Philipp Meixner

3-Dimensional Building Details from Aerial Photography for Internet Maps

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades.

Automated photogrammetry for three-dimensional models of urban spaces

The location-aware Internet is inspiring intensive work addressing the automated assembly of three-dimensional models of urban spaces with their buildings, circulation spaces, vegetation, signs, even their above-ground and underground utility lines. Two-dimensional geographic information systems (GISs) and municipal utility information exist and can serve to guide the creation of models being built with aerial, sometimes satellite imagery, streetside images, indoor imaging, and alternatively with light detection and ranging systems (LiDARs) carried on airplanes, cars, or mounted on tripods. We review the results of current research to automate the information extraction from sensor data. We show that aerial photography at ground sampling distances (GSD) of 1 to 10 cm is well suited to provide geometry data about building facades and roofs, that streetside imagery at 0.5 to 2 cm is particularly interesting when it is collected within community photo collections (CPCs) by the general public, and that the transition to digital imaging has opened the no-cost option of highly overlapping images in support of a more complete and thus more economical automation. LiDAR-systems are a widely used source of three-dimensional data, but they deliver information not really superior to digital photography.

3D roof details by 3D aerial vision

Virtual cities in a 3D GIS will not be complete without the details of roofs with chimneys, dormer windows, skylights. We contribute a method of extracting such details from digital large format aerial photography. The dense point clouds produced by a photogrammetric process produce major and minor plane surfaces of a roof and enter into a classification of its superstructures. In real city data with a total of 1312 superstructures the proposed automated process finds 1024, thus achieving success at a rate of 78%.

Planar roof surface segmentation using 3D vision

Internet search has initially been a strong driving force for the rapid emergence of 3D building models of large urban areas. Additionally, many commercial and governmental initiatives have been started to develop urban 3D geographic information systems in a transition from the classical 2D- to the novel 3D-GIS. The modeling of building roofs is thus a relevant research topic. The focus has been on the use of aerial LiDAR point clouds (Light Detection And Ranging). However, recent progress in digital aerial cameras has rendered possible the acquisition of very dense point clouds from high overlap digital aerial imagery, and to use these point clouds jointly with the image information to generate 3D building models. This paper presents a multi-step processing framework and work flow for the automatic segmentation of building roofs in densely built-up areas from high-resolution vertical aerial images. Details extruding from, or intruding into, a roof are being excluded so that each roof is being modeled by means of its planar segments and can then be classified as a specific roof type from a set of standard roof shapes. Our experimental work employs a test area in Graz (Austria) with 186 buildings.