<p>One of the tasks of the Swedish Civil Contingencies Agency, MSB (<em>Myndigheten för Samhällsskydd och Beredskap</em>), is to partake in relief efforts in foreign countries. Before these relief efforts, roads are inspected for accessibility to ensure that communications will work. The road inspections are currently being performed by hand with the use of pen and paper, which brings difficulties to distribution of information.</p><p>During the course of this thesis project, Sweco Position Karlstad has developed a digital road inspection tool for MSB that recieves information from a road inspector and stores this information in a database. This road inspection tool may in the future replace the pen and paper inspections and ease the work of the road inspectors.</p><p>In this dissertation, a prototype of a web map that displays data from a road inspection is presented. Using a web map to display the results of a road inspection offers several advantages, as it eases distribution of information and allows end users to make selections about what data they want to view.</p>

This paper describes two methods for constructing a depth map from images. Each method has two stages. First, one or more needle maps are determined using a pair of images. This process employs either the Marr-Poggio-Grimson stereo and shape-from-shading, or, instead, photometric stereo. Secondly, a depth map is constructed from the needle map or needle maps computed by the first stage. Both methods make use of an iterative relaxation method to obtain the final depth map.

Autonomous Vehicles (AV) can navigate itself from point `A' to point `B' without the aid of humans. Research on autonomous vehicles were primarily focused on the localization, navigation and path planning schemes. This led to numerous methods in each of the elds of focus. This research focuses on creating a scheme for the autonomous vehicle to navigate using minimal sensors and get maximum data/infor- mation from the map. At rst a digital map contains various structures and each has an associated database. This database contains the details of the environment. At present these data are manipulated for use by humans and for this map to be used with autonomous vehicle require more sensors. This work designs maps for use with autonomous vehicle and navigates using di erential GPS (dGPS) of high accuracy for localization. Then the vehicle gets path and directions from digital map and nav- igates using multiple waypoints that are provided by the path. Finally, the scheme is tested and demonstrated through simulation and test results.

One of the tasks of the Swedish Civil Contingencies Agency, MSB (Myndigheten för Samhällsskydd och Beredskap), is to partake in relief efforts in foreign countries. Before these relief efforts, roads are inspected for accessibility to ensure that communications will work. The road inspections are currently being performed by hand with the use of pen and paper, which brings difficulties to distribution of information. During the course of this thesis project, Sweco Position Karlstad has developed a digital road inspection tool for MSB that recieves information from a road inspector and stores this information in a database. This road inspection tool may in the future replace the pen and paper inspections and ease the work of the road inspectors. In this dissertation, a prototype of a web map that displays data from a road inspection is presented. Using a web map to display the results of a road inspection offers several advantages, as it eases distribution of information and allows end users to make selections about what data they want to view.

In this thesis work, we propose a Map-Reduce variant of the Growing Hierarchical Self Organizing Map (GHSOM) called MR-GHSOM, which is capable of handling mixed attribute datasets of massive size. The Self Organizing Map (SOM) has proved to be a useful unsupervised data analysis algorithm. It projects a high dimensional data onto a lower dimensional grid of neurons. However, the SOM has some limitations owing to its static structure and the incapability to mirror the hierarchical relations in the data. The GHSOM overcomes these shortcomings of the SOM by providing a dynamic structure that adapts its shape according to the input data. It is capable of growing dynamically in terms of the size of the individual neuron layers to represent data at the desired granularity as well as in depth to model the hierarchical relations in the data. However, the training of the GHSOM requires multiple passes over an input dataset. This makes it difficult to use the GHSOM for massive datasets. In this thesis work, we propose a Map-Reduce variant of the GHSOM called MR-GHSOM, which is capable of processing massive datasets. The MR-GHSOM is implemented using the Apache Spark cluster computing engine and leverages the popular Map-Reduce programming model. This enables us to exploit the usefulness and dynamic capabilities of the GHSOM even for a large dataset. Moreover, the conventional GHSOM algorithm can handle datasets with numeric attributes only. This is owing to the fact that it relies heavily on the Euclidean space dissimilarity measures of the attribute vectors. The MR-GHSOM further extends the GHSOM to handle mixed attribute - numeric and categorical - datasets. It accomplishes this by adopting the distance hierarchy approach of managing mixed attribute datasets. The proposed MR-GHSOM is thus capable of handling massive datasets containing mixed attributes. To demonstrate the effectiveness of the MR-GHSOM in terms of clustering of mixed attribute datasets, we present the results produced by the MR-GHSOM on some popular datasets. We further train our MR-GHSOM on a Census dataset containing mixed attributes and provide an analysis of the results.

The thesis deals with map content analyzes of selected historical and contemporary cartographic products which display area Blovicko and production of Homeland Studies map of this area. Except the foundations of cartography and Homeland Studies maps, in the theoretical part is also described and defined area of interest. The practical part contains a description of map content analyzes of geographical names, map symbols and countryside development and also a description of creating Homeland Studies map of Blovicko. Parts of the thesis are tables related to map content analysis, sections of cartographic products which were used for the analysis and the Home Studies map in printed and electronic shape.

Les informations contenues dans les cartes routières numériques revêtent une importance grandissante dans le domaine des véhicules intelligents. La prise en compte d’environnements de plus en plus complexes a augmenté le niveau de précision exigé des informations cartographiques. Les cartes routières numériques, considérées ici comme des bases de données géographiques, contiennent des informations contextuelles sur le réseau routier, facilitant la compréhension correcte de l’environnement. En les combinant avec les données provenant des capteurs embarqués, une représentation plus fine de l’environnement peut être obtenue, améliorant grandement la compréhension de contexte du véhicule et la prise de décision. La performance des différents capteurs peut varier grandement en fonction du lieu considéré, ceci étant principalement dû à des facteurs environnementaux. Au contraire, une carte peut fournir ses informations de manière fiable, sans être affectée par ces éléments extérieurs, mais pour cela, elle doit reposer sur un autre élément essentiel : une source de localisation. Le secteur automobile utilise les systèmes de localisation globale par satellite (GNSS) à des fins de localisation absolue, mais cette solution n’est pas parfaite, étant soumise à différentes sources d’erreur. Ces erreurs sont elles aussi dépendantes de l’environnent d’évolution du véhicule (par exemple, des multi-trajets causés par des bâtiments). Nous sommes donc en présence de deux systèmes centraux, dont les performances sont d´dépendantes du lieu considéré. Cette étude se focalise sur leur dénominateur commun : la carte routière numérique, et son utilisation en tant qu’outil d’évaluation de leur performance. L’idée développée durant cette thèse est d’utiliser la carte en tant que canevas d’apprentissage, pour stocker des informations géoréférencées sur la performance des diésèrent capteurs équipant le véhicule, au cours de trajets répétitifs. Pour cela, une localisation robuste, relative à la carte, est nécessaire au travers d’une méthode de map-matching. La problématique principale réside dans la différence de précision entre la carte et le positionnement GNSS, créant des situations ambigües. Durant cette thèse, un algorithme de map-matching a été conçu pour gérer ces ambigüités en fournissant des hypothèses multiples lorsque nécessaire. L’objectif est d’assurer l’intégrité de l’algorithme en retournant un ensemble d’hypothèses contenant l’hypothèse correcte avec une grande probabilité. Cet algorithme utilise les capteurs proprioceptifs dans une approche de navigation à l’estime aidée d’informations cartographiques. Une procédure d’évaluation de cohérence, utilisant le GNSS comme information redondante de positionnement est ensuite appliquée, visant à isoler une hypothèse cohérente unique qui pourra ainsi être utilisée avec confiance dans le processus d’écriture dans la carte. L’utilisation de la carte numérique en écriture/lecture a été évaluée et la procédure complète d’écriture a été testée sur des données réelles, enregistrées par des véhicules expérimentaux sur route ouverte. / The information stored in digital road maps has become very important for intelligent vehicles. As intelligent vehicles address more complex environments, the accuracy requirements for this information have increased. Regarded as a geographic database, digital road maps contain contextual information about the road network, crucial for a good understanding of the environment. When combined with data acquired from on-board sensors, a better representation of the environment can be made, improving the vehicle’s situation understanding. Sensors performance can vary drastically depending on the location of the vehicle, mainly due to environmental factors. Comparatively, a map can provide prior information more reliably but to do so, it depends on another essential component: a localization system. Global Navigation Satellite Systems (GNSS) are commonly used in automotive to provide an absolute positioning of the vehicle, but its accuracy is not perfect: GNSS are prone to errors, also depending greatly on the environment (e.g., multipaths). Perception and localization systems are two important components of an intelligent vehicle whose performances vary in function of the vehicle location. This research focuses on their common denominator, the digital road map, and its use as a tool to assess their performance. The idea developed during this thesis is to use the map as a learning canvas, to store georeferenced information about the performance of the sensors during repetitive travels. This requires a robust localization with respect to the map to be available, through a process of map-matching. The main problematic is the discrepancy between the accuracy of the map and of the GNSS, creating ambiguous situations. This thesis develops a map-matching algorithm designed to cope with these ambiguities by providing multiple hypotheses when necessary. The objective is to ensure the integrity of the result by returning a hypothesis set containing the correct matching with high probability. The method relies on proprioceptive sensors via a dead-reckoning approach aided by the map. A coherence checking procedure using GNSS redundant information is then applied to isolate a single map-matching result that can be used to write learning data with confidence in the map. The possibility to handle the digital map in read/write operation has been assessed and the whole writing procedure has been tested on data recorded by test vehicles on open roads.

This report is the result of a thesis work carried out on the Risifrutti production at the company Procordia Food AB. The thesis is at a master level and is the final work for the author to receive a Master of Science in Innovative production at Mälardalen University. The thesis comprises 30 credits and has lasted for 18 weeks. The thesis is mainly a research on whether it is possible to perform an Environmental Value Stream Map (E-VSM) in a food processing industry and an evaluation if the tools E-VSM and Green Performance Map (GPM) are useful in this type of industry. An extra part to this research is to use the results from the E-VSM and give Procordia Food AB information on improvement potentials in the Risifrutti production. The author has found it difficult to collect some of the quantitative data to the E-VSM, mainly regarding energy, but by using the Value Stream Mapping (VSM) tool and other Lean tools the author was able to identify and quantify several wastes in the Risifrutti production. Recommendations for improvements based on these wastes have been given to the company. In the research part of this report the utility of the Value Stream Map, Environmental Value Stream Map and Green Performance Map in a food processing industry have been analyzed. The author has found that the Value Stream Map has been a useful tool since waste and unevenness in the tact of the production has been identified. The Environmental Value Stream Map and the Green Performance Map are also great tools when a company wants to analyze their environmental impact and then invest in environmental improvements. However, no profound energy analyzes has been done in the Risifrutti production because of the lack of energy meters for steam, electricity and compressed air in the production lines. / Denna rapport är resultatet av ett examensarbete utfört på Risifruttiproduktionen hos företaget Procordia Food AB. Examensarbetet är på D-nivå och är det slutgiltiga arbetet för att författaren ska få en Civilingenjörsexamen inom Innovativ produktion vid Mälardalens Högskola. Examensarbetet omfattar 30 högskolepoäng och har pågått i 18 veckor.   Examensarbetet är i huvudsak en forskning av huruvida det är möjligt att utföra en Miljövärdeflödesanalys på en livsmedelsprocessindustri samt en utvärdering av om Miljövärdeflödesanalys och Green Performance Map verktygen är användbara i denna typ av industri. En bidel till denna forskning är att med hjälp av resultaten från Miljövärdeflödesanalysen ge Procordia Food AB information om vad som bör förbättras i Risifruttiproduktionen.   Författaren har haft det svårt att samla in vissa kvantitativa data till Miljövärdeflödesanalysen, främst beträffande energiåtgång i delprocesserna, men har ändå med hjälp av en Värdeflödesanalys och andra Lean verktyg kunnat identifiera och kvantifiera flera slöserier i Risifruttiproduktionen. Rekommendationer till förbättringar baserade på dessa slöserier har givits till företaget.   I forskningsanalysen har verktygen Värdeflödesanalys, Miljövärdeflödesanalys samt Green Performance Map användbarhet i en livsmedelsprocessindustri analyserats. Författaren har funnit att Värdeflödesanalys är ett användbart verktyg då slöserier och ojämnheter i produktionstakt har kunnat identifieras. Miljövärdeflödesanalys och Green Performance Map verktygen är också bra verktyg när ett företag vill analysera sin miljöbelastning för att sedan investera i miljöförbättringar. Dock har inga djupare energianalyser kunnat göras i Risifruttiproduktionen eftersom energimätare för el, ånga och tryckluft saknades i produktionen.

Automated map generalization is a necessary technique for the construction of multi-scale vector map databases that are crucial components in spatial data infrastructure of cities, provinces, and countries. Nevertheless, this is still a dream because many algorithms for map feature generalization are not parameter-free and therefore need human’s interference. One of the major reasons is that map generalization is a process of spatial similarity transformation in multi-scale map spaces; however, no theory can be found to support such kind of transformation. This thesis focuses on the theory of spatial similarity relations in multi-scale map spaces, aiming at proposing the approaches and models that can be used to automate some relevant algorithms in map generalization. After a systematic review of existing achievements including the definitions and features of similarity in various communities, a classification system of spatial similarity relations, and the calculation models of similarity relations in the communities of psychology, computer science, music, and geography, as well as a number of raster-based approaches for calculating similarity degrees between images, the thesis achieves the following innovative contributions. First, the fundamental issues of spatial similarity relations are explored, i.e. (1) a classification system is proposed that classifies the objects processed by map generalization algorithms into ten categories; (2) the Set Theory-based definitions of similarity, spatial similarity, and spatial similarity relation in multi-scale map spaces are given; (3) mathematical language-based descriptions of the features of spatial similarity relations in multi-scale map spaces are addressed; (4) the factors that affect human’s judgments of spatial similarity relations are proposed, and their weights are also obtained by psychological experiments; and (5) a classification system for spatial similarity relations in multi-scale map spaces is proposed. Second, the models that can calculate spatial similarity degrees for the ten types of objects in multi-scale map spaces are proposed, and their validity is tested by psychological experiments. If a map (or an individual object, or an object group) and its generalized counterpart are given, the models can be used to calculate the spatial similarity degrees between them. Third, the proposed models are used to solve problems in map generalization: (1) ten formulae are constructed that can calculate spatial similarity degrees by map scale changes in map generalization; (2) an approach based on spatial similarity degree is proposed that can determine when to terminate a map generalization system or an algorithm when it is executed to generalize objects on maps, which may fully automate some relevant algorithms and therefore improve the efficiency of map generalization; and (3) an approach is proposed to calculate the distance tolerance of the Douglas-Peucker Algorithm so that the Douglas-Peucker Algorithm may become fully automatic. Nevertheless, the theory and the approaches proposed in this study possess two limitations and needs further exploration. • More experiments should be done to improve the accuracy and adaptability of the proposed models and formulae. The new experiments should select more typical maps and map objects as samples, and find more subjects with different cultural backgrounds. • Whether it is feasible to integrate the ten models/formulae for calculating spatial similarity degrees into an identical model/formula needs further investigation. In addition, it is important to find out the other algorithms, like the Douglas-Peucker Algorithm, that are not parameter-free and closely related to spatial similarity relation, and explore the approaches to calculating the parameters used in these algorithms with the help of the models and formulae proposed in this thesis.

Práce se zabývá webovými knihovnami pro práci s mapou a jejich možným využitím pro implementaci grafického uživatelského rozhraní nativní aplikace Weather Information Service (WIS) společnosti Honeywell. V práci jsou představeny prvky WIS, které je třeba implementovat pro úspěšné převedení aplikace do webového rozhraní. Dále jsou vysvětleny základy mapových knihoven a podrobněji popsány knihovny Leaflet a Altus Map Engine, které byly vybrány pro vytvoření demonstrační aplikace. Jedna kapitola je věnována metodám použitelným pro vykreslování ve webovém prostředí. Práce dále popisuje implementaci demonstrační aplikace, vytvořené za účelem prezentace možností mapových knihoven implementovat prvky WIS. Na závěr jsou diskutovány výhody a nevýhody obou zkoumaných knihoven a jejich použitelnost pro případné využití v aplikaci WIS.