This research addresses the problem of efficiently and robustly reconstructing semantically-rich 3D architectural models from laser-scanned point-clouds. It first covers the pre-existing literature and industrial developments in active-sensing, 3D reconstruction of the built-environment and procedural modelling. It then documents a number of novel contributions to the classical problems of change-detection between temporally varying multi-modal geometric representations and automatic 3D asset creation from airborne and ground point-clouds of buildings. Finally this thesis outlines on-going research and avenues for continued investigation - most notably fully automatic temporal update and revision management for city-scale CAD models via data-driven procedural modelling from point-clouds. In short this thesis documents the outcomes of a research project whose primary aim was to engineer fast, accurate and sparse building reconstruction algorithms. Formally: this thesis puts forward the hypothesis (and advocates) that architectural reconstruction from actively-sensed point-clouds can be addressed more efficiently and affording greater control (over the geometric results) - via deterministic procedurally-driven analysis and optimisation than via stochastic sampling.

Data about the earth is increasing in value and demand from customers, but itis difficult to produce accurately and cheap. This thesis examines if it is possible to take low resolution and distorted 3D data and increase the accuracy of building geometry by performing building reconstruction. Building reconstruction is performed with a Markov chain Monte Carlo method where building primitives are placed iteratively until a good fit is found. The digital height model and pixel classification used is produced by Vricon. The method is able to correctly place primitive models, but often overestimate their dimensions by about 15%.

This thesis deals with reconstruction of a specific apartment building in Brno-Zidenice. The aim of this thesis is to find out if the investment into reconstruction of the building in this specific area has paid off. To be able to look into this issue, the following method was used: All the reconstruction phases as well as the decision making process are described step by step. After analyzing the reconstruction itself, the cost and revenue valuation is done for both cases - for the building before and after the reconstruction. Having both values, the evaluation of the investment was performed. The results show that the value of the building increased substantially, the investment is profitable and the owner is achieving the expected revenue. Therefore it can be assumed that similar investments into similar building in this area will be profitable.

Viele Hotels in Schierke stehen seit Ende des 19./Anfang des 20. Jahrhunderts leer. Um ein attraktives und besucherreiches Hotel zu entwerfen, besteht die Aufgabe darin, das unbewohnte Hotel „Heinrich Heine“ als Vier-Sterne-Hotel zu entwickeln. Für den Entwurf wurden zwei Analysen durchgeführt. In der ersten Analyse war eine Stadt zu wählen, die eine enge Bedeutung und Bezug zur Stadt Schierke hat, damit die touristische Infrastruktur analysiert werden kann. Das Ziel der zweiten Analyse bestand darin, ein Hotel zu finden, welches sich sehr gut in die bestehende Umgebung (entweder Alpen oder Bergort) einpasst. Außerdem wurde ein Hotel gesucht, welches sich an den Prinzipien der Nachhaltigkeit orientiert. Am Ende wurde der Grundgedanke und das Grundkonzept eines Passivhauses favorisiert. Vor Beginn des Entwurfs fand ein Workshop in Schierke statt, bei dem jede Gruppe Ideen für die Gestaltung eines Ortsentwicklungskonzepts (Masterplan) für die Stadt diskutierte und entwickelte. Aus dieser Idee heraus, ist es wichtig, das Hotelprofil und die Zielgruppe miteinander zu verbinden. Auf Grundlage der Analysen und des Masterplans für die Entwicklung der Stadt wurde ein Entwurf für das Hotel Heinrich Heine erarbeitet. Eckpunkte für die Entwicklung des Projekts waren ebenso die Analyse des Grundstücks, historische Aspekte des Hotels, die Funktionalität, Materialität, Fassade und Aspekte, wie Barrierefreiheit und Nachhaltigkeit.

The aim of this work is to develop research on 3D building modeling. In particular, the research in aerial-based 3D building reconstruction is a topic very developed since 1990. However, it is necessary to pursue the research since the actual approaches for 3D massive building reconstruction (although efficient) still encounter problems in generalization, coherency, accuracy. Besides, the recent developments of street acquisition systems such as Mobile Mapping Systems open new perspectives for improvements in building modeling in the sense that the terrestrial data (very dense and accurate) can be exploited with more performance (in comparison to the aerial investigation) to enrich the building models at facade level (e.g., geometry, texturing).Hence, aerial and terrestrial based building modeling approaches are individually proposed. At aerial level, we describe a direct and featureless approach for simple polyhedral building reconstruction from a set of calibrated aerial images. At terrestrial level, several approaches that essentially describe a 3D urban facade modeling pipeline are proposed, namely, the street point cloud segmentation and classification, the geometric modeling of urban facade and the occlusion-free facade texturing

As more data sources containing 3-D information are becoming available, an increased interest in 3-D imaging has emerged. Among these is the 3-D reconstruction of buildings and other man-made structures. A necessary preprocessing step is the detection and isolation of individual buildings that subsequently can be reconstructed in 3-D using various methodologies. Applications for both building detection and reconstruction have commercial use for urban planning, network planning for mobile communication (cell phone tower placement), spatial analysis of air pollution and noise nuisances, microclimate investigations, geographical information systems, security services and change detection from areas affected by natural disasters. Building detection and reconstruction are also used in the military for automatic target recognition and in entertainment for virtual tourism. Previously proposed building detection and reconstruction algorithms solely utilized aerial imagery. With the advent of Light Detection and Ranging (LiDAR) systems providing elevation data, current algorithms explore using captured LiDAR data as an additional feasible source of information. Additional sources of information can lead to automating techniques (alleviating their need for manual user intervention) as well as increasing their capabilities and accuracy. Several building detection approaches surveyed in the open literature have fundamental weaknesses that hinder their use; such as requiring multiple data sets from different sensors, mandating certain operations to be carried out manually, and limited functionality to only being able to detect certain types of buildings. In this work, a building detection system is proposed and implemented which strives to overcome the limitations seen in existing techniques. The developed framework is flexible in that it can perform building detection from just LiDAR data (first or last return), or just nadir, color aerial imagery. If data from both LiDAR and aerial imagery are available, then the algorithm will use them both for improved accuracy. Additionally, the proposed approach does not employ severely limiting assumptions thus enabling the end user to apply the approach to a wider variety of different building types. The proposed approach is extensively tested using real data sets and it is also compared with other existing techniques. Experimental results are presented.

L'objectif principal de ce travail est le développement de recherches en modélisation 3D du bâti. En particulier, la recherche en reconstruction 3D de bâtiment est un sujet très développé depuis les années 90. Malgré tout, il paraît nécessaire de poursuivre les recherches dans cet axe étant données que les approches actuelles consacrées à la reconstruction 3D de bâtiment (bien qu'efficaces) rencontrent encore des difficultés en terme de généralisation, de cohérence et de précision. Par ailleurs, les récents développements des systèmes d'acquisitions de rues tel que les systèmes de cartographie mobile ouvrent de nouvelles perspectives d'amélioration de la modélisation des bâtiments dans le sens ou les données terrestres (très précises et résolus) peuvent être exploitées avec davantage de cohérence (en comparaison à l'aérien) pour enrichir les modèles de bâtiments au niveau des façades (la géométrie, la texture).Ainsi, des approches de modélisation aériennes et terrestres sont individuellement proposées. Au niveau aérien, nous décrivons une approche directe et dépourvu d'extraction et d'assemblage de primitives géométriques en vue de la reconstruction 3D de modèles polyédriques simples de bâtiments à partir d'un jeu d'images aériennes calibrées. Au niveau terrestre, plusieurs approches qui décrivent essentiellement un pipeline pour la modélisation 3D des façades urbaines sont proposées; à savoir, la segmentation et classification de nuage de rues urbaines, la modélisation géométrique des façades urbaines et le texturage des façades urbaines comportant des occultations causées par d'autres objets du mobilier urbains / The aim of this work is to develop research on 3D building modeling. In particular, the research in aerial-based 3D building reconstruction is a topic very developed since 1990. However, it is necessary to pursue the research since the actual approaches for 3D massive building reconstruction (although efficient) still encounter problems in generalization, coherency, accuracy. Besides, the recent developments of street acquisition systems such as Mobile Mapping Systems open new perspectives for improvements in building modeling in the sense that the terrestrial data (very dense and accurate) can be exploited with more performance (in comparison to the aerial investigation) to enrich the building models at facade level (e.g., geometry, texturing).Hence, aerial and terrestrial based building modeling approaches are individually proposed. At aerial level, we describe a direct and featureless approach for simple polyhedral building reconstruction from a set of calibrated aerial images. At terrestrial level, several approaches that essentially describe a 3D urban facade modeling pipeline are proposed, namely, the street point cloud segmentation and classification, the geometric modeling of urban facade and the occlusion-free facade texturing

Modélisation 3D réaliste des bâtiments et d'autres objets de planification urbaine est un domaine de recherche actif dans le domaine de la modélisation 3D de la ville, la documentation du patrimoine, tourisme virtuel, la planification urbaine, la conception architecturale et les jeux d'ordinateur. La création de ces modèles, très souvent, nécessite la fusion des données provenant de diverses sources telles que les images optiques et de numérisation de nuages ​​de points laser. Pour imiter de façon aussi réaliste que possible les mises en page, les activités et les fonctionnalités d'un environnement du monde réel, ces modèles doivent atteindre de haute qualité et la précision de photo-réaliste en termes de la texture de surface (par exemple pierre ou de brique des murs) et de la morphologie (par exemple, les fenêtres et les portes) des objets réels. Rendu à base d'images est une alternative pour répondre à ces exigences. Il utilise des photos, prises soit au niveau du sol ou de l'air, à ajouter de la texture au modèle 3D ajoutant ainsi photo-réalisme.Pour revêtement de texture pleine de grandes façades des modèles de blocs 3D, des images qui dépeignent la même façade doivent être correctement combinée et correctement aligné avec le côté du bloc. Les photos doivent être fusionnés de manière appropriée afin que le résultat ne présente pas de discontinuités, de brusques variations de l'éclairage ou des lacunes. Parce que ces images ont été prises, en général, dans différentes conditions de visualisation (angles de vision, des facteurs de zoom, etc.) ils sont sous différentes distorsions de perspective, mise à l'échelle, de luminosité, de contraste et de couleur nuances, ils doivent être corrigés ou ajustés. Ce processus nécessite l'extraction de caractéristiques clés de leur contenu visuel d'images.Le but du travail proposé est de développer des méthodes basées sur la vision par ordinateur et les techniques de reconnaissance des formes, afin d'aider ce processus. En particulier, nous proposons une méthode pour extraire les lignes implicites à partir d'images de mauvaise qualité des bâtiments, y compris les vues de nuit où seules quelques fenêtres éclairées sont visibles, afin de préciser des faisceaux de lignes parallèles 3D et leurs points de fuite correspondants. Puis, sur la base de ces informations, on peut parvenir à une meilleure fusion des images et un meilleur alignement des images aux façades de blocs. / Realistic 3D modeling of buildings and other urban planning objects is an active research area in the field of 3D city modeling, heritage documentation, virtual touring, urban planning, architectural design and computer gaming. The creation of such models, very often, requires merging of data from diverse sources such as optical images and laser scan point clouds. To imitate as realistically as possible the layouts, activities and functionalities of a real-world environment, these models need to attain high photo-realistic quality and accuracy in terms of the surface texture (e.g. stone or brick walls) and morphology (e.g. windows and doors) of the actual objects. Image-based rendering is an alternative for meeting these requirements. It uses photos, taken either from ground level or from the air, to add texture to the 3D model thus adding photo-realism. For full texture covering of large facades of 3D block models, images picturing the same façade need to be properly combined and correctly aligned with the side of the block. The pictures need to be merged appropriately so that the result does not present discontinuities, abrupt variations in lighting or gaps. Because these images were taken, in general, under various viewing conditions (viewing angles, zoom factors etc) they are under different perspective distortions, scaling, brightness, contrast and color shadings, they need to be corrected or adjusted. This process requires the extraction of key features from their visual content of images. The aim of the proposed work is to develop methods based on computer vision and pattern recognition techniques in order to assist this process. In particular, we propose a method for extracting implicit lines from poor quality images of buildings, including night views where only some lit windows are visible, in order to specify bundles of 3D parallel lines and their corresponding vanishing points. Then, based on this information, one can achieve better merging of the images and better alignment of the images to the block façades. Another important application dealt in this thesis is that of 3D modeling. We propose an edge preserving interpolation, based on the mean shift algorithm, that operates jointly on the optical and the elevation data. It succeeds in increasing the resolution of the elevation data (LiDAR) while improving the quality (i.e. straightness) of their edges. At the same time, the color homogeneity of the corresponding imagery is also improved. The reduction of color artifacts in the optical data and the improvement in the spatial resolution of elevation data results in more accurate 3D building models. Finally, in the problem of building detection, the application of the proposed mean shift-based edge preserving smoothing for increasing the quality of aerial/color images improves the performance of binary building vs non-building pixel classification.

La reconstruction de bâtiments à partir de photographies aériennes et d’autres données spatiales urbaines multi-sources est une tâche qui utilise une multitude de méthodes automatisées et semi-automatisées allant des processus ponctuels au traitement classique des images et au balayage laser. Dans cette thèse, un système de relaxation itératif est développé sur la base de l'examen du contexte local de chaque bord en fonction de multiples sources d'entrée spatiales (masques optiques, d'élévation, d'ombre et de feuillage ainsi que d'autres données prétraitées, décrites au chapitre 6). Toutes ces données multisource et multirésolution sont fusionnées de manière à extraire les segments de ligne probables ou les arêtes correspondant aux limites des bâtiments. Deux nouveaux sous-systèmes ont également été développés dans cette thèse. Ils ont été conçus dans le but de fournir des informations supplémentaires, plus fiables, sur les contours des bâtiments dans une future version du système de relaxation proposé. La première est une méthode de réseau de neurones à convolution profonde (CNN) pour la détection de frontières de construction. Le réseau est notamment basé sur le modèle SRCNN (Dong C. L., 2015) de super-résolution à la pointe de la technologie. Il accepte des photographies aériennes illustrant des données de zones urbaines densément peuplées ainsi que leurs cartes d'altitude numériques (DEM) correspondantes. La formation utilise trois variantes de cet ensemble de données urbaines et vise à détecter les contours des bâtiments grâce à une nouvelle cartographie hétéroassociative super-résolue. Une autre innovation de cette approche est la conception d'une couche de perte personnalisée modifiée appelée Top-N. Dans cette variante, l'erreur quadratique moyenne (MSE) entre l'image de sortie reconstruite et l'image de vérité de sol (GT) fournie des contours de bâtiment est calculée sur les 2N pixels de l'image avec les valeurs les plus élevées. En supposant que la plupart des N pixels de contour de l’image GT figurent également dans les 2N pixels supérieurs de la reconstruction, cette modification équilibre les deux catégories de pixels et améliore le comportement de généralisation du modèle CNN. Les expériences ont montré que la fonction de coût Top-N offre des gains de performance par rapport à une MSE standard. Une amélioration supplémentaire de la capacité de généralisation du réseau est obtenue en utilisant le décrochage. Le deuxième sous-système est un réseau de convolution profonde à super-résolution, qui effectue un mappage associatif à entrée améliorée entre les images d'entrée à basse résolution et à haute résolution. Ce réseau a été formé aux données d’altitude à basse résolution et aux photographies urbaines optiques à haute résolution correspondantes. Une telle différence de résolution entre les images optiques / satellites optiques et les données d'élévation est souvent le cas dans les applications du monde réel. / Building reconstruction from aerial photographs and other multi-source urban spatial data is a task endeavored using a plethora of automated and semi-automated methods ranging from point processes, classic image processing and laser scanning. In this thesis, an iterative relaxation system is developed based on the examination of the local context of each edge according to multiple spatial input sources (optical, elevation, shadow & foliage masks as well as other pre-processed data as elaborated in Chapter 6). All these multisource and multiresolution data are fused so that probable line segments or edges are extracted that correspond to prominent building boundaries.Two novel sub-systems have also been developed in this thesis. They were designed with the purpose to provide additional, more reliable, information regarding building contours in a future version of the proposed relaxation system. The first is a deep convolutional neural network (CNN) method for the detection of building borders. In particular, the network is based on the state of the art super-resolution model SRCNN (Dong C. L., 2015). It accepts aerial photographs depicting densely populated urban area data as well as their corresponding digital elevation maps (DEM). Training is performed using three variations of this urban data set and aims at detecting building contours through a novel super-resolved heteroassociative mapping. Another innovation of this approach is the design of a modified custom loss layer named Top-N. In this variation, the mean square error (MSE) between the reconstructed output image and the provided ground truth (GT) image of building contours is computed on the 2N image pixels with highest values . Assuming that most of the N contour pixels of the GT image are also in the top 2N pixels of the re-construction, this modification balances the two pixel categories and improves the generalization behavior of the CNN model. It is shown in the experiments, that the Top-N cost function offers performance gains in comparison to standard MSE. Further improvement in generalization ability of the network is achieved by using dropout.The second sub-system is a super-resolution deep convolutional network, which performs an enhanced-input associative mapping between input low-resolution and high-resolution images. This network has been trained with low-resolution elevation data and the corresponding high-resolution optical urban photographs. Such a resolution discrepancy between optical aerial/satellite images and elevation data is often the case in real world applications. More specifically, low-resolution elevation data augmented by high-resolution optical aerial photographs are used with the aim of augmenting the resolution of the elevation data. This is a unique super-resolution problem where it was found that many of -the proposed general-image SR propositions do not perform as well. The network aptly named building super resolution CNN (BSRCNN) is trained using patches extracted from the aforementioned data. Results show that in comparison with a classic bicubic upscale of the elevation data the proposed implementation offers important improvement as attested by a modified PSNR and SSIM metric. In comparison, other proposed general-image SR methods performed poorer than a standard bicubic up-scaler.Finally, the relaxation system fuses together all these multisource data sources comprising of pre-processed optical data, elevation data, foliage masks, shadow masks and other pre-processed data in an attempt to assign confidence values to each pixel belonging to a building contour. Confidence is augmented or decremented iteratively until the MSE error fails below a specified threshold or a maximum number of iterations have been executed. The confidence matrix can then be used to extract the true building contours via thresholding.

<p>The principal aim was to study selected aspects of indoor environment in dwellings and their association with symptoms compatible with the sick building syndrome (SBS). A validated questionnaire was developed specifically for residential indoor investigations, using sociological principles and test procedures. The questionnaire was mailed to 14,243 multi-family dwellings in Stockholm, selected by stratified random sampling. Females, subjects with a history of atopy, those above 65 y, and those in new buildings reported more symptoms. Subjects owning their own dwelling had less symptoms. A multiple regression model was developed, to identify residential buildings with a higher than expected occurrence of SBS. In total, 28.5% reported at least one sign of building dampness in their home (condensation on windows, humidity in the bathroom, mouldy odour, water leakage). All indicators of dampness were related to symptoms, even when adjusting for demographic data, and other building characteristics (OR=2.9-6.0). Associations between symptoms and other building data was evaluated in older houses, built before 1961. Subjects in older buildings with a mechanical ventilation system had fewer symptoms. Heating by electric radiators, and wood heating was associated with an increase of most types of symptoms (OR=1.2-5.0). Multiple sealing measures (OR=1.3), and major reconstruction (OR=1.1-1.9), was associated with an increase of symptoms. The effect of seasonal adapted ventilation (SAV) was studied in a small experimental study. A 20% reduction of ventilation flow from 0.5-0.8 ac/h to 0.4-0.5 ACH during the heating season increased the perception of poor indoor air quality in the dwelling in general, and in the bedroom. In conclusion, low building age, and building dampness in the dwelling are associated with SBS. In older houses, mechanical ventilation is beneficial. The thesis did not support the view that energy saving measures in general is an important risk factor for SBS, but major reconstruction and multiple sealing measures can be risk factor for symptoms. Reducing the outdoor ventilation flow below the current Swedish ventilation standard (0.5 ACH) may increase the perception of impaired air quality. </p>