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Institute
SketchUP
(2013)
Untersuchungen der Variation der Parameter der inneren Orientierung von NIKON Spiegelreflexkameras
(2012)
Forest classification is needed to solve a wide range of environmental issues related to of forest classes and succession processes, the extent of afforestation and deforestation and global environmental change. These applications require a very accurate mapping and monitoring of forest types. This article investigates the combination of modern open geographic information systems and remote sensing data in forest management tasks for a specific part of the Ukrainian state area. Based on the existing afforestation plans, the results of the unsupervised classification of Sentinel-2 images and the selection of forest species fragments with closed crowns as training data for supervised classification, classifiers of forest species of the study object were developed with and without taking into account age groups. A supervised classification of research objects is realized and the accuracy of the obtained results is evaluated. It is established that the accuracy of determining forest species on the basis of the proposed method is 90.3 and 91.4%, taking into account age groups and without taking them into account, respectively. Thus, it is found that the modeling of the age groups does not improve the classification result for the test area.
3D-Vermessung der Zoolithenhöhle in der Fränkischen Schweiz - Eine Tropfsteinhöhle virtuell in 3D
(2014)
The cultural human heritage is important for the identity of following generations and has to be preserved in a suitable manner. In the course of time a lot of information about former cultural constructions has been lost because some objects were strongly damaged by natural erosion or on account of human work or were even destroyed. It is important to capture still available building parts of former buildings, mostly ruins. This data could be the basis for a virtual reconstruction. Laserscanning offers in principle the possibility to take up extensively surfaces of buildings in its actual status.
In this paper we assume a priori given 3d-laserscanner data, 3d point cloud for the partly destroyed church. There are many well known algorithms, that describe different methods of extraction and detection of geometric primitives, which are recognized separately in 3d points clouds. In our work we put them in a common probabilistic framework, which guides the complete reconstruction process of complex buildings, in our case russian-orthodox churches.
Churches are modeled with their functional volumetric components, enriched with a priori known probabilities, which are deduced from a database of russian-orthodox churches. Each set of components represents a complete church. The power of the new method is shown for a simulated dataset of 100 russian-orthodox churches.
Besides the common approach of an exclusively file based management of 3D point clouds, meanwhile it is possible to store and process this special type of massive geodata within spatial database systems. Users benefit from the general advantages of database solutions and especially from the potentials of a combined analysis of original 3D point clouds, 2D rasters, 3D voxel stacks and 2D and 3D vector data in order to gain valuable geo- information. This paper describes the integration of 3D point clouds into an open source PostgreSQL/PostGIS database using the Pointcloud extension and functions of the Point Data Abstraction Library (PDAL). The
focus is on performing three-dimensional spatial queries and the evaluation of different tiling methods for the organization of 3D point clouds into table rows, regarding memory space, performance of spatial queries and effects on interactions between point clouds and other GIS features within the database. A new approach for an optimized point cloud tiling, considering the individual geometric characteristic of a 3D point cloud, is presented. The results show that an individually selected storage structure for a point cloud is crucial for low memory consumption and high-performance 3D queries in PostGIS applications, taking account of its three-dimensional spatial extent and point density.
Land use in the Volyn region (Ukraine) has changed a lot in the last decade. Change detection basing on satellite images is an appropriate method to show the migration of land use. In this article, a classification of the land use on free satellite is implemented. The procedure should be as automatic as possible. For the optimum accuracy different band combinations and the NDVI are tested. Different methods were compared due to the achieved accuracy and the status of automation.
Hochschulen für angewandte Wissenschaften entwickeln beständig die Lehre nicht nur für die eigenen Studierenden, sondern auch die Methoden für den Wissenstransfer in die Unternehmen weiter. Neue didaktische Konzepte werden für anstehende Fachthemen ausprobiert. Im Rahmen des Projekts „Frühe Digitalisierung technischer Planungsprozesse im Bauwesen und Handwerk für kleine und mittlere Unternehmen“ (DiTeP4KMU) sollen daher aktuelle Methoden der Wissensvermittlung (Online Lehrangebote, Blended Learning sowie hybride Lehrveranstaltungsformen) in die berufsbegleitende Weiterbildung übertragen und realisiert werden. Das Projekt ist eine Fortsetzung und Erweiterung der bereits an der Hochschule für angewandte Wissenschaften Würzburg-Schweinfurt umgesetzten Projekte „Digitale Photogrammetrie und Bildverarbeitung für kleine und mittlere Unternehmen (DiPhoBi4KMU)“ und „Mobile 3D Vermessung für kleine und mittlere Unternehmen“ (MV-3D4KMU). Ziel des aktuellen Projekts ist die Prozessoptimierung in KMU durch mobile 3D-Vermessung anhand hybrider Wissensvermittlung in Form von E-Learning Angeboten sowie Präsenzveranstaltungen in Form von Netzwerktreffen und Aktionen. Der fachliche Fokus liegt dabei auf der stärkeren Verbreitung der 3D-Daten für alltägliche Aufgaben - insbesondere der schnell zu generierenden 3D-Punktwolken durch moderne Messsysteme - in die Arbeitsprozesse im Bauwesen und Handwerk, in Zusammenarbeit mit den beteiligten Unternehmen. Die Erfahrungen in dem Projekt zeigen bereits nach ca. sechs Monaten ein großes Interesse und bestätigen die Konzeption des Projektentwurfs.
Dieser Beitrag handelt von einer Methode zur Änderungsdetektion im Gebäudeinneren, die auf der Diskretisierung des Objektraums durch Voxel basiert. Die Belegung des Voxelraums wird anhand von Punktwolken aus terrestrischem Laserscanning zu verschiedenen Bauphasen ermittelt. Durch die Aufdeckung von Belegungskonflikten zwischen zwei Messepochen wird ermittelt, ob Änderungen an einer bestimmten Stelle aufgetreten sind. Es
wird gezeigt, wie sich der Diskretisierungsfehler als Resultat der Voxelisierung auf die noch erreichbare räumliche Auflösung einer Änderungsaussage auswirkt. Am Beispiel von Abbrucharbeiten bei einem Innenumbauprojekt werden die wechselseitigen Auswirkungen von Aufnahmegeometrie, Scanauflösung und Voxelgröße diskutiert. Die Ergebnisse zeigen, dass die Auflösung von 3D-Punktwolken unter Beachtung einiger Anforderungen an die Aufnahmegeometrie bei Innenraum-Messungen ausreicht, um bei gängigen Genauigkeitsanforderungen auf Voxelebene automatisiert strukturelle Änderungen zu beschreiben.
Rapid and uncontrolled expansion of cities has led to unplanned aerial development. In this way, modeling and predicting the urban growth of a city helps decision-makers. In this study, the aspect of sustainable urban development has been studied for the city of Mashhad. In general, the prediction of urban aerial
development is one of the most important topics of modern town management. In this research, using the Cellular Automaton (CA) model and perceptron neural network method with satellite data developed for geo data of Geographic Information Systems (GIS) and presenting a simple and powerful model, a simulation of complex urban processes has been done. In finally our accuracy has been better compared to other researches that have been done in this field and we have slightly improved and optimize the final results approximately %92.1.
Mapping of parking spaces in cities is a prerequisite for future applications in parking space management like community-based parking. Although terrestrial or vehicle based sensors will be the favorite data source for parking space mapping, airborne monitoring can play a role in building up city wide basis maps which include also parking spaces on ancillary and suburban roads. We present a novel framework for automatic city wide classification of vehicles in moving, stopped and parked using aerial image sequences and information from a road database. The time span of observation of a specific vehicle during an image sequence is usually not long enough to decide unambiguously, whether a vehicle stopped e.g. before a traffic light or is parking along the road. Thus, the workflow includes a vehicle detection and tracking method as well as a rule-based fuzzy-logic workflow for the classification of vehicles. The workflow classifies stopped and parked vehicles by including the neighbourhood of each vehicle via a Delaunay-Graph. The presented method reaches correctness values of around 86.3%, which is demonstrated using three different aerial image sequences. The results depend on several factors like detection quality and road database accuracy.