Navigation systems are one of the most commonly found electronic gadgets in modern vehicles nowadays. Alongside navigation units this technology is made readily available to individuals in everyday devices such as a mobile phone. Digital maps which come preloaded on these devices accommodate within them an extensive dataset of spatial information from around the globe which aids the driver in achieving a well guided driving experience. Apart from being essential for navigation this sensor information backs up other vehicular applications in making intelligent decisions. The quality of this information delivered is in direct relation to the underlying dataset used to produce these maps. Since we live in a highly dynamic environment with constantly changing geography, an effort is necessary to keep these maps updated with the most up to date information as frequently as possible. The digital map of interest in this study is OpenStreetMap, the underlying data of which is a combination of donated as well as crowdsourced information from the last 10 years. This extensive dataset helps in building of a detailed digital map of the world using well defined cartographic techniques. The information within OpenStreetMap is currently enhanced by a large group of volunteers who willing use donated satellite imagery, uploaded GPS tracks, field surveys etc. to correct and collect necessary data for a region of interest. Though this method helps in improving and increasing the quality and quantity of the OpenStreetMap dataset, it is very time consuming and requires a great deal of human effort. Through this thesis an effort is made to automatically enrich this dataset by preprocessing crowdsourced sensor data collected from the navigation system and driver assistance systems (Traffic Sign Recognition system and a Lane Detection System) of a driving vehicle. The kind of data that is algorithmically derived includes the calculation of the curvature of the underlying road, correction of speed limit values for individual road segments being driven and the identification of change in the geometry of existing roads due to closure of old ones or addition of new ones in the Nuremberg region of Bavaria, Germany. Except for a small percentage of speed limit information on roads segments, other information is currently not available in the OpenStreetMap database for use in safety and comfort related applications. The navigation system has the ability to deliver geographical data in form of GPS coordinates at a certain frequency. This set of GPS coordinates can grouped together to form a GPS track visualizing the actual path traversed by a driving vehicle. A large number of such GPS tracks repeatedly collected from different vehicles driving in a region of interest gives all GPS points which lie on a particular road. These points, after outlier elimination methods are used as a dataset to scientifically determine the underlying curvature of the road with the aid of curve fitting techniques. Additional information received from the lane detection system helps identify curves on a road for which the curvature must be calculated. The fusion of information from these sources helps to achieve curvature results with high accuracy. Traffic sign recognition system helps detect traffic signs while driving, the fusion of this data with geographical information from the navigation system at the instance of detection helps determine road segments for which the recognized speed limit values are valid. This thesis successfully demonstrates a method to automatically enrich OpenStreetMap data by crowdsourcing raw sensor data from multiple vehicles equipped with driver assistance systems. All OpenStreetMap attributes were 100% updated into the database and the results have proven the effectiveness our system architecture. The positive results obtained in combination with minimal errors promise a better future for assisted driving.

Das Projekt OpenStreetMap als freie Wiki-Weltkarte gewinnt als Quelle von Geodaten für unter-schiedlichste Bedürfnisse innerhalb der Geowissenschaften, des Geomarketings und auch im Alltag immer mehr an Bedeutung. Die kostenlosen, von Freiwilligen einer Community gesammelten geo-graphischen Daten, sogenannte nutzergenerierte Daten, dienen heute vielen Anwendern als Daten-grundlage und stehen in der Konkurrenz zu proprietären Geodaten von kommerziellen Anbietern. Neben Straßendaten sind zahlreiche zusätzliche Daten innerhalb OpenStreetMap verfügbar. Die aktuelle Technologie des Webmapping 2.0 und die dafür zahlreich verfügbaren Open Source GIS Systeme erlauben dem Anwender eine Vielzahl von Möglichkeiten zu Bearbeitung von Geodaten. Die freie Verfügbarkeit von Daten und Software machen die Nutzung und Verarbeitung von Geoda-ten somit auch für kleinere Unternehmen und Privatnutzer bezahlbar. Die vorliegende Studienarbeit stellt ein Verfahren vor, welches es ermöglicht aus OpenStreetMap-Daten Postleitzahlgebiete zu erstellen. Postleitzahlgebiete sind für viele Bereiche der Wirtschaft sehr wichtige Planungsstrukturen. Als Datengrundlage werden OSM-Adressdaten genutzt, aus denen, unter Nutzung von Open Source GIS Software, die Postleitzahlgebiete erzeugt werden. Die Generie-rung ist dabei automatisierbar und ohne die Nutzung grafischer Benutzeroberflächen möglich. Sie liefert als Ergebnis die Postleitzahlgebiete Deutschlands. Diese werden anschließend, unter Berück-sichtigung der ISO-Normen für Geoinformation, mit einem kommerziellen Datensatz verglichen und auf ihre Nutzbarkeit für Geomarketing und andere nützliche Anwendungen hin überprüft.:Kurzfassung ........................................................................................... I Abstract ................................................................................................. II Abbildungsverzeichnis ........................................................................... V Tabellenverzeichnis .............................................................................. VI Formeln ............................................................................................... VII Abkürzungsverzeichnis ...................................................................... VIII 1 Einleitung ............................................................................................ 1 1.1 Motivation ........................................................................................ 1 1.2 Aufbau der Arbeit ............................................................................. 2 1.3 Ziel der Arbeit .................................................................................. 3 2 Theorie ............................................................................................... 4 2.1 Postleitzahlen .................................................................................. 4 2.2 Nutzung von Postleitzahlen in der Privatwirtschaft ......................... 6 2.3 Postleitzahlen in OpenStreetMap .................................................... 8 2.4 Qualität von Geodaten .................................................................. 13 2.5 Vergleich von Daten unterschiedlicher Herkunft ............................ 18 2.5.1 Qualität von OSM-Daten ............................................................. 19 2.5.2 Vergleich von OSM-Daten mit Daten kommerzieller Anbieter ...... 20 2.5.3 Vergleichsmethoden für Polygondatensätze .............................. 22 2.5.3.1 Vergleichsmethoden ................................................................ 23 2.5.3.2 Vergleichskriterien und Qualitätsmaße .................................... 24 2.6 Gebietsgenerierung aus Punktdaten ............................................. 29 2.6.1 Allgemeines und Literatur ........................................................... 29 2.6.2 Voronoi-Verfahren ...................................................................... 32 2.7 Open Source GIS Software ............................................................ 35 3 Praxis ................................................................................................ 41 3.1 Technische Voraussetzungen ........................................................ 41 3.2 Datengrundlage ............................................................................. 41 3.3 Allgemeiner Arbeitsablauf .............................................................. 43 3.3.1 Datenvorverarbeitung ................................................................ 43 3.3.2 Erzeugung der Gebiete ............................................................... 46 3.3.3 Datennachbearbeitung ............................................................... 47 3.3.4 Export der Daten als Shapefile ................................................... 47 3.4 Praktische Umsetzung ................................................................... 48 3.4.1 Datenvorverarbeitung ................................................................ 48 3.4.2 Erzeugung der Gebiete ............................................................... 50 3.4.3 Datennachbearbeitung ............................................................... 51 3.4.4 Export der Daten als Shapefile ................................................... 51 3.5 Ergebnisse ..................................................................................... 51 3.6 Vergleich der Daten ....................................................................... 52 4 Fazit .................................................................................................. 63 5 Ausblick ............................................................................................. 65 Quellenverzeichnis ............................................................................... IX Literaturquellen .................................................................................... IX Internetquellen .................................................................................... XV Anhang ............................................................................................... XIX A Anhang Quellcodes .......................................................................... XIX A.1 Quellcode Import Deutschlandgrenze .......................................... XIX A.2 Quellcode Vorverarbeitung ........................................................... XIX A.3 Quellcode Erzeugung Polygone .................................................... XXI A.4 Quellcode Nachbearbeitung ........................................................ XXII A.5 Quellcode Export ........................................................................ XXIII B Anhang Screenshots PDF-Karten .................................................... XXV B.1 Postleitzonen (PLZ1) Deutschland OSM ....................................... XXV B.2 Postleitregionen (PLZ2) Deutschland OSM ................................. XXVI B.3 Postleitzahlen 3-stellig Deutschland OSM ................................. XXVII B.4 Postleitzahlgebiete (PLZ5) Deutschland OSM .......................... XXVIII B.5 Vergleich der Postleitzahlen ....................................................... XXIX B.6 Vergleich PLZ5 Hamburg .............................................................. XXX B.7 Punktdichte PLZ5-Centroide OpenStreetMap ............................. XXXI B.8 Punktdichte PLZ5-Centroide TeleAtlas ...................................... XXXII B.9 Euklidische Distanz PLZ3-Centroide OpenStreetMap ............... XXXIII B.10 Euklidische Distanz PLZ3-Centroide TeleAtlas ........................ XXXIV B.11 Euklidische Distanz PLZ5-Centroide OpenStreetMap ............... XXXV B.12 Euklidische Distanz PLZ5-Centroide TeleAtlas ........................ XXXVI C Anhang Tabelle .......................................................................... XXXVII C.1 Tabelle Vergleich PLZ5-Gebiete Hamburg ............................... XXXVII D Anhang ............................................................................................ XLI D.1 CD ................................................................................................ XLI / The OpenStreetMap project as the Free Wiki World Map as a source of gains for a wide variety of geospatial data needs within the geosciences, the geomarketing and in everyday life is becoming increasingly important. The free, a community of volunteers gathered geo-graphical data, so-called user-generated data, now serve many users as basic data and are in competition with proprietary spatial data from commercial providers. In addition to road data within OpenStreetMap numerous additional data is available. The current technology of the Web Mapping 2.0 and the many available Open Source GIS systems provide the user with a variety of options for managing spatial data. The free availability of data and software make the use and processing of geospatial data thus affordable for small businesses and home users. The current work presents a method that allows to create postcode areas from OpenStreetMap data. Postcode areas are very important planning structures for many areas of the economy. The OSM address data are used as data base, out of which the zip code areas are produced by taking advan-tage of Open Source GIS software. The creation is automated and without the use of graphical user interfaces. It provides as result the postal code areas of Germany. Taking into account the ISO stan-dards for geoinformation, the postal code areas are later compared with a commercial data set and their usability for geomarketing and other useful application is tested.:Kurzfassung ........................................................................................... I Abstract ................................................................................................. II Abbildungsverzeichnis ........................................................................... V Tabellenverzeichnis .............................................................................. VI Formeln ............................................................................................... VII Abkürzungsverzeichnis ...................................................................... VIII 1 Einleitung ............................................................................................ 1 1.1 Motivation ........................................................................................ 1 1.2 Aufbau der Arbeit ............................................................................. 2 1.3 Ziel der Arbeit .................................................................................. 3 2 Theorie ............................................................................................... 4 2.1 Postleitzahlen .................................................................................. 4 2.2 Nutzung von Postleitzahlen in der Privatwirtschaft ......................... 6 2.3 Postleitzahlen in OpenStreetMap .................................................... 8 2.4 Qualität von Geodaten .................................................................. 13 2.5 Vergleich von Daten unterschiedlicher Herkunft ............................ 18 2.5.1 Qualität von OSM-Daten ............................................................. 19 2.5.2 Vergleich von OSM-Daten mit Daten kommerzieller Anbieter ...... 20 2.5.3 Vergleichsmethoden für Polygondatensätze .............................. 22 2.5.3.1 Vergleichsmethoden ................................................................ 23 2.5.3.2 Vergleichskriterien und Qualitätsmaße .................................... 24 2.6 Gebietsgenerierung aus Punktdaten ............................................. 29 2.6.1 Allgemeines und Literatur ........................................................... 29 2.6.2 Voronoi-Verfahren ...................................................................... 32 2.7 Open Source GIS Software ............................................................ 35 3 Praxis ................................................................................................ 41 3.1 Technische Voraussetzungen ........................................................ 41 3.2 Datengrundlage ............................................................................. 41 3.3 Allgemeiner Arbeitsablauf .............................................................. 43 3.3.1 Datenvorverarbeitung ................................................................ 43 3.3.2 Erzeugung der Gebiete ............................................................... 46 3.3.3 Datennachbearbeitung ............................................................... 47 3.3.4 Export der Daten als Shapefile ................................................... 47 3.4 Praktische Umsetzung ................................................................... 48 3.4.1 Datenvorverarbeitung ................................................................ 48 3.4.2 Erzeugung der Gebiete ............................................................... 50 3.4.3 Datennachbearbeitung ............................................................... 51 3.4.4 Export der Daten als Shapefile ................................................... 51 3.5 Ergebnisse ..................................................................................... 51 3.6 Vergleich der Daten ....................................................................... 52 4 Fazit .................................................................................................. 63 5 Ausblick ............................................................................................. 65 Quellenverzeichnis ............................................................................... IX Literaturquellen .................................................................................... IX Internetquellen .................................................................................... XV Anhang ............................................................................................... XIX A Anhang Quellcodes .......................................................................... XIX A.1 Quellcode Import Deutschlandgrenze .......................................... XIX A.2 Quellcode Vorverarbeitung ........................................................... XIX A.3 Quellcode Erzeugung Polygone .................................................... XXI A.4 Quellcode Nachbearbeitung ........................................................ XXII A.5 Quellcode Export ........................................................................ XXIII B Anhang Screenshots PDF-Karten .................................................... XXV B.1 Postleitzonen (PLZ1) Deutschland OSM ....................................... XXV B.2 Postleitregionen (PLZ2) Deutschland OSM ................................. XXVI B.3 Postleitzahlen 3-stellig Deutschland OSM ................................. XXVII B.4 Postleitzahlgebiete (PLZ5) Deutschland OSM .......................... XXVIII B.5 Vergleich der Postleitzahlen ....................................................... XXIX B.6 Vergleich PLZ5 Hamburg .............................................................. XXX B.7 Punktdichte PLZ5-Centroide OpenStreetMap ............................. XXXI B.8 Punktdichte PLZ5-Centroide TeleAtlas ...................................... XXXII B.9 Euklidische Distanz PLZ3-Centroide OpenStreetMap ............... XXXIII B.10 Euklidische Distanz PLZ3-Centroide TeleAtlas ........................ XXXIV B.11 Euklidische Distanz PLZ5-Centroide OpenStreetMap ............... XXXV B.12 Euklidische Distanz PLZ5-Centroide TeleAtlas ........................ XXXVI C Anhang Tabelle .......................................................................... XXXVII C.1 Tabelle Vergleich PLZ5-Gebiete Hamburg ............................... XXXVII D Anhang ............................................................................................ XLI D.1 CD ................................................................................................ XLI

Denna rapport handlar om sju studenters kandidatarbete som utfördes i kursen TDDD96 - Kandidatprojekt i programvaruutveckling vid Linköpings universitet under våren 2020. Målet med projektet var att utveckla en webbapplikation som visar en karta, där användaren kan styra drönare genom att specificera ett område på denna karta. Drönarna tar flygfoton över området som sedan visas på kartan. Resultatet blev en fungerande produkt för demonstrationssyften, en teknisk beskrivning av produkten och en användarmanual. Rapporten innehåller även sju individuella delar som ger en fördjupning inom olika delområden av projektet.

Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly. This thesis compares three paradigms for systems that manage vector data. Over the past few decades these methodologies have fallen in and out of favor. Today, some are considered new and experimental (distributed), others nearly forgotten (flat file), and others are the workhorse of present-day GIS (relational database). Each is well-suited to some use cases, and poorly-suited to others. This thesis investigates exemplars of each paradigm.

Procedural generation of urban environments is a very difficult problem to solve. Most solutions use predefined production rules which lock them into only few different variations of the result. This works well when producing new urban environments but fails when it comes to the expansion of them. Most cities are too complex to model using an approach which utilises predefined rules. By using an example based approach instead, it is possible to expand any city and still have the new street network follow the layout of the original city, regardless of complexity. This paper describes a method of extracting the necessary information from the GIS database OpenStreetMap and expanding the cities using an example based approach presented by Aliaga et al. The paper will also show how blocks, parcels and buildings can be generated to fit within the urban environment.

This thesis deals with the procedural generation of building models based on a given pattern. The community project OpenStreetMap is used for obtaining datasets that create the buildings platform patterns. A brief survey of classifiers and formal grammars for modeling is introduced. Designing an estate classifier and algorithm for building generation is practical aspect of this thesis, including the algorithm implementation. 3D output meshes are rendered using OpenGL in real-time.

This thesis is carried out in the context of Advanced Driver Assistance Systems, and especially autonomous vehicles. Its aim is to propose a method to enhance localization of vehicles on roads. I suggests using a camera to detect lane markings, and to match these to a map to extract the corrected position of the vehicle.The thesis is divided in three parts dealing with: the map, the line detector and the evaluation. The map is based on the OpenStreetMap data. The line detector is a based on ridge detection. The results are compared with an Iterative Closest Point algorithm. It also focuses on implementing the components under a real-time constraint. Technologies such as ROS, for synchronization of the data, and CUDA, for parallelization, are used.

No description available.

Die Region Mestia liegt abgeschieden im Norden Georgiens im Großen Kaukasus. Markant für die Region Mestia ist der Doppelgipfel der Ushba. Dieser zieht Touristen an, welche Wandertouren und Erkundungen durchführen möchten. Um diese sicher gewährleisten zu können, ist eine Alpenvereinskarte dringend erforderlich. In dieser Arbeit wird eine Generalisierung des Gebäudedatenbestand der Region Mestia mit Hilfe der Geoinformationssoftware ArcGIS Pro für eine ebensolche Alpenvereinskarte erstellt. Als Ausgangsdaten werden OpenStreetMap-Datensätze verwendet. Es werden die für den Zweck dieser Karte relevanten Daten herausgefiltert und alle unwichtigen Elemente entfernt. Zudem werden alle Stützpunkte der Gebäude gebildet und deren Abstand zueinander mittels eines dichte basierten Clustering bestimmt. Im Anschluss werden die Stützpunkte den Gebäude-Polygonen zugewiesen. Anhand der Abstandsbestimmung kann im Folgenden eine Unterscheidung der Gebäude in zwei Kategorien vorgenommen werden. Einzelnstehende Gebäude bzw. Gebäude mit einer sehr geringen Anzahl an Gebäuden in der unmittelbaren Nachbarschaft werden der Kategorie „Einzelgebäude“ zugeordnet. Alle anderen Gebäude werden in die Kategorie „Siedlung“ eingegliedert, da diese Gebäude sehr zahlreich und dicht beieinanderstehen. Die beiden Kategorien werden unterschiedlich generalisiert. Die Gebäude, die der Kategorie „Einzelgebäude“ zugeordnet sind, werden vereinfacht und als Punktsignaturen dargestellt. Alle anderen Gebäude werden aggregiert und Polygone mit einem geringen Flächeninhalt eliminiert. Zudem werden die Außenkanten der Polygone geglättet, damit ein vereinfachter Grundriss der Gebäude vorliegt. In einer Evaluierung wird die erstellte Generalisierung des Gebäudebestandes mit einer weiteren Gebäudegeneralisierung, welche mit dem Programm QGIS erstellt ist, gegenübergestellt. Den Expert*innen werden zudem Bilder von den Gebäuden der Region und zwei Beispielkarten, auf denen die generalisierten Gebäude dargestellt sind, gezeigt. Es ist zu entscheiden, welche Karte die Gebäude auf dem Bild abbildet. In dieser Arbeit wird die unterschiedliche Generalisierung der Einzelgebäude und der Siedlungen betrachtet. Die Unterschiede dieser beiden Kategorien wird herausgearbeitet. Mittels der Evaluierung wird das Ergebnis von Expert*innen überprüft. / The Mestia region is in the north of Georgia in the Great Caucasus. A distinctive feature of the Mestia region is the double peak of the Ushba. It attracts tourists who want to go hiking and exploring. To be able to guarantee this safely, an alpine association map is urgently needed. In this work, a generalisation of the building data stock of the Mestia region is created with the help of the geoinformation software ArcGIS Pro for a similar alpine association map. OpenStreetMap datasets are used as source data. The data relevant for the purpose of this map are filtered out and all unimportant elements are removed. In addition, all support points of the buildings are formed and their distance to each other is determined using density based clustering. Subsequently, the support points are assigned to the building polygons. Based on the distance determination, the buildings can be divided into two categories. Single buildings or buildings with a very small number of buildings in the immediate vicinity are assigned to the category 'single building'. All other buildings are placed in the category 'Settlement', as these buildings are very numerous and close together. The two categories are generalised differently. The buildings assigned to the category 'single building' are simplified and represented as point signatures. All other buildings are aggregated and polygons with a small area are eliminated. In addition, the outer edges of the polygons are smoothed so that a simplified ground plan of the buildings is available. In an evaluation, the created generalisation of the building stock is compared with another building generalisation created with the QGIS programme. The experts are also shown pictures of the buildings in the region and two sample maps showing the generalised buildings. It must be decided which map depicts the buildings on the picture. In this paper, the different generalisation of individual buildings and settlements is considered. The differences between these two categories will be worked out. By means of evaluation, the result is checked by experts.

Street names (odonyms) play an important role not only as descriptors of geographic locations but also due to their sociological and political connotations and commemorative character. Here we analyse street names in Europe and North America extracted from OpenStreetMap, asking in particular to what extent odonyms reflect early European settlements in the New World, i.e., the immigration of German, Austrian and Scandinavian minorities. We observe that old street names of European origin can predominantly be found in rural areas. North American street names indeed recapitulate local and regional settlement histories. The aim of this study is to demonstrate that easily accessible data sets from freely available map data such as street names convey usable information concerning migration patterns and the history of settlements in the case of European immigrants in North America as well as colonial history. We provide a freely available pipeline to analyse this kind of data.