Photogrammetric Approach Research Articles

Accuracy and Precision of Shallow-Water Photogrammetry from the Sea Surface

Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to perform shallow-water surveys easily and at accessible costs. This work presents a simple, low-cost, and highly portable platform that allows gathering sequential photos and echosounder depth values of shallow-water sites (up to 5 m depth). The photos are then analysed in conjunction with photogrammetric techniques to obtain digital bathymetric models and orthomosaics of the seafloor. The workflow was tested on four repeated surveys of the same area in the Western Mediterranean and allowed obtaining digital bathymetric models with centimetric average accuracy and precision and root mean square errors within a few decimetres. The platform presented in this work can be employed to obtain first-order bathymetric products, enabling the contextual establishment of the depth accuracy of the final products.

Development of a Drone-Based Phenotyping System for European Pear Rust (Gymnosporangium sabinae) in Orchards

Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.

The Potential of Hybrid Data Integration Approaches Based UAV-SfM Photogrammetry in Identifying New Archaeological Features in The Archaeological UR Site

Structure-from-Motion (SfM) Photogrammetry-based unmanned aerial vehicles (UAV) are considered one of the most effective non-destructive techniques to detect features in archaeology. However, relying on only standalone-based photogrammetric approaches is not always valid in revealing new archaeological features and monuments. This study aims to verify the efficiency of the integration-based methodologies of SfM photogrammetry products to improve the detection of new archaeological features in the non-excavated regions of the archaeological site of Ur, south of Iraq. The methodology proposes an automatic workflow to extract 3D topographic models such as Digital Terrain Model (DTM) by Agisoft Metashape (MS) software and orthomosaic images as standard products for UAV-photogrammetry. These have delivered the following Structure-from-Motion and Multi-View Stereo (SfM-MVS) algorithms using an optimized flight planning, data capturing, and data processing pipeline. Stand-alone raster images derived from photogrammetry, such as (slope images, hill shade rasters, etc.), have limitations in archaeology compared to hybrid raster images in detecting covered and unrevealed features. The methodology focuses on fusing multiple rasters extracted from DTM using the QGIS software package analysis tools to detect features that may never be detected from single standalone image rasters. This is because combining multiple rasters of different parameters may highlight new potential features such as paths, temples, etc. The results findings revealed that the fusion methodology of raster images obtained from UAV-SfM Photogrammetry through its standard products and using high precision settings could successfully help to identify many new archaeological features in the undiscovered site in Ur, such as walls and houses, which may date back to the late periods of Ur. The results concluded that this enhances the role of photogrammetry and remote sensing techniques as non-destructive excavation tools to provide a rich database for archaeologists for further analysis and interpretation of undiscovered archaeological sites without destruction. Many new archaeological features were also revealed, and a general digital map of the site was prepared. It also prompts a review of the settlement theories in previous studies in the ancient city of UR and the documentation of excavated archaeological ruins.

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

A Comprehensive Comparison of Photogrammetric and RTK-GPS Methods for General Order Land Surveying

One of the main objectives of modern-day surveying is to maximize the efficiency and accuracy of mapping a landscape for natural features and elevations prior to the start of a construction project. This paper focuses on a comparison between terrestrial and aerial photogrammetry and real-time kinematic global positioning systems (RTK-GPSs) in terms of elevation accuracy, data expenditure, and time for each survey to be completed. Two sites in San Diego County were chosen to be studied with a combined area of about 1.14 acres, and a total station system was used to establish 572 control points between both areas. Two of the three methods investigated produced similar results in elevation and were well within the established standard, as the terrestrial photogrammetry averaged 0.0583 feet of error, the aerial photogrammetry averaged 0.345 feet of error, and the RTK-GPS averaged 0.0432 feet of error when compared to the total station ground truth. If data consumption is not a concern, the terrestrial photogrammetric method should be preferred to the aerial photogrammetric and RTK-GPS methods in topographic mapping and land monitoring due to the increase in time efficiency and in surface model detail while keeping within the Caltrans specified tolerance of error of 0.2 feet. For general order land surveys, the photogrammetric approach utilized with a Looq scanner would provide the most efficient and cost-effective survey while staying within the 0.2 foot tolerance of error. This method also allows for the utmost clarity of the resulting point cloud when analyzing terrain, break lines, or other features in the survey area.

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.

Georeferencing of Satellite Images with Geocoded Image Features

Abstract. Currently, using digital orthophoto map (DOM) and digital elevation model (DEM) as reference to achieve geometric positioning of newly acquired satellite images has become a popular photogrammetric approach. However, this method relies on DOM and DEM data which requires a lot of storage space in practical applications. In addition, for geometric positioning of satellite images, only sparse image feature points are needed as control points. Consequently, for the sake of convenience, the compression of control data emerges as a necessity with significant practical implications. This paper investigates a "cloud control" photogrammetry method based on geocoded image features. The method extracts SIFT feature points from DOMs, and obtains their ground coordinates, then constructs geocoded image feature library instead of DOM and DEM data as control, thus realizing the compression of control data. Experiments conducted on the Tianhui-1, Ziyuan-3 and Gaofen-2 satellite images demonstrate that the proposed method can achieve high-precision geometric positioning of satellite images and greatly reduce the size of the control data. Specifically, with the reduction of the reference data from 180~1248 MB 2 m DOM and 30 m DEM to 5~10 MB geocoded image features, the geopositional accuracies of the test Tianhui-1, Ziyuan-3 and Gaofen-2 images are improved from 3.12 pixels to 1.74 pixels, 3.69 pixels to 1.09 pixels, and 150.93 pixels to 2.67 pixels, respectively.

A photogrammetric approach for real‐time visual SLAM applied to an omnidirectional system

AbstractThe problem of sequential estimation of the exterior orientation of imaging sensors and the three‐dimensional environment reconstruction in real time is commonly known as visual simultaneous localisation and mapping (vSLAM). Omnidirectional optical sensors have been increasingly used in vSLAM solutions, mainly for providing a wider view of the scene, allowing the extraction of more features. However, dealing with unmodelled points in the hyperhemispherical field poses challenges, mainly due to the complex lens geometry entailed in the image formation process. To address these challenges, the use of rigorous photogrammetric models that appropriately handle the geometry of fisheye lens cameras can overcome these challenges. Thus, this study presents a real‐time vSLAM approach for omnidirectional systems adapting ORB‐SLAM with a rigorous projection model (equisolid‐angle). The implementation was conducted on the Nvidia Jetson TX2 board, and the approach was evaluated using hyperhemispherical images captured by a dual‐fisheye camera (Ricoh Theta S) embedded into a mobile backpack platform. The trajectory covered a distance of 140 m, with the approach demonstrating accuracy better than 0.12 m at the beginning and achieving metre‐level accuracy at the end of the trajectory. Additionally, we compared the performance of our proposed approach with a generic model for fisheye lens cameras.

Integrating remote sensing and photogrammetric approaches to studying a fortified settlement along the Limes Arabicus: Umm ar‐Rasas (Amman, Jordan).

Integrating remote sensing and photogrammetric approaches to studying a fortified settlement along the Limes Arabicus: Umm ar‐Rasas (Amman, Jordan).

Investigation of the use of topographic data derived from Pléiades imagery for high-resolution hillslope-scale morphometry

Obtaining accurate representations of the Earth surface topography is paramount in many geomorphological investigations. While regional scale studies use Digital Elevation Models (DEM) with resolution of the order of 10–100 m, in many applications resolution <5 m are mandatory to appropriately represent landforms and processes of interest. In particular, hillslopes, which cover the most of continental surfaces, have typical length of the order of 100 m. For that reason, high resolution topographic data are mandatory to correctly represent their morphology. The massive use of higher resolution topographic data has been enabled by a vast array of methodological developments, among which terrestrial and airborne Light Detection And Ranging (LiDAR) or uncrewed aerial vehicle-based photogrammetric approaches are the most commonly used. However, such techniques might be difficult to deploy and not cost-effective, to cover large areas in remote locations.In this study, we present an exploration of the potential of topographic data derived from Pléiades imagery for the characterizing hillslope morphology. We focus on the Gabilan Mesa area in California, where a LiDAR DEM is available and is used as a benchmark. We systematically compare Pléiades-derived topographic data with the DEM constructed from LiDAR data, starting from simple elevation analysis and then using topographic derivatives, such as slope or curvature, which are commonly used in morphometric studies. Finally, we take advantage of the high resolution of the datasets to extract and compare hillslope-scale morphometric parameters, such as length, relief, or hilltop curvature. While some particular situations, such as planar and horizontal surfaces, requires specific attention in terms of the relative differences between the two type of data, the absolute values for the metrics are in good agreement over their relevant range of utilization for hillslope-scale geomorphology.

Supporting Screening of New Plant Protection Products through a Multispectral Photogrammetric Approach Integrated with AI

This work was aimed at developing a prototype system based on multispectral digital photogrammetry to support tests required by international regulations for new Plant Protection Products (PPPs). In particular, the goal was to provide a system addressing the challenges of a new PPP evaluation with a higher degree of objectivity with respect to the current one, which relies on expert evaluations. The system uses Digital Photogrammetry, which is applied to multispectral acquisitions and Artificial Intelligence (AI). The goal of this paper is also to simplify the present screening process, moving it towards more objective and quantitative scores about phytotoxicity. The implementation of an opportunely trained AI model for phytotoxicity prediction aims to convert ordinary human visual observations, which are presently provided with a discrete scale (forbidding a variance analysis), into a continuous variable. The technical design addresses the need for a reduced dataset for training the AI model and relating discrete observations, as usually performed, to some proxy variables derived from the photogrammetric multispectral 3D model. To achieve this task, an appropriate photogrammetric multispectral system was designed. The system operates in multi-nadiral-view mode over a bench within a greenhouse exploiting an active system for lighting providing uniform and diffuse illumination. The whole system is intended to reduce the environmental variability of acquisitions tending to a standard situation. The methodology combines advanced image processing, image radiometric calibration, and machine learning techniques to predict the General Phytotoxicity percentage index (PHYGEN), a crucial measure of phytotoxicity. Results show that the system can generate reliable estimates of PHYGEN, compliant with existing accuracy standards (even from previous PPPs symptom severity models), using limited training datasets. The proposed solution addressing this challenge is the adoption of the Logistic Function with LASSO model regularization that has been shown to overcome the limitations of a small sample size (typical of new PPP trials). Additionally, it provides the estimate of a numerical continuous index (a percentage), which makes it possible to tackle the objectivity problem related to human visual evaluation that is presently based on an ordinal discrete scale. In our opinion, the proposed prototype system could have significant potential in improving the screening process for new PPPs. In fact, it works specifically for new PPPs screening and, despite this, it has an accuracy consistent with the one ordinarily accepted for human visual approaches. Additionally, it provides a higher degree of objectivity and repeatability.

Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach

The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique “Structure from Motion” (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an R^2 of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao’s Q, Coefficient of Variation, Berger–Parker index, and Simpson’s D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao’s Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

Оценка деформационных процессов фотограмметрическим способом в Agisoft Metashape

Active development of digital cameras and computer software has made close-range photogrammetry an extremely popular way to solve various engineering tasks, in particular, to control deformations of constructions and mining objects. Automated measurement of tie points enables reviving traditional techniques of deformation measurement through single shot photogrammetric approach (a zero basis, pseudo parallaxes) at a qualitatively new level. The paper deals with the contour mapping equal displacement method adaptation offered by the first author in the 1970s for processing in Agisoft Metashape. It is proposed to create a virtual basis for two single multi-temporal images by adding a matrix of empty pixels to them. This enables processing those as stereo pair, building virtual point clouds and altitude matrices. In order to test the technique, the authors considered the deflection deformation of various objects under lab conditions and shot their photos with a non-metric camera before and after loading. The obtained images were processed in Metashape, and then the above products were created and contours of equal deformations were plotted. It was found out that the accuracy of displacement value estimation with this method exceeds the spatial resolution of the images more than three times. The technology is applicable for objects deformations monitoring shifting mainly in a single plane, in future it is planned to test it in field at studying slope processes

Photogrammetric survey and spatial recording of settlement features at al-Jumayil (Jordan). 3D terrain model, DEM, orthographic map, and ground plan

This paper discusses the modality of the digital surface documentation of the archaeological site of al-Jumayil during the 2021 excavation campaign organised by the Institute of Classical Archaeology of the University of Vienna. A digital terrain model encompassing an area of 23,783.4 m2 was created using the photogrammetric range imaging technique SfM (Structure from Motion). It is based on 12,983 orthographic photographs. The resulting high-resolution 3D model – a snapshot in time displaying the current condition of the visible structures related to historic al-Jumayil – laid the foundation for further off-site data processing. In addition to the three-dimensional model, a digital elevation model (DEM), a georeferenced orthographic map, and a ground plan containing all visible wall structures of the settlement was produced from the collected data. This case study aims to present the in-field application of a systematic photogrammetric approach used as a fast and accurate tool for mapping an archaeological site step-by-step in two and three dimensions within a topographic survey.

Validation of a photogrammetric approach for the objective study of early bowed instruments

Some early violins have been reduced during their history to fit imposed morphological standards, while more recent ones have been built directly to these standards. We propose an objective photogrammetric approach to differentiate between a reduced and an unreduced instrument, whereby a three-dimensional mesh is studied geometrically by examining 2D slices. Our contribution is twofold. First, we validate the quality of the photogrammetric mesh through a comparison with reference images obtained by medical imaging, and conclude that a sub-millimetre accuracy is achieved. Then, we show how quantitative and qualitative features such as contour lines, channel of minima and a measure of asymmetry between the upper and lower surfaces of a violin can be automatically extracted from the validated photogrammetric meshes, allowing to successfully highlight differences between instruments.Graphical

Multispectral’s Three-Dimensional Model Based on SIFT Feature Extraction

Recently, multispectral images can be captured not only from satellite sensors but also from cameras. Hence, using the photogrammetric approach, multispectral images can be manipulated to generate a three-dimensional model. The main issues regarding multispectral images were the low visibilities of the image features. Moreover, the tie point extractions on multispectral images were still in doubt. Hence, this paper examines the capabilities of the SIFT algorithm to extract feature points from multispectral images and generate the point cloud from the extracted feature points. This study chose a pothole as the subject of this research. The red, red edge, green, and near-infrared bands from the Parrot Sequoia camera were used to generate the pothole model. All captured images were processed using structure-from-motion (SfM) with Multi-View Stereo (MVS) technique. This study records the feature points extraction result and analysis of the pothole model and discuss it in this paper.

The 1965 Mahavel Landslide (Réunion Island, Indian Ocean): Morphology, Volumes, Flow Dynamics, and Causes of a Rock Avalanche in Tropical Setting

AbstractIn May 1965, a main landslide occurred in a deeply incised valley of Piton de la Fournaise volcano, in Réunion Island. This event occurred one day after heavy rainfalls and was consequently interpreted as a mud/debris flow. We take advantage of several sets of historical photographs to reappraise this event. They show that the collapse of a large part of the valley headwall produced a rock flow that dropped 1,740 m and traveled 5,050 m. The surface morphology suggests that the landslide produced a main flow that evolved in a secondary flow in the distal part. The main flow deposit is composed of a matrix‐rich fragmented facies and a jigsaw‐fracturated facies. The matrix presents a gravely‐to‐silty granulometry and fractal dimensions ranging between 2.472 and 2.865. Volumes of the collapsed material (59.7 ± 3.1 Mm3) and the deposit (46 ± 13 Mm3) were determined from a photogrammetric approach. We estimated velocities ranging between 86 and 44 m s−1 along the flow path. Our simulations with SHALTOP reveal that the landslide geometry and velocities are well reproduced with frictional Coulomb rheology. We thus interpret the 1965 Mahavel landslide as a main rock avalanche rather than a mud/debris flow. We propose that the trigger of the 1965 avalanche, and of the smaller events in 1995 and 2001, is the water infiltration related to the intense rainfalls, which systematically preceded by one day each collapse. Finally, the succession of dry and wet years before each collapse event could promote favorable conditions to failure.

An Enhanced Photogrammetric Approach for the Underwater Surveying of the Posidonia Meadow Structure in the Spiaggia Nera Area of Maratea.

The Posidonia oceanica meadows represent a fundamental biological indicator for the assessment of the marine ecosystem's state of health. They also play an essential role in the conservation of coastal morphology. The composition, extent, and structure of the meadows are conditioned by the biological characteristics of the plant itself and by the environmental setting, considering the type and nature of the substrate, the geomorphology of the seabed, the hydrodynamics, the depth, the light availability, the sedimentation speed, etc. In this work, we present a methodology for the effective monitoring and mapping of the Posidonia oceanica meadows by means of underwater photogrammetry. To reduce the effect of environmental factors on the underwater images (e.g., the bluish or greenish effects), the workflow is enhanced through the application of two different algorithms. The 3D point cloud obtained using the restored images allowed for a better categorization of a wider area than the one made using the original image elaboration. Therefore, this work aims at presenting a photogrammetric approach for the rapid and reliable characterization of the seabed, with particular reference to the Posidonia coverage.

Underwater 3D Reconstruction from Video or Still Imagery: Matisse and 3DMetrics Processing and Exploitation Software

This paper addresses the lack of “push-button” software for optical marine imaging, which currently limits the use of photogrammetric approaches by a wider community. It presents and reviews an open source software, Matisse, for creating textured 3D models of complex underwater scenes from video or still images. This software, developed for non-experts, enables routine and efficient processing of underwater images into 3D models that facilitate the exploitation and analysis of underwater imagery. When vehicle navigation data are available, Matisse allows for seamless integration of such data to produce 3D reconstructions that are georeferenced and properly scaled. The software includes pre-processing tools to extract images from videos and to make corrections for color and uneven lighting. Four datasets of different 3D scenes are provided for demonstration. They include both input images and navigation and associated 3D models generated with Matisse. The datasets, captured under different survey geometries, lead to 3D models of different sizes and demonstrate the capabilities of the software. The software suite also includes a 3D scene analysis tool, 3DMetrics, which can be used to visualize 3D scenes, incorporate elevation terrain models (e.g., from high-resolution bathymetry data) and manage, extract, and export quantitative measurements for the 3D data analysis. Both software packages are publicly available.

Machine Learning Based Position Prediction of a Target Tracked by Multi-Aperture Positioning System

This paper proposes a machine learning-based position prediction approach to determine the position of a light-emitting diode (LED) target using a new measuring system called the multi-aperture positioning system (MAPS). The measurement system is based on a photogrammetric approach using an aperture mask and a single camera sensor. To achieve high accuracy in position calculation, several complex algorithms with high computational complexity are used. The accuracy of the system is equal to or better than that of existing photogrammetric devices. We investigate whether a neural network (NN) can replace the algorithms currently used in the system software to increase the measurement frequency with similar accuracy. Simulated images are used to train the NN, while real images are used to measure performance. Previously, various algorithms were used to calculate the position of the target from the captured images. Our approach is to train an NN, using thousands of labeled images, to predict the position of the target from these images. We investigate whether systematic measurement errors can be avoided; not all factors affecting the measurement precision are yet known, can always be accurately determined, or change over time. When NNs are used, all information contained in the images is learned by the model, considering all influences present at the time of training. Results show that the trained NN can achieve similar performance to the previously used Gaussian algorithm in less time since no filters or other pre-processing of images are required. This factor directly affects the measurement frequency of the MAPS. The light spot center was detected with sub-pixel accuracy without systematic errors in contrast to some of the previously used algorithms. The simulation of the sensor images needs to be improved to investigate the full potential of the NN.