The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study.

The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of “traffic lights” is introduced. Classification into a given “traffic lights” color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of “traffic lights”, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.

The accuracy of spatial raw data is contingent on a number of factors, including the accuracy of the sensors, their calibration, and direct and indirect referencing. The efficacy of the captured data can be enhanced through the implementation of effective data processing methodologies. The present research is dedicated to the enhancement of combined photogrammetric and LiDAR data.In the contemporary context, the trajectory of the vehicle can be affected by GPS signal jamming, a phenomenon that is attributable to international circumstances. This necessitates a combined adjustment of RGB/CIR and LiDAR data. The proposed method necessitates the utilisation of both sets of data: The study area was imaged using both RGB and CIR technologies, with intensity LiDAR strips also employed to cover the same area. The present approach is founded upon the notion of lidargrammetry (Jayendra-Lakshman and Devarajan, 2013), which uses photogrammetric algorithms for lidar data processing (Rzonca and Twardowski, 2022).The research tool, PyLiGram, generates synthetic images of the LiDAR data, known as lidargrams or renders, applying the same interior orientation parameters (IOPs) as the real camera used to capture the real RGB/CIR images. The synthetic images are centrally projected according to IOPs and predefined external orientation parameters (EOPs). The projection process is reversible by use of unique lidar point identifiers. Points can be projected onto synthetic images, and after processing the same images, points can be intersected to create a new point cloud.The paper sets out the potential of applying photogrammetric methods to the co-adjustment of RGB/CIR and LiDAR data. The process entails the collaborative processing of RGB/CIR and LiDAR images through the utilisation of deep learning matching techniques and common least squares adjustment. The refined EOPs are then utilised to intersect the refined point cloud and enhance the images' positions.A key benefit of this approach is that it has the potential to eliminate the necessity for LiDAR control patches/points or ground control points for RGB/CIR images, depending on the specific case.The process was subjected to a series of trials on multiple datasets, each comprising a point cloud density of approximately 100 points per square metre and images with a ground sample distance (GSD) ranging from 5 to 10 centimetres.

The present paper introduces a novel methodology for LiDAR point transformation and adjustment, grounded in two primary concepts. In the initial phase of the process, LiDAR data are mapped onto synthetic images, known as lidargrams, through the utilization of exterior orientation parameters (EOPs) of a virtual camera. Secondly, unique lidargram point identifiers (ULPIs) are assigned to each LiDAR point, ensuring the preservation of the relationship between specific LiDAR points and their corresponding lidargram projections. This process facilitates the reconstruction of ground points from their respective projections. The integration of these concepts facilitates the alignment and adjustment of blocks of lidargrams, thereby enabling the estimation of novel EOPs. The exchange of arbitrary EOPs and the intersection of the transformed point cloud based on the ULPIs are facilitated by these refined EOPs. The LiDAR data undergo a three-dimensional transformation using photogrammetric algorithms. This is in accordance with the fundamental principles of lidargrammetry. The accuracy of the new approach and its implementation in a research tool were verified on a range of data types, encompassing synthetic, semisynthetic, and real data. By evaluating the approach across a wide range of data sources, the authors were able to assess its effectiveness and reliability in different scenarios. The method’s flexibility is evidenced by its ability to reduce the final 3D root mean square error of discrepancies measured at check points by 30 times in synthetic data tests, 12 times in semisynthetic data tests, and 96 times in real data tests. The quantitative results obtained provide substantial support for the validity of the presented methodology. The efficacy of the proposed method was also evaluated by way of a comparative analysis with a selection of widely utilized LiDAR processing software developed by TerraSolid Ltd.

The increasing number of photovoltaic installations being introduced into various types of landscapes creates a need to assess their impact on how space is perceived. At the same time, there is still a lack of objective, quantitative methods for evaluating changes in landscape perception based on the number of these elements. This study aimed to evaluate the influence of photovoltaic modules on landscape perception and to examine the usefulness of eye-tracking technology for this purpose. The database consists of AI-generated images representing natural areas and areas with varying degrees of urbanization. These scenes were modified by changing the number of photovoltaic elements. Eye-tracking analysis consisted of evaluating the resulting fixation, saccade maps, and heat maps. The results demonstrated a correlation between gaze fixation and the perceived acceptability of renewable energy sources (RES) installations, as well as a relationship between landscape type and the perceived presence of photovoltaic panels. Eye tracking has proven to be a useful tool for quantitative assessment of changes caused by the location of photovoltaic facilities in the surrounding area. The resulting measures can be used to compare different sustainability projects for selected areas.

This paper presents the functionality and research possibilities of an application that is based on two concepts: the use of photogrammetric analysis for LiDAR data processing (lidargrammetry), and the assignments of identifiers to cloud points in order to be able to return to the original points after processing without data loss and redundant processing.The research tool has, thus far, been developed for the implementation of two distinct LiDAR data-enhancement processes. The initial approach involves the altimetric transformation of the LiDAR data (a process that is founded on the principles of stereo model deformation theory), and the second process employs lidargrammetry for the purpose of 3D local point-cloud corrections, global changes, or non-rigid transformation. This is achieved by applying blocks of lidargrams and their subsequent matching and adjustments.