Real Time Airborne Monitoring for Disaster and Traffic Applications

Remote sensing applications like disaster or mass event monitoring need the acquired data and extracted information within a very short time span. Airborne sensors can acquire the data quickly and on-board processing combined with data downlink is the fastest possibility to achieve this requirement. For this purpose, a new low-cost airborne frame camera system has been developed at the German Aerospace Center (DLR) named 3K-camera. The pixel size and swath width range between 15 cm to 50 cm and 2.5 km to 8 km respectively. Within two minutes an area of approximately 10 km x 8 km can be monitored. Image data are processed onboard on five computers using data from a real time GPS/IMU system including direct georeferencing. Due to high frequency image acquisition (3 images/second) the monitoring of moving objects like vehicles and people is performed allowing wide area detailed traffic monitoring

On the nature of the peculiar superthin galaxy UGC 12281

UGC 12281 has been classified as having a pure disk and being a low surface brightness galaxy (LSBG), thus being an obvious member of the so-called superthin galaxies. At the same time it represents an extremely untypical type of LSBG due to its remarkable amount of current star formation and evidence for extraplanar ionized gas. This makes it become a perfect tool to investigate the triggering of star formation in LSB galaxies, located in an alleged isolated area. By means of deep photometry and long-slit spectroscopy we analyse the H$\alpha$ halo and verify the existence of a potential dwarf companion which we found on processed SDSS images.Comment: 3 pages, 2 figures, to appear in: IAU Symp. 284, The Spectral Energy Distribution of Galaxies, eds.: R.J. Tuffs & C.C. Popesc

Providentia - A Large-Scale Sensor System for the Assistance of Autonomous Vehicles and Its Evaluation

The environmental perception of an autonomous vehicle is limited by its physical sensor ranges and algorithmic performance, as well as by occlusions that degrade its understanding of an ongoing traffic situation. This not only poses a significant threat to safety and limits driving speeds, but it can also lead to inconvenient maneuvers. Intelligent Infrastructure Systems can help to alleviate these problems. An Intelligent Infrastructure System can fill in the gaps in a vehicle's perception and extend its field of view by providing additional detailed information about its surroundings, in the form of a digital model of the current traffic situation, i.e. a digital twin. However, detailed descriptions of such systems and working prototypes demonstrating their feasibility are scarce. In this paper, we propose a hardware and software architecture that enables such a reliable Intelligent Infrastructure System to be built. We have implemented this system in the real world and demonstrate its ability to create an accurate digital twin of an extended highway stretch, thus enhancing an autonomous vehicle's perception beyond the limits of its on-board sensors. Furthermore, we evaluate the accuracy and reliability of the digital twin by using aerial images and earth observation methods for generating ground truth data

Automatic Object Segmentation To Support Crisis Management Of Large-scale Events

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations

Generation of Reference Vehicle Trajectories in real-world Situations using Aerial Imagery from a Helicopter

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

Time perception and the experience of agency in meditation and hypnosis

Mindfulness meditation and hypnosis are related in opposing ways to awareness of intentions. The cold control theory of hypnosis proposes that hypnotic responding involves the experience of involuntariness while performing an actually intentional action. Hypnosis therefore relies upon inaccurate metacognition about intentional actions and experiences. Mindfulness meditation centrally involves awareness of intentions and is associated with improved metacognitive access to intentions. Therefore, mindfulness meditators and highly hypnotizable people may lie at opposite ends of a spectrum with regard to metacognitive access to intention‐related information. Here we review the theoretical background and evidence for differences in the metacognition of intentions in these groups, as revealed by chronometric measures of the awareness of voluntary action: the timing of an intention to move (Libet's “W” judgments) and the compressed perception of time between an intentional action and its outcome (“intentional binding”). We review these measures and critically evaluate their proposed connection to the experience of volition and sense of agency

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Real time camera system for disaster and traffic monitoring

A real time airborne monitoring system for monitoring of natural disasters, mass events, and large traffic disasters was developed in the last years at the German Aerospace Center (DLR). This system consists of an optical wide-angle camera system (3K system), a SAR sensor, an optical and microwave data downlink, an onboard processing unit and ground processing station with online data transmission to different end user portals. The development of the real time processing chain from the data acquisition to the ground station is still a very challenging task. In this paper, an overview of all relevant parts of the airborne optical mapping system is given and selected system processes are addressed and described in more detail. The experiences made in the flight campaigns of the last years are summarized with focus on the image processing part, e.g. reached accuracies of georeferencing and status of the traffic processors

Near Real Time Processing of DSM from Airborne Digital Camera System for Disaster Monitoring

Knowledge of accurate digital surface models (DSMs) is a valuable information for security authorities and organizations during emergencies, disasters, or big events. In this context, the potential of the recently developed digital optical camera system (3K) to derive DSMs automatically and in near real time will be evaluated. The 3K camera system with a frame rate of 3Hz consists of three non-metric off-the-shelf cameras (Canon EOS 1Ds Mark II, 16 MPixel), which are aligned in an array with one camera looking in nadir direction and two in oblique direction. The DSMs are calculated using a subpixel hierarchical matching based on interest points followed by a region growing algorithm. Exterior orientation is given online by GPS/IMU data, whereas interior camera parameters are provided in advance. The resulting DSM is calculated by forward intersection. For the validation, the point accuracies in relation to the base-to-height ratio as well as the reached point density in relation to the calculation time were calculated. Two flight campaigns with the 3K camera system were performed with varying frame rates over the centre of Munich on 30th April and on 17th June 2007. Point accuracies over flat terrain were validated with reference DEMs and correspond quite well to simulated accuracies. The validation showed that smaller base-to-height ratios decrease the point accuracies but increase point densities as images are more similar in particular over urban areas. Exemplarily possible applications for near real time DSMs in the context of disaster monitoring are described, e.g. monitoring of land slides, 3D change detection over urban area, and monitoring of infrastructure. Problematic in all cases is the long processing time, thus the matching algorithms must be modified or new techniques must be applied. Some starting points for the reduction of processing times are discussed. In this context, the proposed matching algorithms and results serve as reference for much faster implementations