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The world-wide absolute geographic positioning accuracy of optical Satellite imagery is mostly about a few pixels of the image resolution. So for example WorldView-3 images have a CE90 of about 4 m. Also the direct georeferencing without ground control information of aerial imagery is in the same range of one to a few metres. These inaccuracies originate predominantly in uncertainties of angular measurements for the sensor attitude. An angular error of only one arc-second at a satellite 750 km above ground results in an absolute error on ground of 3.6 metres. On the other hand radar satellites like TerraSAR-X or TanDEM-X do not measure angles but signal runtimes. So if we identify the same point in an optical image and in a radar image we can solve the problem of inaccurate angle-measurements in the optical sensor models and are able to georeference optical images world wide absolute to below one pixel. In this paper we present a method for identification of point-objects which can be detected in both types of images: the footpoints of poles. If such a footpoint of a pole can be detected simultaneously in both types of images the geoposition of the optical image can be corrected to the accuracy of the point-measurement in the radar image. To achieve a high accuracy also a nearly perfect correction of all errors in signal propagation times of the radar signals has to be conducted. In this paper we describe how the footpoints of poles will be extracted in optical spaceborne or air-borne imagery and how these footpoints are correlated to the potential footpoints of poles detected in the radar imagery.

In Earth observation, multimodal data fusion is an intuitive strategy to break the limitation of individual data. Complementary physical contents of data sources allow comprehensive and precise information retrieval. With current satellite missions, such as ESA Copernicus programme, various data will be accessible at an affordable cost. Future applications will have many options for data sources. Such a privilege can be beneficial only if algorithms are ready to work with various data sources. However, current data fusion studies mostly focus on the fusion of two data sources. There are two reasons; first, different combinations of data sources face different scientific challenges. For example, the fusion of synthetic aperture radar (SAR) data and optical images needs to handle the geometric difference, while the fusion of hyperspectral and multispectral images deals with different resolutions on spatial and spectral domains. Second, nowadays, it is still both financially and labour expensive to acquire multiple data sources for the same region at the same time. In this paper, we provide the community with a benchmark multimodal data set, MDAS, for the city of Augsburg, Germany. MDAS includes synthetic aperture radar data, multispectral image, hyperspectral image, digital surface model (DSM), and geographic information system (GIS) data. All these data are collected on the same date, 7 May 2018. MDAS is a new benchmark data set that provides researchers rich options on data selections. In this paper, we run experiments for three typical remote sensing applications, namely, resolution enhancement, spectral unmixing, and land cover classification, on MDAS data set. Our experiments demonstrate the performance of representative state-of-the-art algorithms whose outcomes can serve as baselines for further studies. The dataset is publicly available at https://doi.org/10.14459/2022mp1657312 (Hu et al., 2022a) and the code (including the pre-trained models) at https://doi.org/10.5281/zenodo.7428215 (Hu et al., 2022b).

Highly accurate reference vehicle trajectories are required in the automotive domain e. g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices.

Terrestrial sensors are commonly used to inspect and document the condition of roads at regular intervals and according to defined rules. For example in Germany, extensive data and information is obtained, which is stored in the Federal Road Information System and made available in particular for deriving necessary decisions. Transverse and longitudinal evenness, for example, are recorded by vehicles using laser techniques. To detect damage to the road surface, images are captured and recorded using area or line scan cameras. All these methods provide very accurate information about the condition of the road, but are time-consuming and costly. Aerial imagery (e.g. multi- or hyperspectral, SAR) provide an additional possibility for the acquisition of the specific parameters describing the condition of roads, yet a direct transfer from objects extractable from aerial imagery to the required objects or parameters, which determine the condition of the road is difficult and in some cases impossible. In this work, we investigate the transferability of objects commonly used for the terrestrial-based assessment of road surfaces to an aerial image-based assessment. In addition, we generated a suitable dataset and developed a deep learning based image segmentation method capable of extracting two relevant road condition parameters from high-resolution multispectral aerial imagery, namely cracks and working seams. The obtained results show that our models are able to extraction these thin features from aerial images, indicating the possibility of using more automated approaches for road surface condition assessment in the future.

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

The 3D information of road infrastructures are gaining importance with the development of autonomous driving. In this context, the exact 2D position of the road markings as well as the height information play an important role in e.g. lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we apply a wavelet-enhanced fully convolutional network on multi-view high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multi-view imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results show an improved IoU of the automatic road marking segmentation by exploiting the multi-view character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the Semi Global Matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.

The 3D information of road infrastructures is growing in importance with the development of autonomous driving. In this context, the exact 2D position of road markings as well as height information play an important role in, e.g., lane-accurate self-localization of autonomous vehicles. In this paper, the overall task is divided into an automatic segmentation followed by a refined 3D reconstruction. For the segmentation task, we applied a wavelet-enhanced fully convolutional network on multiview high-resolution aerial imagery. Based on the resulting 2D segments in the original images, we propose a successive workflow for the 3D reconstruction of road markings based on a least-squares line-fitting in multiview imagery. The 3D reconstruction exploits the line character of road markings with the aim to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Results showed an improved IoU of the automatic road marking segmentation by exploiting the multiview character of the aerial images and a more accurate 3D reconstruction of the road surface compared to the semiglobal matching (SGM) algorithm. Further, the approach avoids the matching problem in non-textured image parts and is not limited to lines of finite length. In this paper, the approach is presented and validated on several aerial image data sets covering different scenarios like motorways and urban regions.

Building footprint information is vital for 3D building modeling. Traditionally, in remote sensing, building footprints are extracted and delineated from aerial imagery and/or LiDAR point cloud. Taking a different approach, this paper is dedicated to the optimization of OpenStreetMap (OSM) building footprints exploiting the contour information, which is derived from deep learning-based semantic segmentation of oblique images acquired by the Unmanned Aerial Vehicle (UAV). First, a simplified 3D building model of Level of Detail 1 (LoD 1) is initialized using the footprint information from OSM and the elevation information from Digital Surface Model (DSM). In parallel, a deep neural network for pixel-wise semantic image segmentation is trained in order to extract the building boundaries as contour evidence. Subsequently, an optimization integrating the contour evidence from multi-view images as a constraint results in a refined 3D building model with optimized footprints and height. Our method is leveraged to optimize OSM building footprints for four datasets with different building types, demonstrating robust performance for both individual buildings and multiple buildings regardless of image resolution. Finally, we compare our result with reference data from German Authority Topographic-Cartographic Information System (ATKIS). Quantitative and qualitative evaluations reveal that the original OSM building footprints have large offset, but can be significantly improved from meter level to decimeter level after optimization.

DLRAD - a new vision and mapping benchmark dataset for autonomous driving is now ready for the development and validation of intelligent driving algorithms. Stationary, mobile, and airborne sensors monitored simultaneously the environment around a reference vehicle, which was driving on urban, suburb and rural roads in and around the city of Braunschweig/Germany. Airborne images were acquired with the DLR 4k sensor system mounted on a helicopter. The DLR reference car FASCarE is equipped with the latest car sensor technology like front/rear radar, ultrasound and laser sensors, optical single and stereo cameras, and GNSS/IMU. Additionally, stationary terrestrial sensors like induction loops, optical mono and stereo cameras, radar and laser scanners monitor defined sections of the path from the ground. The stationary sensors are installed on gantries at main crossings and on pylons. The benchmark path with total length of 156km is divided in an urban road scenario (34km), a rural road scenario (50km), an industrial area scenario (26km), and a motorway scenario (46 km). Simultaneously, the helicopter with the 4k sensor systems follows the reference car by keeping it all the time in the central nadir view. Two 20MPix full frame nadir looking cameras with focal lengths of 50mm and 25mm cover the area around the reference car staggered according to the distance from the reference car with different GSDs of 7cm resp. 14cm. With a focal length of 50mm an area of 320m x 240m is covered assuming a flight height of 500m above ground. With frame rates around 1 Hz, it will be possible to create a 3D reference map and database with the positions of all moving and non-moving objects around the reference car including pedestrians, cyclists and all kinds of vehicles. This database will be augmented with the data from the stationary sensors to have a more detailed view at defined sections. A next crucial step in the construction of the DLRAD benchmark dataset is the annotation of all objects in the reference dataset. The reference vehicle FASCarE is a Volkswagen eGolf. It is equipped with different range detectors as follows: each four rear and front ultrasound sensors for the close range detection < 5m, three front and one rear IBEO laser scanner with range 200m, each two front and rear SMS radar, and one front Bosch radar. Additionally, an optical Bosch camera is installed at the front window and a stereo camera system is installed at the car roof for 3D and object detection purposes. The DLRAD benchmark dataset enables a huge variety of validation capabilities and opens a wide field of possibilities for the development, training and validation of machine learning algorithms in the context of autonomous driving. In this paper, we will present details of the sensor configurations and the acquisition campaign, which had taken place between the 18th July and 20th July 2017 in Braunschweig/Germany. Also, we show a first analysis of the data including the completeness and geometrical quality.

Die vertraglichen Au��enhandelsbeziehungen gelten als ���sch��rfste Waffe��� im au��enpolitischen Arsenal der Europ��ischen Union und werden bisweilen kontrovers diskutiert (Stichwort: TTIP). In der Literatur wurde mehrfach prognostiziert, dass der Vertrag von Lissabon (2009) hier Ver��nderungen im Sinne einer ���Politisierung��� von Handelsabkommen schaffen k��nnte; detaillierte empirische Studien fehlten aber bislang. In der vorliegenden Arbeit soll die Frage beantwortet werden, ob der Vertrag von Lissabon tats��chlich eine derart weitreichende Umgestaltung der Aushandelsbeziehungen zur Folge hatte. Die Beantwortung dieser Forschungsfrage erfolgt mittels einer qualitativen Analyse von sechs ausgew��hlten Freihandelsabkommen der EU, die vor und nach dem Lissabon- Vertrag verhandelt wurden. Die Unterschiede der Vertr��ge aus den beiden Zeitr��umen werden anhand der Theorie des Neo-Institutionalismus mit den institutionellen ��nderungen durch den Vertrag von Lissabon in diesem Politikbereich erkl��rt. Durch den Nachweis eines gestiegenen Einflusses des Europ��ischen Parlaments auf die Inhalte der Handelsabkommen der Union kann die Frage Do Treaties matter? eindeutig bejaht werden.