Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

Energetic Map Data Imputation: A Machine Learning Approach

Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profiles of 4.6 million kilometres of training and test traces. For evaluation, two test-scenarios capture the models’ performance for the analysed problem in two perspectives. First, we evaluate our ML models, followed by the problem-specific energetic evaluation perspective for better interpretability. From the latter, the results indicate energetic map data imputation performs promisingly better when using the regression instead of the classification model

Road Surface Feature Extraction and Reconstruction of Laser Point Clouds for Urban Environment

Automakers are developing end-to-end three-dimensional (3D) mapping system for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AVs). Using geomatics, artificial intelligence, and SLAM (Simultaneous Localization and Mapping) systems to handle all stages of map creation, sensor calibration and alignment. It is crucial to have a system highly accurate and efficient as it is an essential part of vehicle controls. Such mapping requires significant resources to acquire geographic information (GIS and GPS), optical laser and radar spectroscopy, Lidar, and 3D modeling applications in order to extract roadway features (e.g., lane markings, traffic signs, road-edges) detailed enough to construct a “base map”. To keep this map current, it is necessary to update changes due to occurring events such as construction changes, traffic patterns, or growth of vegetation. The information of the road play a very important factor in road traffic safety and it is essential for for guiding autonomous vehicles (AVs), and prediction of upcoming road situations within AVs. The data size of the map is extensive due to the level of information provided with different sensor modalities for that reason a data optimization and extraction from three-dimensional (3D) mobile laser scanning (MLS) point clouds is presented in this thesis. The research shows the proposed hybrid filter configuration together with the dynamic developed mechanism provides significant reduction of the point cloud data with reduced computational or size constraints. The results obtained in this work are proven by a real-world system

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

Building structural characterization using mobile terrestrial point cloud for flood risk anticipation

Compte tenu de la fréquence élevée et de l'impact majeur des inondations, les décideurs, les acteurs des municipalités et le ministère de la sécurité publique ont un besoin urgent de disposer d'outils permettant de prédire ou d'évaluer l'importance des inondations et leur impact sur la population. D'après les statistiques, le premier étage des bâtiments, ainsi que les ouvertures inférieures, sont plus susceptibles de subir des dommages lors d'une inondation. Ainsi, dans le cadre de l'évaluation de l'impact des inondations, il serait nécessaire d'identifier l'emplacement de l'ouverture la plus basse des bâtiments et surtout sa hauteur par rapport au sol. Le système de balayage laser mobile (MLS) monté sur un véhicule s'est avéré être l'une des sources les plus fiables pour caractériser les bâtiments. Il peut produire des millions de points géoréférencés en 3D avec un niveau de détail suffisant, grâce à son point de vue depuis la rue et sa proximité. De plus, l'augmentation du nombre de jeux de données, issues des MLS acquis dans les villes et les environnements ruraux, permet de développer des approches pour caractériser les maisons résidentielles à l'échelle provinciale. Plusieurs défis sont associés à l'extraction d'informations descriptives des façades de bâtiments à l'aide de données MLS. Ainsi, les occlusions devant une façade rendent impossible l'obtention de points 3D sur ces parties de la façade. Aussi, comme les fenêtres sont principalement constituées de verre, qui ne réfléchit pas les signaux laser, les points disponibles pour celles-ci sont généralement limités. De plus, les approches de détection exploitent la répétitivité et les positions symétriques des ouvertures sur la façade. Mais ces caractéristiques sont absentes pour des maisons rurales et résidentielles. Finalement, la variabilité de la densité de points dans les données MLS rend difficile le processus de détection lorsqu'on travaille à l'échelle d'une ville. Par conséquent, l'objectif principal de cette recherche est de concevoir et de développer une approche globale d'extraction efficace des ouvertures présentes sur une façade. La solution proposée se compose de trois phases: l'extraction des façades, la détection des ouvertures et l'identification des occlusions. La première phase utilise une approche de segmentation adaptative par croissance de régions pour extraire la boîte englobante 3D de la façade. La deuxième phase combine la détection de trous avec une technique de maillage pour extraire les boîtes englobantes 2D des ouvertures. La dernière phase, qui vise à discriminer les occlusions des ouvertures, est en cours d'achèvement. Des évaluations qualitatives et quantitatives ont été réalisées à l'aide d'un jeu de données réelles, fourni par Jakarto Cartographie 3D Inc., de la province de Québec, au Canada. Les statistiques ont révélé que l'approche proposée pouvait obtenir de bons taux de performance malgré la complexité du jeu de données, représentatif des données acquises en situation réelle. Les défis concernant l'auto-occlusion de certaines façades et la présence de grandes occlusions environnantes seront à étudier plus en profondeur afin d'obtenir des informations plus précises sur les ouvertures des façades.Given the high frequency and major impact of floods, decision-makers, stakeholders in municipalities and public security ministry are in the urgent need to have tools allowing to predict or assess the significance of flood events and their impact on the population. Based on statistics, the first floor of the buildings, as well as the lower openings, are more likely subject to potential damage during a flood event. Thus, in the context of flood impact assessment, it would be required identifying the location of the buildings' lowest opening and especially its height above the ground. The capacity to characterize building with a relevant level of detail depends on the data sources used for the modeling. Different sources of data have been employed to characterize buildings' façade and openings. Mobile Laser Scanning (MLS) system mounted on a vehicle has proved to be one of the most reliable sources in this domain. It can produce millions of 3D georeferenced points with sufficient level of detail of the building facades and its openings, due to its street-view and close-range distance. Moreover, the increase of MLS providers and acquisitions in towns and rural environments, makes it possible to develop approaches to characterize residential houses at a provincial scale. Although being effective, several challenges are associated with extracting descriptive information of building facades using MLS data. The presence of occlusion in front of a facade makes it impossible to obtain the 3D points of the covered parts of the facade. Given the fact that windows mostly consist of glass and laser signals could not be reflected from the glass, limited points are usually available for windows. While the repetitive pattern and symmetrical positions of the openings on the facade makes it easier for the detection system to extract them, this characteristic is missing on the facade on rural and residential houses. The inconsistency of the point density in MLS data make the detection process even harder when working at city scale. Accordingly, the main objective of this research is to design and develop a comprehensive approach that effectively extracts facade openings. In order to meet the research project objective, the proposed solution consists of three phases including facade extraction, opening detection, and occlusion recognition. The first phase employs an adaptive region growing segmentation approach to extract the 3D bounding box of the facade. The second phase combines a hole-based assumption with an XZ gridding technique to extract 2D bounding boxes of the openings. The last phase which recognizes holes related to the occlusion from the openings is currently being completed. Qualitative and quantitative evaluations were performed using a real-word dataset provided by Jakarto Cartographie 3D inc. of the Quebec Province, Canada. Statistics revealed that the proposed approach could obtain good performance rates despite the complexity of the dataset, representative of the data acquired in real situations. Challenges regarding facade's self-occlusion and the presence of large surrounding occlusions should be further investigated for obtaining more accurate opening information on the facade

A window to the past through modern urban environments: Developing a photogrammetric workflow for the orientation parameter estimation of historical images

The ongoing process of digitization in archives is providing access to ever-increasing historical image collections. In many of these repositories, images can typically be viewed in a list or gallery view. Due to the growing number of digitized objects, this type of visualization is becoming increasingly complex. Among other things, it is difficult to determine how many photographs show a particular object and spatial information can only be communicated via metadata. Within the scope of this thesis, research is conducted on the automated determination and provision of this spatial data. Enhanced visualization options make this information more eas- ily accessible to scientists as well as citizens. Different types of visualizations can be presented in three-dimensional (3D), Virtual Reality (VR) or Augmented Reality (AR) applications. However, applications of this type require the estimation of the photographer’s point of view. In the photogrammetric context, this is referred to as estimating the interior and exterior orientation parameters of the camera. For determination of orientation parameters for single images, there are the established methods of Direct Linear Transformation (DLT) or photogrammetric space resection. Using these methods requires the assignment of measured object points to their homologue image points. This is feasible for single images, but quickly becomes impractical due to the large amount of images available in archives. Thus, for larger image collections, usually the Structure-from-Motion (SfM) method is chosen, which allows the simultaneous estimation of the interior as well as the exterior orientation of the cameras. While this method yields good results especially for sequential, contemporary image data, its application to unsorted historical photographs poses a major challenge. In the context of this work, which is mainly limited to scenarios of urban terrestrial photographs, the reasons for failure of the SfM process are identified. In contrast to sequential image collections, pairs of images from different points in time or from varying viewpoints show huge differences in terms of scene representation such as deviations in the lighting situation, building state, or seasonal changes. Since homologue image points have to be found automatically in image pairs or image sequences in the feature matching procedure of SfM, these image differences pose the most complex problem. In order to test different feature matching methods, it is necessary to use a pre-oriented historical dataset. Since such a benchmark dataset did not exist yet, eight historical image triples (corresponding to 24 image pairs) are oriented in this work by manual selection of homologue image points. This dataset allows the evaluation of frequently new published methods in feature matching. The initial methods used, which are based on algorithmic procedures for feature matching (e.g., Scale Invariant Feature Transform (SIFT)), provide satisfactory results for only few of the image pairs in this dataset. By introducing methods that use neural networks for feature detection and feature description, homologue features can be reliably found for a large fraction of image pairs in the benchmark dataset. In addition to a successful feature matching strategy, determining camera orientation requires an initial estimate of the principal distance. Hence for historical images, the principal distance cannot be directly determined as the camera information is usually lost during the process of digitizing the analog original. A possible solution to this problem is to use three vanishing points that are automatically detected in the historical image and from which the principal distance can then be determined. The combination of principal distance estimation and robust feature matching is integrated into the SfM process and allows the determination of the interior and exterior camera orientation parameters of historical images. Based on these results, a workflow is designed that allows archives to be directly connected to 3D applications. A search query in archives is usually performed using keywords, which have to be assigned to the corresponding object as metadata. Therefore, a keyword search for a specific building also results in hits on drawings, paintings, events, interior or detailed views directly connected to this building. However, for the successful application of SfM in an urban context, primarily the photographic exterior view of the building is of interest. While the images for a single building can be sorted by hand, this process is too time-consuming for multiple buildings. Therefore, in collaboration with the Competence Center for Scalable Data Services and Solutions (ScaDS), an approach is developed to filter historical photographs by image similarities. This method reliably enables the search for content-similar views via the selection of one or more query images. By linking this content-based image retrieval with the SfM approach, automatic determination of camera parameters for a large number of historical photographs is possible. The developed method represents a significant improvement over commercial and open-source SfM standard solutions. The result of this work is a complete workflow from archive to application that automatically filters images and calculates the camera parameters. The expected accuracy of a few meters for the camera position is sufficient for the presented applications in this work, but offer further potential for improvement. A connection to archives, which will automatically exchange photographs and positions via interfaces, is currently under development. This makes it possible to retrieve interior and exterior orientation parameters directly from historical photography as metadata which opens up new fields of research.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.4 Initial parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3.5 Bundle adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.6 Dense reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.7 Georeferencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.4 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2 Generation of a benchmark dataset using historical photographs for the evaluation of feature matching methods 29 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 Image differences based on digitization and image medium . . . . . . . 30 2.1.2 Image differences based on different cameras and acquisition technique 31 2.1.3 Object differences based on different dates of acquisition . . . . . . . . 31 2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 The image dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4 Comparison of different feature detection and description methods . . . . . . 35 2.4.1 Oriented FAST and Rotated BRIEF (ORB) . . . . . . . . . . . . . . . 36 2.4.2 Maximally Stable Extremal Region Detector (MSER) . . . . . . . . . 36 2.4.3 Radiation-invariant Feature Transform (RIFT) . . . . . . . . . . . . . 36 2.4.4 Feature matching and outlier removal . . . . . . . . . . . . . . . . . . 36 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Photogrammetry as a link between image repository and 4D applications 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 IX Contents 3.2 Multimodal access on repositories . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Conventional access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Virtual access using online collections . . . . . . . . . . . . . . . . . . 48 3.2.3 Virtual museums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3 Workflow and access strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Browser access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.5 VR and AR access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4 An adapted Structure-from-Motion Workflow for the orientation of historical images 69 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 Related Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.2.1 Historical images for 3D reconstruction . . . . . . . . . . . . . . . . . 72 4.2.2 Algorithmic Feature Detection and Matching . . . . . . . . . . . . . . 73 4.2.3 Feature Detection and Matching using Convolutional Neural Networks 74 4.3 Feature Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.4 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.4.1 Step 1: Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4.2 Step 2.1: Feature Detection and Matching . . . . . . . . . . . . . . . . 78 4.4.3 Step 2.2: Vanishing Point Detection and Principal Distance Estimation 80 4.4.4 Step 3: Scene Reconstruction . . . . . . . . . . . . . . . . . . . . . . . 80 4.4.5 Comparison with Three Other State-of-the-Art SfM Workflows . . . . 81 4.5 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.8 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5 Fully automated pose estimation of historical images 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.1 Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.2 Feature Detection and Matching . . . . . . . . . . . . . . . . . . . . . 101 5.3 Data Preparation: Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . 102 5.3.1 Experiment and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2.1 Layer Extraction Approach (LEA) . . . . . . . . . . . . . . . 104 5.3.2.2 Attentive Deep Local Features (DELF) Approach . . . . . . 105 5.3.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.4 Camera Pose Estimation of Historical Images Using Photogrammetric Methods 110 5.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4.1.1 Benchmark Datasets . . . . . . . . . . . . . . . . . . . . . . . 111 5.4.1.2 Retrieval Datasets . . . . . . . . . . . . . . . . . . . . . . . . 113 5.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.4.2.1 Feature Detection and Matching . . . . . . . . . . . . . . . . 115 5.4.2.2 Geometric Verification and Camera Pose Estimation . . . . . 116 5.4.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6 Related publications 129 6.1 Photogrammetric analysis of historical image repositores for virtual reconstruction in the field of digital humanities . . . . . . . . . . . . . . . . . . . . . . . 130 6.2 Feature matching of historical images based on geometry of quadrilaterals . . 131 6.3 Geo-information technologies for a multimodal access on historical photographs and maps for research and communication in urban history . . . . . . . . . . 132 6.4 An automated pipeline for a browser-based, city-scale mobile 4D VR application based on historical images . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.5 Software and content design of a browser-based mobile 4D VR application to explore historical city architecture . . . . . . . . . . . . . . . . . . . . . . . . 134 7 Synthesis 135 7.1 Summary of the developed workflows . . . . . . . . . . . . . . . . . . . . . . . 135 7.1.1 Error assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.1.2 Accuracy estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.1.3 Transfer of the workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.2 Developments and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8 Appendix 149 8.1 Setup for the feature matching evaluation . . . . . . . . . . . . . . . . . . . . 149 8.2 Transformation from COLMAP coordinate system to OpenGL . . . . . . . . 150 References 151 List of Figures 165 List of Tables 167 List of Abbreviations 169Der andauernde Prozess der Digitalisierung in Archiven ermöglicht den Zugriff auf immer größer werdende historische Bildbestände. In vielen Repositorien können die Bilder typischerweise in einer Listen- oder Gallerieansicht betrachtet werden. Aufgrund der steigenden Zahl an digitalisierten Objekten wird diese Art der Visualisierung zunehmend unübersichtlicher. Es kann u.a. nur noch schwierig bestimmt werden, wie viele Fotografien ein bestimmtes Motiv zeigen. Des Weiteren können räumliche Informationen bisher nur über Metadaten vermittelt werden. Im Rahmen der Arbeit wird an der automatisierten Ermittlung und Bereitstellung dieser räumlichen Daten geforscht. Erweiterte Visualisierungsmöglichkeiten machen diese Informationen Wissenschaftlern sowie Bürgern einfacher zugänglich. Diese Visualisierungen können u.a. in drei-dimensionalen (3D), Virtual Reality (VR) oder Augmented Reality (AR) Anwendungen präsentiert werden. Allerdings erfordern Anwendungen dieser Art die Schätzung des Standpunktes des Fotografen. Im photogrammetrischen Kontext spricht man dabei von der Schätzung der inneren und äußeren Orientierungsparameter der Kamera. Zur Bestimmung der Orientierungsparameter für Einzelbilder existieren die etablierten Verfahren der direkten linearen Transformation oder des photogrammetrischen Rückwärtsschnittes. Dazu muss eine Zuordnung von gemessenen Objektpunkten zu ihren homologen Bildpunkten erfolgen. Das ist für einzelne Bilder realisierbar, wird aber aufgrund der großen Menge an Bildern in Archiven schnell nicht mehr praktikabel. Für größere Bildverbände wird im photogrammetrischen Kontext somit üblicherweise das Verfahren Structure-from-Motion (SfM) gewählt, das die simultane Schätzung der inneren sowie der äußeren Orientierung der Kameras ermöglicht. Während diese Methode vor allem für sequenzielle, gegenwärtige Bildverbände gute Ergebnisse liefert, stellt die Anwendung auf unsortierten historischen Fotografien eine große Herausforderung dar. Im Rahmen der Arbeit, die sich größtenteils auf Szenarien stadträumlicher terrestrischer Fotografien beschränkt, werden zuerst die Gründe für das Scheitern des SfM Prozesses identifiziert. Im Gegensatz zu sequenziellen Bildverbänden zeigen Bildpaare aus unterschiedlichen zeitlichen Epochen oder von unterschiedlichen Standpunkten enorme Differenzen hinsichtlich der Szenendarstellung. Dies können u.a. Unterschiede in der Beleuchtungssituation, des Aufnahmezeitpunktes oder Schäden am originalen analogen Medium sein. Da für die Merkmalszuordnung in SfM automatisiert homologe Bildpunkte in Bildpaaren bzw. Bildsequenzen gefunden werden müssen, stellen diese Bilddifferenzen die größte Schwierigkeit dar. Um verschiedene Verfahren der Merkmalszuordnung testen zu können, ist es notwendig einen vororientierten historischen Datensatz zu verwenden. Da solch ein Benchmark-Datensatz noch nicht existierte, werden im Rahmen der Arbeit durch manuelle Selektion homologer Bildpunkte acht historische Bildtripel (entspricht 24 Bildpaaren) orientiert, die anschließend genutzt werden, um neu publizierte Verfahren bei der Merkmalszuordnung zu evaluieren. Die ersten verwendeten Methoden, die algorithmische Verfahren zur Merkmalszuordnung nutzen (z.B. Scale Invariant Feature Transform (SIFT)), liefern nur für wenige Bildpaare des Datensatzes zufriedenstellende Ergebnisse. Erst durch die Verwendung von Verfahren, die neuronale Netze zur Merkmalsdetektion und Merkmalsbeschreibung einsetzen, können für einen großen Teil der historischen Bilder des Benchmark-Datensatzes zuverlässig homologe Bildpunkte gefunden werden. Die Bestimmung der Kameraorientierung erfordert zusätzlich zur Merkmalszuordnung eine initiale Schätzung der Kamerakonstante, die jedoch im Zuge der Digitalisierung des analogen Bildes nicht mehr direkt zu ermitteln ist. Eine mögliche Lösung dieses Problems ist die Verwendung von drei Fluchtpunkten, die automatisiert im historischen Bild detektiert werden und aus denen dann die Kamerakonstante bestimmt werden kann. Die Kombination aus Schätzung der Kamerakonstante und robuster Merkmalszuordnung wird in den SfM Prozess integriert und erlaubt die Bestimmung der Kameraorientierung historischer Bilder. Auf Grundlage dieser Ergebnisse wird ein Arbeitsablauf konzipiert, der es ermöglicht, Archive mittels dieses photogrammetrischen Verfahrens direkt an 3D-Anwendungen anzubinden. Eine Suchanfrage in Archiven erfolgt üblicherweise über Schlagworte, die dann als Metadaten dem entsprechenden Objekt zugeordnet sein müssen. Eine Suche nach einem bestimmten Gebäude generiert deshalb u.a. Treffer zu Zeichnungen, Gemälden, Veranstaltungen, Innen- oder Detailansichten. Für die erfolgreiche Anwendung von SfM im stadträumlichen Kontext interessiert jedoch v.a. die fotografische Außenansicht des Gebäudes. Während die Bilder für ein einzelnes Gebäude von Hand sortiert werden können, ist dieser Prozess für mehrere Gebäude zu zeitaufwendig. Daher wird in Zusammenarbeit mit dem Competence Center for Scalable Data Services and Solutions (ScaDS) ein Ansatz entwickelt, um historische Fotografien über Bildähnlichkeiten zu filtern. Dieser ermöglicht zuverlässig über die Auswahl eines oder mehrerer Suchbilder die Suche nach inhaltsähnlichen Ansichten. Durch die Verknüpfung der inhaltsbasierten Suche mit dem SfM Ansatz ist es möglich, automatisiert für eine große Anzahl historischer Fotografien die Kameraparameter zu bestimmen. Das entwickelte Verfahren stellt eine deutliche Verbesserung im Vergleich zu kommerziellen und open-source SfM Standardlösungen dar. Das Ergebnis dieser Arbeit ist ein kompletter Arbeitsablauf vom Archiv bis zur Applikation, der automatisch Bilder filtert und diese orientiert. Die zu erwartende Genauigkeit von wenigen Metern für die Kameraposition sind ausreichend für die dargestellten Anwendungen in dieser Arbeit, bieten aber weiteres Verbesserungspotential. Eine Anbindung an Archive, die über Schnittstellen automatisch Fotografien und Positionen austauschen soll, befindet sich bereits in der Entwicklung. Dadurch ist es möglich, innere und äußere Orientierungsparameter direkt von der historischen Fotografie als Metadaten abzurufen, was neue Forschungsfelder eröffnet.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . .

APPLICATIONS OF UAS IMAGERY IN WHEAT BREEDING

Plant breeding is a field of study with goals that have not changed significantly over time: develop cultivars with high yield, disease resistance, and drought tolerance, to name a few. While the goals of a breeding program may not change frequently, the form and technology used with which those goals are achieved are constantly evolving. High throughput phenotyping (HTP) with unoccupied aerial systems (UAS) shows significant promise in improving how crops are bred. Data collected from UAS can provide a breeder with new insights into how cultivars respond to stress and a particular environment, creating potential use cases for improving other areas of breeding, such as genomic selection and how field experiments are designed and analyzed. These new technologies, however, should not be adopted without consideration. The first study, outlined here, utilized three different HTP platforms and collection methodologies, two ground systems and one UAS-based, to determine if there is a difference in the quality of data collected. Across four years, data collected from ground systems only moderately correlated to UAS. It was also shown that data collected with UAS produced more heritable data than that collected with either ground-based system. While manufacturing specifications of the data collected from remote sensors may be similar, it is essential to be aware of the methodology used in the collection. Reflectance data standardization, sensor platform, and environmental conditions can significantly impact the quality of the data obtained and limit utility across platforms and methodologies. In the second study, spectral reflectance indices (SRI) were evaluated for their ability to improve genomic selection (GS). SRIs collected on 11,593 plots across four years were used with genomic data in univariate models as covariates and in multivariate models as secondary response variables for the assessment of prediction accuracy of grain yield. Including SRI data as covariates in univariate genomic prediction models improved prediction accuracy over the control GS model but was unreliable across years. In multivariate models, SRIs improved prediction performance across years, but due to the dataset size, high-performance computational resources were required, which could limit feasibility in an applied setting. The final study highlights the potential for SRI to improve how a breeder deals with field variability in yield trial experiments. Across three years, 47 breeding trials were evaluated under three spatial analysis strategies: linear models incorporating block-effect, row-column effect, and 2D splines. Model fit was improved across all spatial analysis methods when SRIs were incorporated as covariates. Model fitness was most greatly improved in unreplicated early-generation trials. This study highlighted the potential of SRIs to enhance how breeding trials are analyzed despite extreme environmental variables and climate conditions. This collective research highlights the challenges and benefits of utilizing UAS imagery in an applied breeding pipeline. When used strategically, the insights gained from UAS will, like genomic selection, make it an invaluable tool in the plant breeder's toolbelt

A data-driven approach to road accidents in the municipality of Lisbon

Traffic accidents in urban areas lead to reduced quality of life and social inequality in cities, specially in third world countries. The growth of the urban mesh and the population density is seldom accompanied by the development or sizing of the road infrastructure. It is a fact that the number and severity of road accidents in Portugal have been decreasing over the last thirty years, bringing us closer to the European average. However, despite these facts, the situation remains worrying. Despite the adoption of programs such as the European Commission Road Safety Program and the recent EU Road Safety Policy Framework 2021-2030 or, on a national basis, the PENSE 2020 - National Strategic Plan for Road Safety the number of road accidents with victims in the district of Lisbon is still higher than the European average. Thus, and for this dissertation, we conducted an exploratory data analysis (EDA) on the combined data of traffic incidents recorded in the occurrence management system of the Lisbon Fire Brigade Regiment (RSB) and the road accidents reported to ANSR by the security forces (GNR and PSP) through the Statistical Bulletin of Traffic Accidents (BEAV). Furthermore, with data from occurrences in the Municipality of Lisbon between 2010 and 2020, to identify the existence of Black Spots in Lisbon's roads and which are the most significant and contributing factors to explain their existence. The data on road accidents were also georeferenced to capitalize their spatial existence and, consequently, better understand the existing spatial patterns and risk factors. Subsequently, through the use of the ArcGIS Pro we apply the algorithms of the Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of the black spots, and that human, environmental and circumstantial factors have an influence on the severity of accidents, being the content validity guaranteed through an expert committee. This way, our research goal is to contribute to identify accident concentration areas in the city of Lisbon (hotspots), considering their influencing conditions.Os acidentes de trânsito em áreas urbanas conduzem à redução da qualidade de vida e à desigualdade social nas cidades, especialmente nos países em desenvolvimento. O crescimento da malha urbana, assim como, a densidade populacional raramente é acompanhada pelo desenvolvimento ou dimensionamento da infraestrutura rodoviária. É um facto que o número e a gravidade dos acidentes rodoviários em Portugal têm vindo a diminuir ao longo dos últimos trinta anos, o que permitiu aproximarmos da média Europeia, apesar destes factos a situação continua a ser preocupante. Apesar da adoção de programas como o Programa de Segurança Rodoviária da Comissão Europeia ou, numa base nacional, o PENSE 2020 - Plano Estratégico Nacional para a Segurança Rodoviária os números de acidentes de viação com vítimas no distrito de Lisboa continuam a ser mais elevados do que a média europeia. Desta forma e para efeitos deste trabalho realizamos uma análise de dados exploratória (AED) aos dados dos incidentes de transito registados no sistema de gestão de ocorrências do Regimento de Sapadores Bombeiros de Lisboa e a os dados de acidentes rodoviários reportados à ANSR pelas forças de segurança (GNR e PSP) através do Boletim Estatístico de Acidentes de Viação (BEAV) e ocorridos no concelho de Lisboa entre 2010 e 2020 por forma a identificar a existência de Pontos Negros nas vias de Lisboa e quais os fatores mais significantes e contribuintes que permitam explicar a sua existência. Os dados relativos aos acidentes rodoviários foram também georreferenciados para capitalizar a sua existência espacial e, consequentemente, compreender melhor os padrões espaciais existentes e os fatores de risco. Posteriormente através do recurso ArcGIS Pro aplicaram-se os algoritmos das ferramentas Densidade de Kernel e Hot Spot Analysis (Getis-Ord Gi*), identificando a existência dos pontos negros, e que fatores humanos, ambientais e circunstanciais têm influência na gravidade dos acidentes e que algumas variáveis de exposição foram consideradas importantes na explicação da ocorrência dos mesmos, sendo a validade do conteúdo garantida através de uma comissão de especialistas. Pretende-se, assim, contribuir para a identificação das zonas de concentração de acidentes da cidade de Lisboa (hotspots), tendo em conta as suas condições influenciadoras. Potenciando a segurança rodoviária no município

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images