Open Access

Das Ziel dieser Arbeit ist die Erstellung einer Übung über das Thema „Streifenprojektion“ zur dreidimensionalen Rekonstruktion. Diese soll künftig in die Vorlesungen an der Hochschule Würzburg-Schweinfurt integriert werden. Dafür wurde die für die Hochschule bis dato unbekannte Software „DAVID-Laserscanner“ erworben, welche auf einem Prinzip der Streifenprojektion basiert, das in den Vorlesungen bisher noch nicht erwähnt wurde. Bei diesem Prinzip werden Oberflächen mit Hilfe einer Kamera und eines Linienlasers erfasst. Dabei können Objekte einer Größe von 10cm – 2m berührungslos dreidimensional erfasst und rekonstruiert werden. Es musste zunächst getestet werden, ob sich die Software für den Einsatz in der Lehre eignet. Dafür wurde sich sowohl mit den technischen Hintergründen (dem Streifenprojektionssystem von DAVID- Laserscanner), als auch mit der Funktionsweise derselben beschäftigt. Um selbst Erfahrung im Umgang mit der Software zu erwerben, wurde die Rekonstruktion mit DAVID an zwei unterschiedlichen Objekten durchgeführt. Diese sollten möglichst stark differieren, um in Anbetracht der Übung mögliche Unterschiede oder Komplikationen bei der Arbeit mit DAVID festzustellen. Dabei wurden sämtliche Features und Möglichkeiten der Software getestet, ebenso wie unterschiedliche Versuchsaufbauten und Umgebungsbedingungen. Auf Basis dieses Wissens folgte die Erstellung der Übung. Dabei war vor allen Dingen wichtig, dass die Aufgabenstellung keinen mehrseitigen „Fahrplan“ mit sämtlichen Features, Buttons und Funktionen der Software darstellt, sondern die Studenten zur aktiven Teilnahme am Erreichen des Übungszieles anregt. Deswegen beschränkt sich die Aufgabenstellung der Übung nur auf die wichtigsten Informationen, sämtliche weitere Hilfestellungen werden in unterschiedlichen Hilfsmitteln bereitgestellt. Diese sind zum Beispiel die Bedienungsanleitung, sowie die Homepage der Software. Ferner erfolgte die Erstellung weiterer Zusatzinformationen, welche in der Softwarebeschreibung nicht vorkommen, aber für den erfolgreichen Abschluss einer Rekonstruktion mit DAVID als wichtig erachtet wurden.

We present an automated approach for the dodging of images, with which we edit digital images as it is usually done with analogue images in dark-rooms. Millions of aerial images of all battle fields were taken during the Second World War. They were intensively used, e.g. for the observation of military movements, the documentation of success and failure of military operations and further planning. Today, the information of these images supports the removal of explosives of the Second World War and the identi- fication of dangerous waste in the soil. In North Rhine-Westphalia, approximately 300.000 aerial images are scanned to handle the huge amount of available data efficiently. The scanning is done with a gray value depth of 12 bits and a pixel size of 21 μm to gain both, a high radiometric and a high geometric resolution of the images. Due to the photographic process used in the 1930s and 1940s and several reproductions, the digitized images are exposed locally very differently. Therefore, the images shall be improved by automated dodging. Global approaches mostly returned unsatisfying results. Therefore, we present a new approach, which is based on local histogram equalization. Other methods as spreading the histogram or linear transformations of the histogram manipulate the images either too much or not enough. For the implementation of our approach, we focus not only on the quality of the resulting images, but also on robustness and performance of the algorithm. Thus, the technique can also be used for other applications concerning image improvements.

In this paper we describe a new concept for the reconstruction of buildings. In contrast to most of the published approaches, we link the reconstruction process with the building interpretation. With this linkage we want to enhance the reconstruction result and to yield semantic information about the buildings. We introduce building models based on their topology. We also may use data from different sensor types. The analysis is done locally using statistical building information for the interpretation in a Markov-Random-Field and using e. g. geometric or radiometric “appearance” models for the reconstruction. A real data example from laserscanner observations demonstrates the approach.

In various projects we investigate on the extraction of buildings on di erent type and representation of data. This paper presents a strategy for 3D building acquisition which combines different approaches based on di erent levels of description. The approach consists of detectionofregions of interest and automatic and semiautomatic reconstruction of object parts and complete buildings. We incorporate the approach in a global concept of interaction between scene and sensors for image interpretation.

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.

Automatic construction progress documentation and metric evaluation of execution work in confined building interiors requires particularly reliable geometric evaluation and interpretation of statistically uncertain as-built point clouds. This paper presents a method for high-resolution change detection based on dense 3D point clouds from terrestrial laser scanning (TLS) and the discretization of space by voxels. In order to evaluate the metric accuracy of a BIM according to the Level of Accuracy (LOA) specification, the effects of laser range measurements on the occupancy of space are modeled with belief functions and evaluated using Dempster and Shafer's theory of evidence. The application is demonstrated on the point cloud data of multi temporal scanning campaigns of real indoor reconstructions. The results show that TLS point clouds are suitable to verify a given BIM up to LOA 40 if special attention is paid to the scanning geometry during the acquisition. The proposed method can be used to document construction progress, verify and even update the LOA status of a given BIM, confirming valid and BIM-compliant as-built models for further planning.

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually. This paper presents a novel method for the detection of road defects. The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology. The defects were categorized in three major groups with the following geometric primitives: points, lines and polygons. The method suggests the detection of point objects from matched point clouds, panoramic images and ortho photos. Defects were mapped as point, line or polygon geometries, directly derived from orthomosaics and panoramic images. Besides the geometric position of road defects, all objects were assigned to a variety of attributes: defect type, surface material, center-of-gravity, area, length, corresponding image of the defect and degree of damage. A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly. The final product is a spatial vector data set, consisting of points, lines and polygons, which contains attributes with further information and geometry. This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects. By its simplicity and flexibility, the presented methodology allows it to be easily adapted to extract further feature types with their attributes. This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts, e.g., offline crowdsourcing.