Vision zero in traffic is something that car manufacturers and countries around the world strive for. To achieve this, safer and smarter cars are needed. Car manufacturers are introducing safety systems after safety systems to make it safer for the driver and other road users. One of these safety systems is LiDAR sensors. They scan the road ahead of the car and construct a 3D image of the surroundings to help and prevent accidents from happening. The sensors are very advanced but at the same time sensitive. For the sensor to function optimally, it needs the readings not to be disturbed by, for example, dirt, dust, or liquids that can end up on the surface of the sensor as the car travels. Therefore, a cleaning system needs to be developed to solve the problem. The project team gathered a large amount of information about LiDAR sensors and cleaning techniques to understand the problem. Different cleaning techniques were compared, and the project team concluded that ultrasound technology was best suited in terms of cleaning ability and water usage. The project team gathered information about ultrasound technology and built their own cleaning system to test the technology. The goal of the project was to create an innovative cleaning concept through a large amount of information and manufacture a prototype for testing the chosen concept. The result was UsoniClean, which is a concept that cleans the surface of a LiDAR sensor using ultrasound technology. The concept and prototype consist of three necessary components that create the cleaning ability. A circuit board that drives an ultrasound transducer that converts electrical current into mechanical vibration. A sonotrode, which is a metal piece screwed into the transducer to allow it to impact a larger surface area. The transducer causes the sonotrode to vibrate at the same frequency. The sonotrode, which is in contact with water and the sensor surface, causes the water to vibrate. When water vibrates at an ultrasonic frequency, it has a very effective cleaning ability that the project team uses to clean the sensor. The cleaning system successfully passed tests with test dirt that is standard within the automotive industry. UsoniClean is an innovative solution to promote safer and more secure traffic in the future.

Landslides in the Southern Appalachian Mountains of East Tennessee often activate and reactivate. Often triggered by high-intensity or prolonged rainfall, landslides are responsible for infrastructure damage, closure of transportation routes, and even fatality. The study area is defined by the New River Watershed which has high elevation and steep slopes cutting through State Route 116. The route has hairpin turns and has experienced damage from past landslide events. The geology here is mostly shale and sandstones with coal bedding throughout. Much of the soil consists of a fine-loamy texture. Most drainage occurs from the New River, fed by runoff from slopes into roadways. This area experiences heavy rainfall with a yearly average of 70 inches. Landcover consists of a mostly forested landscape with shrubs and grassland. In response to previous landslides, the Tennessee Department of Transportation (TDOT) recently repaired six areas within the route intercepted by recent landslides. Aside from the landslides near TDOT’s corridors, approximately 50 additional landslides have been found using Google Earth and LiDAR data. Landslide hotspots were identified using kernel density estimation and the nearest neighbor index. A heuristic landslide susceptibility model was prepared by weighing the ArcGIS layers: slope, soil particle, geology, curvature, elevation, distance from the stream, and land cover, in their contribution to the previous landslides. Results indicate that additional sites in Anderson and Morgan County should be studied further for potential landslide-related damage. The study will improve the proactive decisions of TDOT and justify timely monitoring, maintenance, and strategic protection of the route from slope hazards.

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Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets. / The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds.

Perceiving the surrounding multiple vehicles robustly and effectively is a very important step in building Advanced Driving Assistant System (ADAS) or autonomous vehicles. This thesis presents the design of the Light Detection and Ranging (LiDAR) perception system which consists of several sub-tasks: ground detection, object detection, object classification, and object tracking. It is believed that accomplishing these sub-tasks will provide a guideline to a vast range of potential autonomous vehicles applications. More specifically, a probability occupancy grid map based approach was developed for ground detection to address the issues of over-segmentation, under-segmentation and slow-segmentation by non-flat surface. Given the non-ground points, point cloud clustering algorithm is developed for object detection by using a Radially Bounded Nearest Neighbor (RBNN) method on the static Kd-tree. To identify the object, a supervised learning approach based on our LiDAR sensor for vehicle type classification is proposed. The proposed classification algorithm is used to classify the object into four different types: ``Sedan'', ``SUV'', ``Van'', and ``Truck''. To handle disturbances and motion uncertainties, a generalized form of Smooth Variable Structure Filter (SVSF) integrated with a combination of Hungarian algorithm (HA) and Probability Data Association Filter (PDAF), referred to as GSVSF-HA/PDAF, is developed. The developed approach is to overcome the multiple targets data association in the content of dynamics environment where the distribution of data is unpredictable. Last but not the least, a comprehensive experimental evaluation for each sub-task is presented to validate the robustness and effectiveness of our developed perception system. / Thesis / Doctor of Philosophy (PhD)

High-latitude ecosystems have experienced substantial warming over the past 40 years, which is expected to continue into the foreseeable future. Consequently, an increase in vegetation growth has occurred throughout the circumpolar North as documented through remote sensing and plot-level studies. A major component of this change is shrub expansion (shrubbing) in arctic and subarctic ecotones. However, these changes are highly variable depending on plant species, topographic position, hydrology, soils and other ecosystem properties. Changes in shrub and other vegetation properties are critical to document due to their first-order control on water, energy and carbon balances. This study uses a combination of multi-temporal LiDAR (Light Detection and Ranging) and field surveys to measure temporal changes in shrub vegetation cover over the Wolf Creek Research Basin (WCRB), a 180 km2 long-term watershed research facility located ~15 km south of Whitehorse, Yukon Territory. This work focuses on the smaller Granger Basin, a 7.6 km2 subarctic headwater catchment that straddles WCRB’s subalpine and alpine tundra ecozones with a wide range of elevation, landscape topography, and vegetation. Airborne LiDAR surveys of WCRB were conducted in August 2007 and 2018, providing an ideal opportunity to explore vegetation changes between survey years. Vegetation surveys were conducted throughout Granger Basin in summer 2019 to evaluate shrub properties for comparisons to the LiDAR. Machine learning classification algorithms were used to predict shrub presence/absence in 2018 based on rasterized LiDAR metrics with up to 97% overall independent accuracy compared to field validation points, with the best-performing model applied to the 2007 LiDAR to create binary shrub cover layers to compare between survey years. Results show a 63.3% total increase in detectable shrub cover > 0.45 m in height throughout Granger Basin between 2007 and 2018, with an average yearly expansion of 5.8%. These changes in detectable shrub cover were compared across terrain derivatives created using the LiDAR to quantify the influence of topography on shrub expansion. The terrain comparison results show that shrubs in the study area are located in and are preferentially expanding into lower and flatter areas near stream networks, at lower slope positions and with a higher potential for topographic wetness. The greatest differences in terrain derivative value distributions across the shrub and non-shrub change categories were found in terms of stream distance, elevation, and relative slope position. This expansion of shrubs into higher-resource areas is consistent with previous studies and is supported by established physical processes. As vegetation responses to warming have far-reaching influences on surface energy exchange, nutrient cycling, and the overall water balance, this increase in detectable shrub cover has a wide range of impacts on the future of northern watersheds. Overall, the findings from this research reinforce the documented increase in pan-Arctic shrub vegetation in recent years, quantify the variation in shrub expansion over terrain derivatives at the landscape scale, and demonstrate the feasibility of using LiDAR to compare changes in shrub properties over time. / Thesis / Master of Science (MSc)

Indoor spaces, where we spend the majority of our lives, greatly impact our work, social interactions, and well-being. In recognition of the central role that buildings play in our lives, architects and designers have increasingly focused on creating spaces that intentionally promote interaction and collaboration between building occupants. One challenge arising from this trend is evaluating the efficacy of new designs. This study used object tracking data for the Fall 2023 semester from a collection of lidar sensors installed in a portion of a mixed-use academic building on a university campus to algorithmically detect occupancy and serendipitous collisions between people - patterns of simultaneous movement and pause that indicate that two or more individuals have stopped and had a meaningful interaction. The algorithm detected over 14,000 collisions throughout the semester with high spatial and temporal precision. Occupancy and collisions were highly related over several scales of temporal and spatial analysis. Furthermore, several interesting patterns emerged, including (a) collisions peaked early in the semester, then declined before leveling off, (b) occupancy peaked in mid-afternoon, while collisions peaked in the late afternoon and early evening, (c) collisions peaked later in the week than did occupancy, and (d) specific hotspots were apparent at important nodes such as the bottom of stairs and near elevators. The patterns found in this study can provide insight as to how interactions can be measured using remote sensing data, and can aid designers in attempting to increase collaboration in shared indoor environments. / Master of Science / We spend lots of our times in buildings, and they are very important for our well-being. Designers have recently been focusing on promoting collaboration and interaction between people within building spaces. Despite their importance, these interactions within buildings have been challenging to categorize and analyze. This study used object-tracking data for the Fall 2023 semester from a collection of lidar sensors, which were intermittently placed in the ground-floor public spaces of a new hybrid residential-academic university building on Virginia Tech's Blacksburg campus. A computer program was written to parse through this data, and detect unplanned collisions between people; patterns of movement and pause that indicate that two or more people have stopped and had a meaningful interaction (for example, running into a friend while walking down the hallway). This study was able to detect collisions relatively well using a computer algorithm. The patterns and distributions of these collisions were then analyzed in time and space. The number of collisions and the number of people present in the space were highly related on all scales of time and space. In terms of time itself, collisions happened the most at the beginning of the semester, where they then dropped off. Collisions happened more frequently both later in the day (in afternoon, evening, and night hours) and later in the week (on Thursday, Friday, and Saturday). In terms of space, these collisions happened most frequently in the areas around the elevator, at the base of the stairs, and in the building's main lobby area. They happened less in hallways and near some seating areas. The patterns revealed from this study can help us better understand how to detect interactions between people within buildings, and can help designers increase the amount of these interactions.

Avec l'avancement des connaissances cartographiques en hydrologie forestière, il est maintenant envisageable de déterminer les limites inférieures aquatique et terrestre des écotones riverains à partir de données topographiques à haute résolution spatiale. L'utilisation d'informations dérivées des données LiDAR aéroportées pourrait permettre de localiser précisément et de décrire la transition entre les milieux aquatiques et terrestres. Quatre variables géospatiales ont été utilisées, soit le *Depth-to-Water* (DTW), le *Topographic Wetness Index* (TWI), le Modèle de Hauteur de Canopée (MHC) et la différence d'élévation adjacente (DEA) issue d'un modèle numérique de terrain (MNT). Des inventaires sur le terrain pour décrire et documenter la zone de transition riveraine ont été réalisés en2019 et 2020 dans six bassins versants localisés dans trois régions écologiques différentes du Québec (Dépression de la Tuque, Massif de la Jacques-Cartier, Plateau de l'Abitibi). Au total, 3 743 stations de caractérisation réparties le long de 896 transects de longueurs variables ont été inventoriées. Les valeurs relatives aux différentes variables géospatiales utilisées ont été extraites à partir des données d'observations collectées sur le terrain. Les résultats ont montré que chacune des variables géospatiales utilisées permet d'identifier cartographiquement, à différents niveaux, les limites aquatiques et terrestres des écotones riverains sur le territoire. Il fut démontré que la DEA et le MHC forment la combinaison la plus parcimonieuse pour identifier lesdites limites. Ainsi, il a été possible d'évaluer les capacités des variables géospatiales pour identifier des limites représentant les écotones riverains selon une définition qui intègre et respecte les différents processus écologiques liés au milieu riverain. Elles pourraient être utilisées de façon fiable dès maintenant par les différents intervenants des environnements forestiers, assurant une protection adéquate des composantes hydrologiques et riveraines et qui répondent au besoin actuel de gestion et de planification.