Search

Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.

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.

To allow for a robust and automatic exploitation of Sentinel-2 data, Analysis Ready Data (ARD) products are requested by most users. The processors of ARD products take care of the common burdens necessary for most applications, that include precise orthorectification, cloud detection and atmospheric correction steps, as well as the generation of periodic syntheses of cloud free surface reflectances. The French Theia land data center, and the German Earth Observation Center (EOC) started delivering Sentinel-2 surface reflectance products to users in 2016 in France and 2019 in Germany respectively. Both centers produce and distribute these data sets in near real time, over large regions requested by French users such as Western Europe, Maghreb, Sahel, Madagascar… Theia’s and EOC products include an instantaneous surface reflectance product (Level-2A), and a monthly cloud free synthesis of surface reflectance (Level-3A). This article shortly describes the methods used to generate the Level-2A products with the MAJA processor, and the Level-3A products with theWASP processor. The MAJA processor is based on multi-temporal methods, that use the slow variation of surface reflectance to detect clouds and estimate aerosol depth, while WASP, thanks to the quality of MAJA cloud mask, calculates a weighted average of all the cloud free observations over 45 days, every month. The article also provides validation results for Level-2A and Level-3A products, resulting from comparison with in-situ data and with other methods. A last section gives first insights from the monitoring of user uptake of the distributed products.

Undocumented buildings are buildings which were built years ago, but were never recorded in official digital cadastral maps. Detection of undocumented buildings is of great importance for urban planning and monitoring. The state of Bavaria, Germany, pursues this task based on high resolution optical data and digital surface models, using semi-automatic detection methods, which suffer from a high false alarm rate. In order to study the influence of sampling strategies on the performance of building detection, we have firstly designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether the trained model from a district contains valuable information for building detection in a different district. It was found that the large-scale building detection results can be considerably improved when training samples are collected from different districts. Based on the building detection results, we propose a novel framework for the detection of undocumented buildings using Convolutional Neural Network (CNN) and official geodata. More specifically, buildings are identified as undocumented, when their pixels in the output of the CNN are predicted as “building”, whereas they belong to the “non-building” in the Digital Cadastral Map (DFK). The detected undocumented building pixels are subsequently divided into the class of old or new undocumented building with the aid of a Temporal Digital Surface Model (tDSM) in the stage of decision fusion. By doing so, a seamless map of undocumented buildings is generated for 1/4th of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has demonstrated the use of CNN for the robust detection of undocumented buildings at large-scale.

DLRAD – a new vision and mapping benchmark dataset for autonomous driving is under development for the 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 research car FASCarE is equipped with the latest 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. Simultaneously, the helicopter with the 4k sensor systems follows the reference car by keeping it all the time in the central nadir view. A next crucial step in the construction of the DLRAD benchmark dataset is the annotation of all objects in the reference dataset.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. The dataset will be published as soon as the coregistration and annotations are complete.

In this paper, we present a work-flow to investigate the joint visibility between very-high-resolution SAR and optical images of urban scenes. For this task, we extend the simulation framework SimGeoI to enable a simulation of individual pixels rather than complete images. Using the extended SimGeoI simulator, we carry out a case study using a TerraSAR-X staring spotlight image and a Worldview-2 panchromatic image acquired over the city of Munich, Germany. The results of this study indicate that about 55 % of the scene are visible in both images and are thus suitable for matching and data fusion endeavours, while about 25 % of the scene are affected by either radar shadow or optical occlusion. Taking the image acquisition parameters into account, our findings can provide support regarding the definition of upper bounds for image fusion tasks, as well as help to improve acquisition planning with respect to different application goals.

GeoRaySAR, an automated SAR simulator developed at DLR, identifies buildings in high resolution SAR data by utilizing geometric knowledge extracted from digital surface models (DSMs). Hitherto, the simulator has utilized DSMs generated from LiDAR data from airborne sensors with pre-filtered vegetation. Discarding the need for pre-optimized model input, DSMs generated from high resolution optical data (acquired with WorldView-2) are used for the extraction of building-related SAR image parts in this work. An automatic preprocessing of the DSMs has been developed for separating buildings from elevated vegetation (trees, bushes) and reducing the noise level. Based on that, automated simulations are triggered considering the properties of real SAR images. Locations in three cities, Munich, London and Istanbul, were chosen as study areas to determine advantages and limitations related to WorldView-2 DSMs as input for GeoRaySAR. Beyond, the impact of the quality of the DSM in terms of building extraction is evaluated as well as evaluation of building DSM, a DSM only containing buildings. The results indicate that building extents can be detected with DSMs from optical satellite data with various success, dependent on the quality of the DSM as well as on the SAR imaging perspective.

The assignment of phase center positions (in 2D or 3D) derived from SAR data to physical object is challenging for many man-made structures such as buildings or bridges. In contrast, light poles and traffic signs are promising targets for tasks based on 3-D geolocation as they often show a prominent and spatially isolated appearance. For a detailed understanding of the nature of both targets, this paper presents results of a dedicated simulation case study, which is based on ray tracing methods (simulator RaySAR). For the first time, the appearance of the targets is analyzed in 2D (image plane) and 3D space (world coordinates of scene model) and reflecting surfaces are identified for related dominant image pixels. The case studies confirms the crucial impact of spatial resolution in the context of light poles and traffic signs and the appropriateness of light poles as target for 3-D geolocation in case of horizontal ground surfaces beneath.

Motivated by the distinct appearance of facades in high resolution SAR images with respect to signal incidence angles and polarizations, this paper introduces a way to fuse SAR imagery and 3D GIS (geoinformation system) data (format: CityGML) based on SAR simulation methods. To this end, the known building geometry is used to simulate the extent of building layover for identifying the related image parts in high resolution TerraSAR-X images. The simulated SAR images are generated and geocoded by an automated processing chain which is initialized by the automated parsing of the CityGML dataset and the TerraSAR-X orbit file. Confirming the functionality of the developed interface between simulation and CityGML, first results are presented for an urban scene in the Munich city center in order to discuss future opportunities in the context of change detection applications.

Thanks to the high resolution of modern SAR satellites many persistent scatterers (PS) appear at single buildings, approximately half of them representing building facades. These groups of points often show regular patterns in the SAR images that can be related to repeating structure elements on the facades. Hence, individual buildings can now be monitored over time, either based on amplitude (for change detection) and phase information (using interferometric methods). However, patterns of point signatures are often disrupted or disappear when the imaging geometry is slightly changed. The investigation at hand provides a detailed analysis of PS patterns based on reference data obtained from a photogrammetric survey. The processing of the optical images allows to create a detailed 3D model of the respective facade, which represents the geometrical object information for SAR simulation. From the simulation results the appearance of the point patterns of natural PS in high resolution SAR data can be better understood. The approach and findings for a case study in Munich is described in detail in this paper.

Abstract Background  NAP, an 8-amino acid peptide (NAPVSIPQ=Asn-Ala-Pro-Val-Ser-Ile-Pro-Gln) derived from activity-dependent neuroprotective protein (ADNP), plays an important role in neuronal differentiation and the survival of neurons in different pathological situations. We already discovered that NAP increases the survival of retinal ganglion cells (RGC) in vitro, and supports neurite outgrowth in retinal explants at femtomolar concentrations. The aim of this study was to investigate the effects of NAP on RGC survival after transient retinal ischemia and optic nerve crush. Methods  RGC of male Wistar rats were labelled retrogradely with 6 l FluoroGold injected stereotactically into both superior colliculi. Seven days later, retinal ischemia was induced by elevating the intraocular pressure to 120 mm Hg for 60 minutes or by crushing one optic nerve for 10 s after a partial orbitotomy. NAP was either injected intraperitoneally in the concentration of 100 mg/kg 1 day before, directly after, and on the first and the second days after damage, or intravitreally (0.05 or 0.5 μg/eye) directly after the optic nerve crush. Controls received the same concentrations of a control peptide. Densities of surviving RGC and activated microglial cells (AMC) were quantified in a masked fashion 10 days after damage by counting FluoroGold-labelled cells. Results  After retinal ischemia, intraperitoneal injections of NAP increased the number of surviving RGC by 40% (p p = 0.07) when injected intraperitoneally and by 54% (p  Conclusions  NAP acts neuroprotectively in vivo after retinal ischemia and optic nerve crush, and may have potential in treating optic nerve diseases. [ABSTRACT FROM AUTHOR]

There are different types of dust particles in interplanetary space, such as dust from comets and asteroids, and interstellar grains traversing the solar system. Based on experience with current space dust instruments, a novel dust telescope is being developed. A dust telescope is a combination of a dust trajectory sensor for the identification and an analyzer for the elemental composition of the dust. Dust particles’ trajectories are determined by the measurement of the electric signals that are induced when a charged grain flies through a position-sensitive electrode system. The objective of the trajectory sensor is to measure dust charges in the range 10−16–10−13 C and dust speeds in the range 6–100 km/s. First tests with a laboratory setup have been performed. The chemical analyzer will have an impact area of 0.1 m2. It consists of a target with an acceleration grid and a single-stage reflectron for energy focusing, and a central ion detector. Results from SIMION simulations show that a mass resolution of M/ΔM>150 can be obtained. [ABSTRACT FROM AUTHOR]

In Drosophila, Notch and EGFR signalling pathways are closely intertwined. Their relationship is mostly antagonistic, and may in part be based on the phosphorylation of the Notch signal transducer Suppressor of Hairless [Su(H)] by MAPK. Su(H) is a transcription factor that together with several cofactors regulates the expression of Notch target genes. Here we address the consequences of a local induction of three Su(H) variants on Notch target gene expression. To this end, wild-type Su(H), a phospho-deficient Su(H)MAPK-ko and a phospho-mimetic Su(H)MAPK-ac isoform were overexpressed in the central domain of the wing anlagen. The expression of the Notch target genes cut, wingless, E(spl)m8-HLH and vestigial, was monitored. For the latter two, reporter genes were used (E(spl)m8-lacZ, vgBE-lacZ). In general, Su(H)MAPK-ko induced a stronger response than wild-type Su(H), whereas the response to Su(H)MAPK-ac was very weak. Notch target genes cut, wingless and vgBE-lacZ were ectopically activated, whereas E(spl)m8-lacZ was repressed by overexpression of Su(H) proteins. In addition, in epistasis experiments an activated form of the EGF-receptor (DERact) or the MAPK (rlSEM) and individual Su(H) variants were co-overexpressed locally, to compare the resultant phenotypes in adult flies (thorax, wings and eyes) as well as to assay the response of the Notch target gene cut in cell clones.