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A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.2 of 20 building shapes, including the recovery of disturbed boundaries and robust reconstruction of precise rooftop geometries, is in demand.Remote sensing technology provides several ways to measure the 3D urban morphology. Conventional ground surveying, stereo airborne or satellite photogrammetry, interferometric synthetic aperture radar (InSAR), and light detection and ranging (LIDAR) are the main data sources used to obtain high-resolution elevation information [1]. The main advantage of digital surface models (DSMs) generated using ground surveying and LIDAR is their good quality and detailed object representations. However, their production is costly and time consuming, and covers relatively small areas compared with images produced with spaceborne remote sensing [2]. SAR imagery is operational in all seasons under different weather conditions. Nevertheless it has a side-looking sensor principle that is not so useful for building recognition a...

ABSTRACT:Digital surface models can be efficiently generated with automatic image matching from optical stereo images. The Working Group 4 of Commission I on "Geometric and Radiometric Modelling of Optical Spaceborne Sensors" provides a matching benchmark dataset with several stereo data sets from high and very high resolution space borne stereo sensors at http://www.commission1.isprs.org/wg4/. The selected regions are in Catalonia, Spain, and include three test areas, covering city areas, rural areas and forests in flat and medium undulated terrain as well as steep mountainous terrain. In this paper, digital surface models (DSM) are derived from the Cartosat-1 and Worldview-1 datasets using Semiglobal Matching. The resulting DSM are evaluated against the first pulse returns of the LIDAR reference dataset provided by the Institut Cartogràfic de Catalunya (ICC), using robust accuracy measures.

Building models are a valuable information source for urban studies and in particular for analyses of urban mass concentrations (UMCS). Most commonly, light detection and ranging (LiDAR) is used for their generation. The trade-off for the high geometric detail of these data is the low spatial coverage, comparably high costs and low actualization rates. Spaceborne stereo data from Cartosat-1 are able to cover large areas on the one hand, but hold a lower geometric resolution on the other hand. In this paper, we investigate to which extent the geometric shortcomings of Cartosat-1 can be overcome integrating building footprints from topographic maps for the derivation of large-area building models. Therefore, we describe the methodology to derive digital surface models (DSMs) from Cartosat-1 data and the derivation of building footprints from topographic maps at 1:25 000 (DTK-25). Both data are fused to generate building block models for four metropolitan regions in Germany with an area of ∼ 16 000 km 2 . Building block models are further aggregated to 1 × 1 km grid cells and volume densities are computed. Volume densities are classified to various levels of UMCs. Performance evaluation of the building block models reveals that the building footprints are larger in the DTK-25, and building heights are lower with a mean absolute error of 3.21 m. Both factors influence the building volume, which is linearly lower than the reference. However, this error does not affect the classification of UMC, which can be classified with accuracies between 77% and 97%.

Here we present the operational, fully automatic processing system CATENA developed at DLR. The uniform pre-processing of an increasing amount of satellite data for generation of whole coverages of e.g. Europe for one time or of time-series for one location covering many years is requested more and more. Such requirements contain the processing of huge amounts of data which can hardly be handled manually. So a fully automatic pre-processing environment was developed at the Remote Sensing Technology Institute of DLR in Oberpfaffenhofen since 2006. This processing environment named CATENA was designed for uniform, automatic general purpose processing of huge amounts of optical satellite data of similar type. In this paper we present the concept of the processing system, the framework and the decomposition of processing requirements to processing modules and processing chains. We give some examples for already implemented general purpose or project specific processing chains and an analysis of performance and quality of the results.