Invitamos a investigadores, profesores, arqueólogos, arquitectos, ingenieros, historiadores de arte... que se ocupan del patrimonio cultural desde la arqueología, la informática gráfica y la geomática, a compartir conocimientos y experiencias en el campo de la Arqueología Virtual. La participación de investigadores y empresas de prestigio será muy apreciada. Se ha preparado un atractivo e interesante programa para participantes y visitantes.Lerma García, JL. (2016). 8th. International congress on archaeology computer graphica. Cultural heritage and innovation. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/73708EDITORIA