The regular inspection of the crane tracks of storage cranes at the Container Terminal Altenwerder (CTA), Hamburg requires high accuracy of measurements to determine its position. The allowed tolerances are in the range of 10 mm in the XY plane on a track length of 300 m. The traditional semi-automatic surveying methods are slow and require the interruption of the activities in the storage blocks. The research project AeroInspekt proposed a fully automatic measurement of the position of the tracks using UAV-based photogrammetry. In this paper, the results of the test campaign, carried out in June 2020, were presented where different cameras (150 mm and 80 mm telelens) and flight speeds (1.1 m/s and 1.9 m/s) at a 35 m flying height were performed. Furthermore, an automated rail delineation in the derived surface model was developed and evaluated with ground reference measurements. The results show that the required accuracy of the rail position with an RMSE of 3 mm in XY plane and 8 mm in altitude can be achieved with comparatively less disruption of regular block activities.

In professional use cases like police or fire brigade missions, coordinated and systematic force management is crucial for achieving operational success during intervention by the emergency personnel. A real-time situation picture enhances the coordination of the team. This situation picture includes not only an overview of the environment but also the positions, i.e., localization, of the emergency forces. The overview of the environment can be obtained either from known situation pictures like floorplans or by scanning the environment with the aid of visual sensors. The self-localization problem can be solved outdoors using the Global Navigation Satellite System (GNSS), but it is not fully solved indoors, where the GNSS signal might not be received or might be degraded. In this paper, we propose a novel combination of an inertial localization technique based on simultaneous localization and mapping (SLAM) with 3D building scans, which are used as prior information, for geo-referencing the positions, obtaining a situation picture, and finally visualizing the results with an appropriate visualization tool. We developed a new method for converting point clouds into a hexagonal prism map specifically designed for our SLAM algorithm. With this combination, we could keep the equipment for first responders as lightweight as required. We showed that the positioning led to an average accuracy of less than 1m indoors, and the final visualization including the building layout obtained by the 3D building reconstruction will be advantageous for coordinating first responder operations.

For professional use cases like police or fire brigade operations, a reliable self-localization is advantageous for the coordination of first responders (FRs). Self-localization is especially difficult in indoor scenarios where neither global navigation satellite systems’ signals may be received nor additional signals of opportunity exist for enabling reliable positioning. In this article, we propose an overall system that combines self-localization, communication of the FRs’ locations, 3-D building reconstruction or floor plans (if available), and visualization. The indoor navigation technique is based solely on inertial sensors and builds on a simultaneous localization and mapping technique. It is capable of using any information about the building layout as prior information for enhancing indoor positioning, georeferencing the positions, and finally, visualizing the results in a suitable visualization tool.

Real-time processing of UAV imagery is crucial for applications requiring urgent geospatial information, such as disaster response, where rapid decision-making and accurate spatial data are essential. However, processing high-resolution imagery in real time presents significant challenges due to the computational demands of feature extraction, matching, and bundle adjustment (BA). Conventional BA methods either downsample images, sacrificing important details, or require extensive processing time, making them unsuitable for time-critical missions. To overcome these limitations, we propose a novel real-time BA framework that operates directly on full-resolution UAV imagery without downsampling. Our lightweight, onboard-compatible approach divides each image into user-defined patches (e.g., N×N grids, default 150×150 pixels) and dynamically tracks them across frames using UAV GNSS/IMU data and a coarse, globally available digital surface model (DSM). This ensures spatial consistency for robust feature extraction and matching between patches. Overlapping relationships between images are determined in real time using UAV navigation system, enabling the rapid selection of relevant neighbouring images for localized BA. By limiting optimization to a sliding cluster of overlapping images, including those from adjacent flight strips, the method achieves real-time performance while preserving the accuracy of global BA. The proposed algorithm is designed for seamless integration into the DLR Modular Aerial Camera System (MACS), supporting large-area mapping in real time for disaster response, infrastructure monitoring, and coastal protection. Validation on MACS datasets with 50MP images demonstrates that the method maintains precise camera orientations and high-fidelity mapping across multiple strips, running full bundle adjustment in under 2 seconds without GPU acceleration.

Precise, standardized assessment of spatial resolution is essential for optical remote sensing. We present an adaptable Siemens-star methodology aligned with DIN 18740-8 that enables reliable, reproducible modulation transfer function (MTF) measurements across simulation, laboratory, and aerial (UAV) scenarios. The target geometry is parameterized by star frequency and ground sampling distance (GSD), providing a simple sizing rule for practical deployment. Design variants include multiple segment counts (8–48 Hz) and four gray-level contrasts to mitigate overexposure, a frequent field issue that biases MTF. A phase-based center-detection algorithm — applicable to square- and sinusoidal-type stars — ensures robust centering, a prerequisite for valid MTF estimation. Results show highly consistent MTFs across all configurations: average deviations of 0.007, 0.006, and 0.011 line/pixel for simulation, DSLR, and UAV data, respectively. Center localization exhibits an average two-dimensional error of 0.074 pixels over twenty target variants, confirming numerical stability and implementation accuracy. The scalable vector graphics (SVG) design ensures pixel-exact reproducibility and straightforward scaling to different GSDs while offering practical advantages in production cost, transport, and deployment. Overall, the approach establishes Siemens-star targets as a cost-effective and field-ready alternative to conventional methods, providing standardized procedures, robustness to non-ideal imaging conditions, and direct applicability to both RGB and monochrome systems in operational photogrammetry and remote sensing.SVG-designs of all Siemens stars as well as the ”Resolving-Power” tool to perform the measurements can be downloaded from https://macs.dlr.de/box/resolvingpower.html.