Aerial photogrammetric 3D mapping refers to the process of capturing overlapping imagery - and increasingly, LiDAR data - via aerial platforms such as unmanned and manned aircraft, followed by computational processing to generate precise 3D representations of urban environments, infrastructure and natural landscapes. In recent years, the field has undergone significant transformation, driven by advancements in imaging technology, including larger and more sensitive sensors, the deployment of multi-camera systems and the integration of photogrammetry with LiDAR. These developments have been accompanied by increasing automation in feature detection, semantic segmentation and both 2D and 3D object classification. This paper aims to provide a critical review of the current state-of-the-art in sensor technologies and multi-sensor integration strategies. It highlights key technological innovations and evolving methodologies that are reshaping aerial 3D mapping practices and influencing both industry standards and market dynamics.

Due to advancements in the automotive industry, there has been a significant increase in the availability of compact LiDAR (Light Detection and Ranging) sensors designed for consumer use in recent years. Some of these sensors are suitable for surveying tasks based on Uncrewed Aerial Vehicles (UAVs). This paper initially explores the differences between consumer-grade and survey-grade LiDAR systems. It focuses on two key components: the scanning mechanisms and the laser ranging units. Drawing upon the technical specifications of two specific systems, the consumer-grade DJI Zenmuse L1 sensor and the survey-grade scanner RIEGL VUX-1UAV, the paper first discusses the anticipated effects of sensor parameters on the resulting 3D point cloud. Subsequently, these theoretical findings are validated using a sample dataset collected in Hessigheim, Baden-W & uuml;rttemberg, Germany. The analysis serves to highlight the capabilities and limitations of consumer-grade LiDAR. Applying rigorous strip adjustment and subsequent quality assessment of the resulting 3D point clouds, we found out that the consumer-grade system mainly suffers from insufficient scan angle calibration, which could be effectively mitigated by using only points with a scan angle < 25 degrees. In terms of georeferencing, we can state that high-end scanners have lower measurement noise (in the range of 5 - 10 mm) and higher accuracy in localizing 3D points compared to low-cost sensors. Additionally, the enhanced laser beam quality of high-end devices, including aspects such as beam divergence and beam shape, enables more detailed object detection at the same point density. For geodetic and cartographic applications, however, the much less expensive consumer LiDAR systems can be used if moderate accuracy requirements of 5 - 10 cm are sufficient.

Driven by developments in the automotive industry, the availability of compact consumer-grade LiDAR (Light Detection and Ranging) sensors has increased significantly in recent years. Some of these sensors are also suitable for UAV-based surveying tasks. This paper first discusses the differences between consumer-grade and survey-grade LiDAR systems. Special attention will be paid to the scanning mechanisms used on the one hand and to different solutions for the transceiver units on the other hand. Based on the technical data of two concrete systems, the consumer-grade DJI Zenmuse L1 sensor and the survey-grade scanner RIEGL VUX-1UAV, the expected effects of the sensor parameters on the 3D point cloud are first discussed theoretically and then verified using an exemplary data set in Hessigheim (Baden-Württemberg, Germany). The analysis shows the possibilities and limitations of consumer-grade LiDAR. Compared to the low-cost sensor, the high-end scanner exhibits lower range measurement noise (5–10 mm) and better 3D point location accuracy. Furthermore, the higher laser beam quality of high-end devices (beam divergence, beam shape) enables more detailed object detection at the same point density. With moderate accuracy requirements of 5–10 cm, however, applications in the geodetic-cartographic context also arise for the considerably less expensive consumer-grade LiDAR systems.

Geographic information systems (GIS) receive data from many sources that are different in technology, geographic coverage, date of capture, and accuracy – to mention few categories. The vast majority of the today's topographical and GIS-data are captured from mobile and possibly autonomous platforms that operate from the air, on the ground (also indoors) or on the water and that are equipped with optical sensors. Although the palette of optical sensors is rather large the most useful for mapping purposes falls into two categories. First are the passive sensors such as digital cameras in frame or line configuration. The main technological concepts of these sensors are introduced in Optical Sensors together with Lidar that serves the acquisition of detailed terrain structure in natural areas. The optical acquisition is supported by trajectory determination through the combined use of integrated navigation technology, which main concepts are outlined in Navigation Sensors. The geometrical principals of 3-D restitution of the scene are described first in Photogrammetry for the case of frame imagery only, later in Sensor Fusion for active sensors and integrated approaches. An overview of Mapping Products concludes this chapter.

The use of Unmanned Aerial Vehicles (UAVs) has surged in the last two decades, making them popular instruments for a wide range of applications, and leading to a remarkable number of scientific contributions in geoscience, remote sensing and engineering. However, the development of best practices for high quality of UAV mapping are often overlooked representing a drawback for their wider adoption. UAV solutions then require an inter-disciplinary research, integrating different expertise and combining several hardware and software components on the same platform. Despite the high number of peer-reviewed papers on UAVs, little attention has been given to the interaction between research topics from different domains (such as robotics and computer vision) that impact the use of UAV in remote sensing. The aim of this paper is to (i) review best practices for the use of UAVs for remote sensing and mapping applications and (ii) report on current trends -including adjacent domains -for UAV use and discuss their future impact in photogrammetry and remote sensing. Hardware developments, navigation and acquisition strategies, and emerging solutions for data processing in innovative applications are considered in this analysis. As the number and the heterogeneity of debated topics are large, the paper is organized according to very specific questions considered most relevant by the authors.

During the last two decades, UAV emerged as standard platform for photogrammetric data collection. Main motivation in that early phase was the cost effective airborne image collection at areas of limited size. This was already feasible by rather simple payloads like an off-the-shelf, compact camera and a navigation-grade GNSS sensor. Meanwhile, dedicated sensor systems enable applications that have not been feasible in the past. One example is the airborne collection of dense 3D point clouds at millimetre accuracies, which will be discussed in our paper. For this purpose, we collect both LiDAR and image data from a joint UAV platform and apply a so-called hybrid georeferencing. This process integrates photogrammetric bundle block adjustment with direct georeferencing of LiDAR point clouds. By these means georeferencing accuracy is improved for the LiDAR point cloud by an order of magnitude. We demonstrate the feasibility of our approach in the context of a project, which aims on monitoring of subsidence of about 10 mm/year. The respective area of interest is defined by a ship lock and its vicinity of mixed use. In that area, multiple UAV flights were captured and evaluated for a period of three years. As our main contribution, we demonstrate that 3D point accuracies at sub-centimetre level can be achieved. This is realized by joint orientation of laser scans and images in a hybrid adjustment framework, which enables accuracies corresponding to the GSD of the captured imagery.

Historical aerial photographs represent a special cultural asset for preserving information about land cover and land use change in the twentieth century with a high spatial and temporal resolution. A current topic is the digitisation of historical images to make them accessible to a wider range of users and to preserve them from age deterioration. For a photogrammetric evaluation, a high geometric stability and accuracy during the digitization process is required. In this work, the resolving power and geometric quality of a Phase One iXM-MV150F high-performance camera was investigated, which is used at the Landesamt für Geoinformation und Landentwicklung Baden-Württemberg in the project ‘Digitaler Luftbildatlas Baden-Württemberg’ for the digitisation of historical aerial photographs. The resolving power of the system was empirically measured and analysed. The required modulation transfer function was determined using Siemens stars. With this method, the significant influence of the focus setting and deviations of the plane-parallel alignment could be determined. Using a digitised aerial survey of the Vaihingen/Enz test field, the impact of the above-mentioned effects and the influence of the geometry of the scanning camera on the quality of the derived data products was shown in comparison to a photogrammetric scanner. The comparison showed that dedicated photogrammetric scanners still achieve a higher accuracy, even if a high-quality optical system is used for the digitising stand with the document camera. Further investigations are justified to improve the accuracy and stability of digitising the aerial image with a document camera.

The objective of this paper is to evaluate the performance of high-end and regular UAV-based camera systems. Different factors contribute to the overall accuracy. The evaluation partly relies on the methods, which are part of the new upcoming German standard DIN 18740-8 “Photogrammetric products – Part 8: Requirements for image quality (quality of optical remote sensing data)”. The image data quality in general is quantitatively evaluated at different processing levels e.g. uncorrected, corrected original image, influence of debayering, orthoimage processing, image restoration, etc. This requires pre-processing of the image data to produce a comparable data quality, the acquisition, provision and processing of reference data and additional information. This analysis includes the spatial resolution. Furthermore, the geometric camera stability and the influence of different image block constellations directly influences the overall 3D object point quality. This is typically evaluated from test sites. For empirical testing UAV-based images from the DJI Phantom 4 series with proprietary in-built cameras are compared to drone images taken with Phase One iXM 100 MPix camera.

Effective image resolution is an important image quality factor for remote sensing sensors and significantly affects photogrammetric processing tool chains. Tie points, mandatory for forming the block geometry, fully rely on feature points (i.e. SIFT, SURF) and quality of these points however is significantly correlated to image resolution. Spatial resolution can be determined in different ways. Utilizing bar test charts (e.g. USAF51), slanted edges (ISO 12233) and Siemens-Stars are widely accepted techniques. The paper describes these approaches and compares all in one joint experiment. Moreover, Slanted-Edge and Siemens-Star method is evaluated using (close to) ideal images convolved with known parameters. It will be shown that both techniques deliver conclusive and expected results.

This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.

This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture. It is motivated by a project aiming at the deformation monitoring of a ship lock and its surrounding. This study is part of a research and development project initiated by the German Federal Institute of Hydrology (BfG) in Koblenz in partnership with the Office of Development of Neckar River Heidelberg (ANH). For this first official presentation of the project, data from the first flight campaign will be analysed and presented. Despite the fact that monitoring aspects cannot be discussed before data from additional flight campaigns will be available later this year, our results from the first campaign highlight the potential of high-end UAV-based image and LiDAR sensors and their data fusion. So far, only techniques from engineering geodesy could fulfil the aspired accuracy demands in the range of millimetres. To the knowledge of the authors, this paper for the first time addresses such ultra-high accuracy applications by combing high precision UAV-based LiDAR and dense image matching. As the paper is written at an early stage of processing only preliminary results can be given here.

Sensor calibration, image orientation, object extraction and scene understanding from images and image sequences are important research topics in Photogrammetry, Remote Sensing, Computer Vision and Geoinformation Science, the areas of interest of the International Society for Photogrammetry and Remote Sensing (ISPRS). Within these areas, both geometry and semantics play an important role, and high quality results require appropriate handling of all these aspects. While individual algorithms differ according to the imaging geometry and the employed sensors and platforms, all mentioned aspects need to be integrated in a suitable workflow to solve most real-world problems.

Digital airborne camera systems and their high geometric resolution demand for new algorithms and procedures of image data analysis and interpretation. Parameters describing image quality are necessary for various fields of application (e.g. sensor and mission design, sensor comparison, algorithm development, in-orbit-behaviour of instruments). The effective sensor resolution is one important parameter which comprehensively estimates the optical quality of a given imaging sensor-lens combination. Although determination of resolving power is a well-studied field of research, there are still some scientific questions to be answered when it comes to a standardized (eventually absolute) determination. This is also research object of a committee of the “German Institute for Standardization” and the given contribution outlines the current state of investigation concerning effective resolving power for airborne camera systems. Therefore an approach using signal processing techniques to calculate the effective image resolution will be described. The open scientific issues will be introduced, explained and answered to some extend.

Different UAV platforms and sensors are used in mapping already, many of them equipped with (sometimes) modified cameras as known from the consumer market. Even though these systems normally fulfil their requested mapping accuracy, the question arises, which system performs best? This asks for a benchmark, to check selected UAV based camera systems in well-defined, reproducible environments. Such benchmark is tried within this work here. Nine different cameras used on UAV platforms, representing typical camera classes, are considered. The focus is laid on the geometry here, which is tightly linked to the process of geometrical calibration of the system. In most applications the calibration is performed in-situ, i.e. calibration parameters are obtained as part of the project data itself. This is often motivated because consumer cameras do not keep constant geometry, thus, cannot be seen as metric cameras. Still, some of the commercial systems are quite stable over time, as it was proven from repeated (terrestrial) calibrations runs. Already (pre-)calibrated systems may offer advantages, especially when the block geometry of the project does not allow for a stable and sufficient in-situ calibration. Especially for such scenario close to metric UAV cameras may have advantages. Empirical airborne test flights in a calibration field have shown how block geometry influences the estimated calibration parameters and how consistent the parameters from lab calibration can be reproduced.