Automatic coregistration of three-dimensional building models with image features

The aim of this article is to investigate methods for\ud the automatic extraction of the infrared (IR) textures\ud for the roofs and facades of existing building models.\ud We focus on the correction of the measured exterior\ud orientation parameters of the IR camera mounted on\ud a mobile platform. The developed method is based on\ud point-to-point matching of the features extracted from\ud IR images with a wire-frame building model. Firstly,\ud the extraction of different feature types is studied on\ud a sample IR image; Förstner and intersection points\ud are chosen for a representation of the image features.\ud Secondly, the three-dimensional (3D) building model\ud is projected into each frame of the IR video sequence\ud using orientation parameters; only coarse exterior\ud orientation parameters are known. Then the automatic\ud co-registration of a 3D building model projection into\ud the image sequence with image features is carried out.\ud The matching of a model and extracted features is\ud applied iteratively, and exterior orientation parameters\ud are adjusted with least square adjustment. The method\ud is tested on a dataset of a dense urban area. Finally,\ud an evaluation of the developed method is presented\ud with five quality parameters, i.e. efficiency of the\ud method, completeness and correctness of matching\ud and extraction

Die Luftwaffe im Kampf um die Luftherrschaft : Entscheidende Einflussgrößen bei der Niederlage der Luftwaffe im Abwehrkampf im Westen und über Deutschland im Zweiten Weltkrieg unter besonderer Berücksichtigung der Faktoren „Luftrüstung“, „Forschung und Entwicklung“ und „Human Ressourcen“

Eine Analyse der strategischen Bedeutung der Faktoren Luftrüstung, Forschung und Entwicklung und des Faktors "Mensch" zur Erklärung der Niederlage der Luftwaffe im Kampf mit den West-Alliierten

Combining visibility analysis and deep learning for refinement of semantic 3D building models by conflict classification

Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms

Persistent scatterer aided facade lattice extraction in single airborne optical oblique images

We present a new method to extract patterns of regular facade structures from single optical oblique images. To overcome the missing three-dimensional information we incorporate structural information derived from Persistent Scatter (PS) point cloud data into our method. Single oblique images and PS point clouds have never been combined before and offer promising insights into the compatibility of remotely sensed data of different kinds. Even though the appearance of facades is significantly different, many characteristics of the prominent patterns can be seen in both types of data and can be transferred across the sensor domains. To justify the extraction based on regular facade patterns we show that regular facades appear rather often in typical airborne oblique imagery of urban scenes. The extraction of regular patterns is based on well established tools like cross correlation and is extended by incorporating a module for estimating a window lattice model using a genetic algorithm. Among others the results of our approach can be used to derive a deeper understanding of the emergence of Persistent Scatterers and their fusion with optical imagery. To demonstrate the applicability of the approach we present a concept for data fusion aiming at facade lattices extraction in PS and optical data