ABSTRACT:The importance of single trees and the determination of related parameters has been recognized in recent years, e.g. for forest inventories or management. For urban areas an increasing interest in the data acquisition of trees can be observed concerning aspects like urban climate, CO 2 balance, and environmental protection. Urban trees differ significantly from natural systems with regard to the site conditions (e.g. technogenic soils, contaminants, lower groundwater level, regular disturbance), climate (increased temperature, reduced humidity) and species composition and arrangement (habitus and health status) and therefore allometric relations cannot be transferred from natural sites to urban areas. To overcome this problem an extended approach was developed for a fast and non-destructive extraction of branch volume, DBH (diameter at breast height) and height of single trees from point clouds of terrestrial laser scanning (TLS). For data acquisition, the trees were scanned with highest scan resolution from several (up to five) positions located around the tree. The resulting point clouds (20 to 60 million points) are analysed with an algorithm based on voxel (volume elements) structure, leading to an appropriate data reduction. In a first step, two kinds of noise reduction are carried out: the elimination of isolated voxels as well as voxels with marginal point density. To obtain correct volume estimates, the voxels inside the stem and branches (interior voxels) where voxels contain no laser points must be regarded. For this filling process, an easy and robust approach was developed based on a layer-wise (horizontal layers of the voxel structure) intersection of four orthogonal viewing directions. However, this procedure also generates several erroneous "phantom" voxels, which have to be eliminated. For this purpose the previous approach was extended by a special region growing algorithm. In a final step the volume is determined layer-wise based on the extracted branch areas A i of this horizontal cross-section multiplied by the thickness of the voxel layer. A significant improvement of this method could be obtained by a reasonable determination of the threshold for excluding sparsely filled voxels for noise reduction which can be defined based on the function change of filled voxels. Field measurements were used to validate this method. For a quality assessment nine deciduous trees were selected for control and were scanned before felling and weighing. The results are in good accordance to the control trees within a range of only -5.1% to +14.3%. The determined DBH values show only minor deviations, while the heights of trees are systematically underestimated, mainly due to field measurements. Possible error sources including gaps in surface voxels, influence of thin twigs and others are discussed in detail and several improvements of this approach are suggested. The advantages of the algorithm are the robustness and simple structure as well as the quality of the results obtained. The drawbacks are t...
The aim of this article is to present a method to calculate the morphological properties of the built environment using LiDAR (light detection and ranging) data, geographic information systems (GIS) data and three-dimensional (3D) models of cities as a source of information. A hybrid approach that takes into account different types of inputs and consequently evaluates the accuracy of each type of used data is presented. This work is intended to give a first response to the lack of a comprehensive and accurate procedure that uses LiDAR data in order to automatically derive precise morphological properties, such as volumes and surfaces (façades and roofs) of buildings. The method was tested on two case-study areas in the Geneva region with different characteristics, one in the old town along the Rhone River and the other on the CERN campus. A statistical analysis that compares the results of the computation with the 3D model of the built environment was used to validate the results, complemented by significance statistical tests. Outcomes showed that the proposed method to derive morphological properties can reach high levels of accuracy, thus enhancing the potential uses of LiDAR data for numerous applications, typically for the assessment of the urban environmental quality (UEQ) at the city and
Due to the increasing scarcity of fossil fuels and the upwards trend in energy costs over time, many countries-especially in Europe-have begun to modify their energy policies aiming to increase that percentage obtained from renewable energies. The EAGLE (FP7 program, European Commission) has developed a web-based platform to promote renewable energy systems (RES) in the public and private sectors, and to deliver a comprehensive information source for all interested users. In this paper, a comprehensive quality assessment of extracted roof planes suitable for solar energy installations (photovoltaic, solar thermal) from height data derived automatically from both LiDAR (Light Detection and Ranging) and aerial images will be presented. A shadow analysis is performed regarding the daily path of the sun including the shading effects of nearby objects (chimneys, dormers, vegetation, buildings, topography, etc.). A quality assessment was carried out for both LiDAR and aerial images of the same test sites in UK and Germany concerning building outline accuracy, extraction rate of roof planes and the accuracy of their geometric parameters (inclination and aspect angle, size). The benefit is an optimized system to extract roof planes for RES with a high level of detail, accuracy and flexibility (concerning different commonly available data sources) including an estimation of quality of the results which is important for individual house owners as well as for regional applications by governments or solar energy companies to judge their usefulness.
<p><strong>Abstract.</strong> Today, automatic image analysis is one of the basic approaches in the field of industrial applications. One of frequent tasks is pose estimation of objects which can be solved by different methods of image analysis. For comparison two of them have been selected and investigated in this project: Convolutional Neural Networks (CNNs) and a classical method of image analysis based on contour extraction. The main point of interest was to investigate the potential and limits of CNNs to fulfil the requirements of this special task regarding accuracy, reliability and time performance. The classical approach served as comparison to a state-of-the-art solution. The workpiece for these investigations was a commonly used transistor element. As database an image archive consisting of 9000 images with different illumination and perspective conditions has been generated. One part was used for training of the CNN and the creation of a so-called shape model respectively, the rest for the investigation of the extraction quality. With CNN technique two different approaches have been realised. Even if CNNs are predestined for classification this method delivered insufficient results. In a more sophisticated approach the system learns the parameters of an affine transformation including the sought-after parameters of translation and rotation. Our experiments confirm that CNNs are able to obtain at best only a medium accuracy of rotation angles (about &plusmn;<span class="thinspace"></span>2&deg;), in contrast to the classical approach (about &plusmn;<span class="thinspace"></span>0.5&deg;). Concerning the determination of translations both methods deliver comparable results, about ±<span class="thinspace"></span>0.5<span class="thinspace"></span>pixel from CNN and about &plusmn;<span class="thinspace"></span>0.4<span class="thinspace"></span>pixel from classical approach.</p>
ABSTRACT:This paper shows several working steps for updating the \Digitale Landschaftsmodell 200 (DLM 200)\ using satellite images. It is based on a two-step approach: verication and classication. First the existing semantic model (DLM 200) is used for the knowlegde based object oriented analysis of the satellite images. At the second stage the information gained from the rst step serves to prove and update the DLM 200. Since the DLM 200 is produced by digitizing the map layers of the \Topographische Ubersichtskarte (T UK 200)\, typical cartographic aspects (e.g. generalisation, displacement) have to be considered. Some examples illustrating these eects on a representative class (e.g. forest) of the DLM 200 will be shown. After the determination of these geometric relations between the DLM 200 and the images (i.e. a rectication of the satellite images) the \knowlegde\, based on the DLM 200, will back up the object based analysis of the satellite images. Image areas which do not t the DLM 200 will be examined at the second stage. The classication -a minor topic in this presentation -has to assign the changes detected in the course of the verication to appropriate classes of the DLM 200. This process will use the parameters of the image analysis as additional information.KURZFASSUNG:
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