Bundle Block Adjustment Research Articles

Impacts of GCP Distributions on UAV-PPK Photogrammetry at Sermeq Avannarleq Glacier, Greenland

Real-Time/Post-Processing Kinematic (RTK/PPK) technology has been widely applied in Unmanned Aerial Vehicle (UAV) photogrammetry in glaciological research. Considering that ground control points (GCPs) cannot be set on glaciers, evaluating the impacts of one-sided distribution is essential. In this study, 8571 images were captured at Sermeq Avannarleq glacier in western Greenland from 4 August 2021 to the 6th, covering approximately 85 km2, with the furthest distance being 13.22 km away from the coastline. Benefited by the meandering coastline, 11 roving stations roughly uniformly distributed on bare rock were surveyed with the RTK technique. PPK-geotagged images were processed in Agisoft Metashape Professional to derive the DSMs, utilizing eight different configurations of GCP distributions that gradually extended longitudinally (along the glacier flow direction) to the upper part of the glacier. The accuracy of DSMs was evaluated by referring to the validation points (VPs) that were not employed in the Bundle Block Adjustment (BBA). The results indicated that the RMSE values of the easting, northing, and height of the reconstruction model georeferenced by only PPK geotagging (no GCPs applied) were 0.038 m, 0.031 m, and 0.146 m, respectively. Applying four GCPs located at one side of the region but with both longitudinal and lateral distribution improved the RMSE values in easting, northing, and vertical to 0.037 m, 0.031 m, and 0.081 m, respectively, and these values were stable even when distributing four GCPs evenly or when increasing the number of GCPs to eleven. Moreover, the cross-validation with ICESat-2 and ArcticDEM performed only at an off-glacier region also suggested that vertical accuracy shows significant improvements for every configuration of GCPs compared to the reconstruction model optimized only by PPK, but such improvements were not obvious if the number of GCPs exceeded four. Moreover, no elevation ramps appeared in the UAV DSM, even for the GCP configuration with only two GCPs distributed at the terminus. Therefore, combining PPK with only a few GCPs but distributing in both directions of the surveying region can offer a viable solution for obtaining glacier DSMs at the coastline with decimeter-level accuracy.

Landed Planetary Sensor Model Support with the Community Sensor Model

This note describes recent work to develop landed planetary sensor model support conforming to the widely adopted Community Sensor Model (CSM) standard. We describe the CSM specification and its use in the planetary sciences, introduce the test sensors carried aboard the Mars Science Laboratory Curiosity Rover, and describe the engineering research requirements fulfilled. Ultimately, we demonstrate use of a CSM framing sensor model for the NavCam and MastCam instruments including stereo terrain extraction and map projection using the NASA Ames Stereo Pipeline over a multi-day traverse and multi-image correspondence identification needed as an input to stereophotogrammetric block bundle adjustment using the USGS-maintained Integrated Software for Imagers and Spectrometers library.

A New Precise Point Positioning with Ambiguity Resolution (PPP-AR) Approach for Ground Control Point Positioning for Photogrammetric Generation with Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.

Optimizing Spatial Accuracy for Photogrammetric Processing of Drone-based 3D Mapping

Unmanned aerial vehicle (UAV) systems have become crucial for gathering information for observation, surveillance, mapping, and 3D modeling tasks. The use of UAVs in close and mid-range sectors has shown potential for cost-effective alternatives to traditional aerial photogrammetry conducted by humans. The research aims to optimize photogrammetric processing for drone-based 3D mapping by examining strategies and applications to increase accuracy and efficiency. The study highlights the significance of image overlap and high-quality camera equipment to obtain reliable outcomes for stereo-photogrammetry and depth measurements. Drones and specialized software like 3DF Zephyr and Pix4D were used to create a 3D map. The software uses automatic structure from motion techniques, including feature extraction, image matching, and bundle block adjustment, to produce a dense point cloud that forms the basis for the 3D model. A DGPS system was implemented to enhance spatial accuracy. The map initially showed inadequate accuracy, with an error rate exceeding 80cm. However, with the use of a DGPS system, the error was reduced to less than 3cm. This study provides suggestions and insights for improving photogrammetric processing for drone-based 3D mapping.

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Utilization of Unmanned Aerial Vehicles (UAV) mounted with non-metric consumer grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, there is a need for accurate camera parameters determined through camera calibration. Camera calibration can be performed before (pre-calibration) or during the bundle block adjustment (self-calibration). This study aims to analyze the effect of camera calibration parameters on the accuracy of UAV products namely Digital Elevation Model (DEM) and orthoimage. Camera calibration parameters are estimated using two approaches namely self-calibration which deploys 3D image information of the scene in a bundle adjustment and a 2D reference object-based approach known as Zhang’s technique which requires image information of a planar pattern. A DJI FC220 camera mounted on a DJI Mavic Pro UAV was used. Self-calibration was deployed in Agisoft Metashape software based on Brown’s method and Zhang’s technique was deployed in MATLAB and OpenCV. Based on internal measures of accuracy, OpenCV yields the least reprojection error of 0.14 followed by MATLAB (0.79) and self-calibration (1.21). Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm, for the horizontal and vertical, respectively, followed by Zhang’s technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of accuracy of UAV mapping products computed with and without calibration emphasizes the need for camera calibration to optimize accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

Multimedia Photogrammetry with non-planar Water Surfaces – Accuracy Analysis on Simulation Basis

Abstract. If multimedia-photogrammetry is used for the generation of point clouds of submerged objects or of the water bottom, Snell’s law has to be considered. When the images are taken from air, image rays are refracted at the air-water interface. This results in the collinearity equations being no longer valid. Bundle block adjustment can still be solved by adding additional terms considering Snell’s law. Existing approaches usually assume that the water surface is flat. Refractive indices and water height can either be measured separately or included as unknowns in the adjustment. However, when the water surface is not flat due to the presence of waves, assuming a planar water surface leads to large geometric errors. This work will analyze the significance of those errors and propose a way of including water surface parameters as unknowns into the bundle block adjustment, both based on simulated data. The simulation reproduces multiple images taken simultaneously, e.g. from synchronized UAV cameras or from cameras on tripods.

High-detail and low-cost underwater inspection of large-scale hydropower dams

Abstract. The article presents a practical method that combines low-cost camera systems with remotely operated vehicles (ROVs) to accomplish a comprehensive but economically feasible underwater survey of large hydropower infrastructures. Typically, inspecting reservoirs entails draining them off to allow for visual inspections, which are time-intensive, pose risks to operators' safety and are associated with generation losses. In this regard, ROVs are a much safer and more efficient alternative to traditional methods. The study was conducted at the Pack reservoir in Austria, where a reference framework was set up using terrestrial laser scanning and checkerboard markings for the above-water components. A ROV equipped with a GoPro camera and lighting system for the underwater recordings has been employed. Via a close-range photogrammetric approach, it was possible to generate 3D point clouds of the submerged infrastructure with a survey-grade accuracy level. Various strategies were explored to perform bundle block adjustment (BBA), among these were strategies where ground control points (GCPs) were used, strategies without the use of GCPs but pre-calibrated initial camera parameters and strategies with a combination of using both GCPs and pre-calibrated camera parameters in the BBA. The deployment of an inspection technique using low-cost sensors that can generate highly detailed three-dimensional models of submerged infrastructure areas is presented and discussed, allowing easy detection and localization for maintenance inspection, all while being cost-effective. The paper strengthens the suggestion of best practices that optimize camera settings, considering the effect of electronic image stabilization, suggesting its avoidance, and using advanced calibration methods.

Depth Supervised Neural Surface Reconstruction from Airborne Imagery

Abstract. While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS). Triggered by a manifold of research activities, promising results have been gained especially for textureless, transparent, and reflecting surfaces, while such scenarios remain challenging for traditional MVS-based approaches. However, most of these investigations focus on close-range scenarios, with studies for airborne scenarios still missing. For this task, NeRFs face potential difficulties at areas of low image redundancy and weak data evidence, as often found in street canyons, facades or building shadows. Furthermore, training such networks is computationally expensive. Thus, the aim of our work is twofold: First, we investigate the applicability of NeRFs for aerial image blocks representing different characteristics like nadir-only, oblique and high-resolution imagery. Second, during these investigations we demonstrate the benefit of integrating depth priors from tie-point measures, which are provided during presupposed Bundle Block Adjustment. Our work is based on the state-of-the-art framework VolSDF, which models 3D scenes by signed distance functions (SDFs), since this is more applicable for surface reconstruction compared to the standard volumetric representation in vanilla NeRFs. For evaluation, the NeRF-based reconstructions are compared to results of a publicly available benchmark dataset for airborne images.

BEYOND GROUND CONTROL POINTS: COST-EFFECTIVE 3D BUILDING RECONSTRUCTION THROUGH GNSS-INTEGRATED PHOTOGRAMMETRY

Abstract. The process of 3D building modeling serves a multitude of practical and strategic purposes across diverse industries. Building a 3D model involves employing a range of techniques and technologies. Among these, the most used methods include 3D laser scanning and photogrammetry, whether applied at close-range or through the use of Unmanned Aerial Systems (UAS). In photogrammetry, ground control points (GCPs) are generally needed to scale and georeference the digital reconstruction process, but it is a timeconsuming practice or sometimes impractical or dangerous. This paper aims to evaluate the efficiency of two integrated devices to perform photogrammetric 3D reconstruction without GCPs. They are both composed by a Sony ZV1 camera coupled with two different RTK/PPK GNSS system: the Emlid Reach RS2 GNSS receiver and the Emlid Reach M2 module with a multi-band GNSS helical antenna. Different sets of images were acquired with the two proposed devices for the lever-arm estimation and to perform the 3D surveying of the Galata monastery historical monument. The accuracy of the process and derived dense point clouds is assessed by comparing them with GCPs and a reference point cloud derived by fusing an UAS and a high-resolution mobile laser scanning point cloud. The ultimate goal is to obtain a 3D building model without the use of GCPs in the process of bundle block adjustment with centimeter accuracy.

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the surface of a multi-use trail, as well as to calculate sediment deposition and generate a flood hazard map in a partially forested region. The eBee X mapping Unmanned Aerial Vehicle (UAV) equipped with the senseFly S.O.D.A. 3D camera and Real-Time Kinematic (RTK) technology flew over the study area of 149 ha in Northern Greece at an altitude of 120 m and achieved a high spatial resolution of 2.6 cm. The specific constellation of fixed-wing equipment makes the use of ground control points obsolete, compared to previous, in most cases polycopter-based, terrain deformation research. Employing the same methodology, two distinct classifications were applied, utilizing the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for analysis. The Geolocation Errors and Statistics for Bundle Block Adjustment exhibited a high level of accuracy in the model, with the mean values for each of the three directions (X, Y, Z) being 0.000023 m, −0.000044 m, and 0.000177 m, respectively. The standard deviation of the error in each direction was 0.022535 m, 0.019567 m, and 0.020261 m, respectively. In addition, the Root Mean Square (RMS) error was estimated to be 0.022535 m, 0.019567 m, and 0.020262 m, respectively. A total of 20 and 30 altitude categories were defined at a 4 cm spatial resolution, each assigned specific ranges of values, respectively. The area of each altitude category was quantified in square meters (m2), while the volume of each category was measured in cubic meters (m3). The development of a Deformation Classification Model for the deck of a trail or forest road, coupled with the computation of earthworks and the generation of a flood hazards map, represents an efficient approach that can provide valuable support to forest managers during the planning phase or maintenance activities of hiking trails and forest roads.

Digitalisierung und Georeferenzierung einer überdimensionalen historischen Karte der Landvogtei Neuland (Winsen/Luhe) mittels Structure-from-Motion (SfM) Photogrammetrie

AbstractThe study of historical maps has gained significant importance in recent years due to their ability to shed light on past geographical and topographic landscapes since they serve as crucial sources for understanding past conditions. They not only provide insights into the geography and topography during the time they were created but also allow for studying long-term changes over time. In this study, an oversized historical map of the Bailiwick of Neuland from the years 1780 to 1790 was discovered in the archives of the municipal building office in Winsen an der Luhe, Germany. The map, measuring approximately 5 m × 2 m, was digitised by photogrammetric methods and subsequently georeferenced. The process involved photographing the map with two different cameras and two UAV systems at the Geodetic Laboratory of HafenCity University Hamburg. This allowed to generate a high-resolution orthophoto from each data set. The resulting orthophoto achieved a pixel size of 0.2 mm, ensuring a detailed representation of the map. To ensure accuracy, the best photo block was scaled in a bundle block adjustment using ground control points with an accuracy of 1 mm and scale bars with an accuracy of 0.1 mm. Georeferencing of the historical map was conducted using current digital orthophotos of Lower Saxony with a resolution of 20 cm. A third-degree polynomial transformation was applied during georeferencing, resulting in mean residuals of 2.5 m at the ground control points. This process ensured that the historical map was accurately aligned with the current digital orthophotos, allowing for precise spatial referencing.

Large-Scale Block Bundle Adjustment of LROC NAC Images for Lunar South Pole Mapping Based on Topographic Constraint

Large-Scale Block Bundle Adjustment of LROC NAC Images for Lunar South Pole Mapping Based on Topographic Constraint

ESTIMATION OF SENSOR OFFSETS FOR A UAV PLATFORM USING TIEPOINTS ONLY

Abstract. In a UAV system, physical sensor offsets occur between an observation sensor and a GPS/IMU sensor which represents the position of UAV. The difference of angle between the GPS/IMU sensor’s axis and the observation sensor’s axis is referred to as boresight angle. The difference in physical position between the two is referred to as lever-arm. It is important to obtain accurate offset values in order to utilize UAVs in rapid mapping manner. Due to the sensor offsets, misalignment error can be caused when generating a mosaic image. Offset values can be measured by using extensive ground control points. However, this is very costly and time-consuming. In this study, we describe an estimation method for sensor offsets using tie-points and re-weighted least square estimation method. The proposed method consists of 5 steps. Firstly, a frame images for the target area were classified into image strips based on the kappa value of initial EOPs (Exterior Orientation Parameters), after which tie points were extracted between adjacent images. Secondly, strip bundle adjustment was performed to update initial EOPs using images with the same flight direction. Thirdly, tie-points between adjacent strips were extracted. Fourthly, block bundle adjustment was performed in all images, using all extracted tie-points. Finally, a mosaic image is generated using the EOPs value to which the estimated sensor offset is applied. From this study, we confirmed that the sensor offset of the platform could be estimated only with the tie-points extracted between adjacent images. And we confirmed that misalignment was adjusted when generating mosaic image. We expect that our research makes UAV system to be operated more fluently.

Principled bundle block adjustment with multi-head cameras

This paper examines the effects of implementing relative orientation constraints on bundle adjustment, as well as provides a full derivation of the Jacobian matrix for such an adjustment, that can be used to facilitate other implementations of bundle adjustment with constrained cameras. We present empirical evidence demonstrating improved accuracy and reduced computational load when these constraints are imposed.

High-resolution high-accuracy orthophoto map and digital surface model of Forni Glacier tongue (Central Italian Alps) from UAV photogrammetry

ABSTRACT This work presents the high-resolution high-accuracy orthophoto map and the Digital Surface Model of Forni Glacier (Italian Alps). These represent the status of the glacier tongue in mid-August 2022 when surveys were carried out with a DJI Phantom 4 RTK drone. The processing was carried out in Leica Infinity, and a 3 cm orthomosaic and a 20 cm Digital Surface Model were generated and made available for analysis of the current status of the glacier, which shows signs of downwasting, with the occurrence of collapsing areas and a rapidly changing proglacial landscape. This work can also be used as a reference to investigate the glacier evolution, also in light of climate change. Accuracy requirements of the deliverables were ensured by combining Post Processed Kinematic and Structure from Motion integrated with bundle block adjustment, and using Ground Control Points and Check Points, to guarantee redundancy and evaluate the geolocation accuracy and precision.

Influence of On-Site Camera Calibration with Sub-Block of Images on the Accuracy of Spatial Data Obtained by PPK-Based UAS Photogrammetry

Unmanned Aerial Systems (UAS) Photogrammetry has become widely used for spatial data acquisition. Nowadays, RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) are the main correction methods for accurate positioning used for direct measurements of camera station coordinates in UAS imagery. Thus, 3D camera coordinates are commonly used as additional observations in Bundle Block Adjustment to perform Global Navigation Satellite System-Assisted Aerial Triangulation (GNSS-AAT). This process requires accurate Interior Orientation Parameters to ensure the quality of photogrammetric intersection. Therefore, this study investigates the influence of on-site camera calibration with a sub-block of images on the accuracy of spatial data obtained by PPK-based UAS Photogrammetry. For this purpose, experiments of on-the-job camera self-calibration in the Metashape software with the SfM approach were performed. Afterward, experiments of GNSS-Assisted Aerial Triangulation with on-site calibration in the Erdas Imagine software were performed. The outcomes show that only the experiment of GNSS-AAT with three Ground Control Points yielded horizontal and vertical accuracies close to nominal precisions of the camera station positions by GNSS-PPK measurements adopted in this study, showing horizontal RMSE (Root-Mean Square Error) of 0.222 m and vertical RMSE of 0.154 m. Furthermore, the on-site camera calibration with a sub-block of images significantly improved the vertical accuracy of the spatial information extraction.

CRBeDaSet: A Benchmark Dataset for High Accuracy Close Range 3D Object Reconstruction

This paper presents the CRBeDaSet—a new benchmark dataset designed for evaluating close range, image-based 3D modeling and reconstruction techniques, and the first empirical experiences of its use. The test object is a medium-sized building. Diverse textures characterize the surface of elevations. The dataset contains: the geodetic spatial control network (12 stabilized ground points determined using iterative multi-observation parametric adjustment) and the photogrammetric network (32 artificial signalized and 18 defined natural control points), measured using Leica TS30 total station and 36 terrestrial, mainly convergent photos, acquired from elevated camera standpoints with non-metric digital single-lens reflex Nikon D5100 camera (ground sample distance approx. 3 mm), the complex results of the bundle block adjustment with simultaneous camera calibration performed in the Pictran software package, and the colored point clouds (ca. 250 million points) from terrestrial laser scanning acquired using the Leica ScanStation C10 and post-processed in the Leica Cyclone™ SCAN software (ver. 2022.1.1) which were denoized, filtered, and classified using LoD3 standard (ca. 62 million points). The existing datasets and benchmarks were also described and evaluated in the paper. The proposed photogrammetric dataset was experimentally tested in the open-source application GRAPHOS and the commercial suites ContextCapture, Metashape, PhotoScan, Pix4Dmapper, and RealityCapture. As the first experience in its evaluation, the difficulties and errors that occurred in the software used during dataset digital processing were shown and discussed. The proposed CRBeDaSet benchmark dataset allows obtaining high accuracy (“mm” range) of the photogrammetric 3D object reconstruction in close range, based on a multi-image view uncalibrated imagery, dense image matching techniques, and generated dense point clouds.

Accuracy Assessment of Establishing 3D Real Scale Model in Close-Range Photogrammetry with Digital Camera

Three-dimensional (3D) real scale models delivered from digital photogrammetric techniques have rapidly increased to meet the requirements of many applications in different fields of daily life. This paper deals with the establishment of a 3D real scale model from a block of images (18 images) that were captured by using Canon EOS 500D digital camera to cover a test field area consisting of 90 artificial target points, 25 of them are ground control points (GCPs) while the remains are checkpoints (CPs). The analytical photogrammetric processes including the calculation of interior orientation parameters (IOPs) of the camera during the camera calibration process, exterior orientation parameters (EOPs) of the camera in each capturing, and the object space (ground) coordinates of the model are calculated simultaneously based on collinearity equation using bundle block adjustment method (BBA). Assessment and validation of the accuracy of the results is an important task in this study that was implemented to determine and analyze the errors of 3D coordinates through linear regression analysis (LRA). Root mean square error (RMSE) is the statistical parameter that was used in the statistical analysis of results. The standard error is another statistical parameter which also used to evaluate the accuracy of locations and rotation angles (EOPs) of cameras. The total RMSE (RMSE)xyz of GCPs is ± 2.530 mm while the total RMSE (RMSExyz) of CPs is ± 2.740 mm. The overall accuracy of the work is 5.000 mm.

Near-automated orthorectification of archived aerial photographs

The proposed objective was to develop a near-automated workflow for orthorectification of archived aerial photographs that overcomes the limited resources problem. Automation was implemented using the oriented fast and rotated brief (ORB) and random sample consensus (RANSAC) for conjugate points detection. Because it is difficult to determine the RANSAC threshold, the RANSAC threshold search for two step triangulation method was developed to find the threshold that resulted in the minimum three-dimensional misclosures at check points from the threshold candidates. For triangulation, the bundle block adjustment (BBA) with a minimal constraint and the absolute orientation were implemented. Although the absolute orientation still requires manual measurement of control points, the required number is usually less than that of traditional BBA, especially for longer strips. The control points were measured in relatively recent high-accuracy orthophotos and the digital elevation model. In the experiments, the archived aerial strip photographs were orthorectified using a differential rectification method with ∼1 m resolution. The first and second orthorectification experiments showed 1.31 m (X), 1.14 m (Y), and 1.88 m (X), 1.51 m (Y) of RMSE. The proposed workflow suggested is significant because it can save cost, time, and labor required for the orthorectification of archived aerial photographs while achieving reasonable accuracy.

APPLICATION OF UAV SWARM SEMI-AUTONOMOUS SYSTEM FOR THE LINEAR PHOTOGRAMMETRIC SURVEY

Abstract. Unmanned Aerial Vehicles (UAVs) are used as stand-alone systems for a variety of purposes from agriculture, and environmental monitoring through architecture, and construction to humanitarian missions. The advantage of UAV is high spatial and temporal resolution but on the other hand, the disadvantage is the small area of cover and time demanding data collection. As a result of technological advancements, the complexity of systems to unprecedented levels these disadvantages can be solved by UAV Swarm systems. A UAV Swarm system is defined as the utilization of more than one UAV that are cooperating together in a semi-autonomous or autonomous manner to achieve a common goal. There are numerous factors in play while designing a system as advanced as a UAV Swarm. In our experiment, we focused on the semi-autonomous concept of creating and deploying a UAV Swarm with three Small UAVs in master-slave architecture for high-resolution fine-scale mapping. We demonstrate the implementation of collective behaviour of UAV swarm for river bed mapping that considers all on-board systems, including high resolution georeferenced aerial photography and navigation using high accuracy GPS. The testing field for this study was a 13.3 ha linear area of Solani River in the Haridwar district of the state of Uttarakhand, India. Images were captured by all three UAVs (one leader and two followers) and 5 ground control points (GCP) were used for geo-referencing. Aerial Triangulation and Bundle Block adjustment were processed by photogrammetric software Pix4DMapper. This UAV swarm mapping concept generates standard accurate geospatial results of 1.24 cm GSD and RMS Error 0.023 meter. Assessing the proposed system's efficiency and accuracy after such processes are taken into account reduces the time and cost manifolds of the UAV surveying.