In this study, LiDAR sensor data were used to identify agricultural land boundaries. This is a remote sensing method using a pulsating laser directed toward the ground. This study focuses on accurately determining the edges of parcels using only the point cloud, which is an original approach because the point cloud is a scattered set, which may complicate finding those points that define the course of a straight line defining the parcel boundary. The innovation of the approach is the fact that no data from other sources are supported. At the same time, a unique contribution of the research is the attempt to automate the complex process of detecting the edges of parcels. The first step was to classify the data, using intensity, and define land use boundaries. Two approaches were decided, for two test fields. The first test field was a rectangular shaped parcel of land. In this approach, pixels describing each edge of the plot separately were automatically grouped into four parts. The edge description was determined using principal component analysis. The second test area was the inner subdivision plot. Here, the Hough Transform was used to emerge the edges. Obtained boundaries, both for the first and the second test area, were compared with the boundaries from the Polish land registry database. Performed analyses show that proposed algorithms can define the correct course of land use boundaries. Analyses were conducted for the purpose of control in the system of direct payments for agriculture (Integrated Administration Control System—IACS). The aim of the control is to establish the borders and areas of croplands and to verify the declared group of crops on a given cadastral parcel. The proposed algorithm—based solely on free LiDAR data—allowed the detection of inconsistencies in farmers’ declarations. These mainly concerned areas of field roads that were misclassified by farmers as subsidized land, when in fact they should be excluded from subsidies. This is visible in both test areas with areas belonging to field roads with an average width of 1.26 and 3.01 m for test area no. 1 and 1.31, 1.15, 1.88, and 2.36 m for test area no. 2 were wrongly classified as subsidized by farmers.

Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data.

Transmission pipelines belong to technical infrastructure, the condition of which is subject to periodic monitoring. The research was to verify whether aerial measurement methods, especially UAV laser scanning, could determine the geometric shape of pipelines with a precision similar to that of terrestrial scanning, adopted as a reference method. The test object was a section of a district heating pipeline with two types of surfaces: matte and glossy. The pipeline was measured using four methods: terrestrial scanning, airborne scanning, UAV scanning and the structure from motion method. Then, based on the reference terrestrial scanning data, pipeline models were created, with which all methods were compared. The comparison made it possible to find that only the UAV scanning yielded results consistent with those of the terrestrial scanning for all the pipes. The differences usually did not exceed 10 mm, sometimes reaching 20 mm. The structure from motion method yielded unstable results. For the old, matte pipes, the results were similar to those of the UAV scan; however, for the new, shiny pipes, the differences were up to 60 mm.

The detection of archeological sites in satellite imagery is often hindered by environmental constraints such as vegetation cover and variability in meteorological conditions, which affect the visibility of subsurface structures. This study aimed to develop predictive models for assessing archeological site visibility in satellite imagery by integrating vegetation indices and meteorological data using machine learning techniques. The research focused on megalithic tombs associated with the Funnel Beaker culture in Poland. The primary objective was to create models capable of detecting archeological features under varying environmental conditions, thereby enhancing the efficiency of field surveys and reducing associated costs. To this end, a combination of vegetation indices and meteorological parameters was employed. Key indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Archeological Index (NAI)—were analyzed alongside meteorological variables such as wind speed, temperature, humidity, and total precipitation. By integrating these datasets, the study evaluated how environmental conditions influence the visibility of archeological sites in satellite imagery. The machine learning models, including logistic regression and decision tree-based algorithms, demonstrated strong potential for predicting site visibility. The highest predictive accuracy was achieved during periods of high soil moisture variability and fluctuating weather conditions. These findings enabled the development of visibility prediction maps, guiding the optimal timing of aerial surveys and minimizing the risk of unsuccessful data acquisition. The results underscore the effectiveness of integrating meteorological data with satellite imagery in archeological research. The proposed approach not only improves site detection but also reduces operational costs by concentrating resources on optimal survey conditions. Furthermore, the methodology is applicable to diverse archeological contexts, enhancing the capacity to locate and document heritage sites across varying environmental settings.

The more and more high resolution of aerial and ground images, as well as high density of laser data cause that they are more and more widely applied in many engineering projects. Given the current technical parameters, it is also possible to map railway infrastructure not only from the ground level but also from airborne locations (photogrammetry, laser scanning). Testing the usefulness of those data in obtaining information about railway infrastructure, and in particular, in detecting rail heads has been a subject of research of this paper authors. The paper presents results of experiments, consisting in verification of existing solutions and testing own algorithms for an automatic extraction of railway rail heads. The tested algorithms of object detecting and locating produced preliminary, satisfying results. The authors believe it to be reasonable to continue their research work.

Shell structures belong to a characteristic group of objects for which it is essential to know the magnitude and distribution of local deformations of the coat. Deformations may be important both in terms of stability, as well as special functions performed by these objects. While survey instruments usually ensure the required accuracy of the measurements, the methods for providing a mathematical description of the shape of shells do not always allow for a correct detection of deformations. This paper compares several methods of approximation potentially useful for making a precise description of the shape of a given shell object. Spline functions, as the most versatile tool in terms of description of objects shape, were adopted as the base method. For comparison purposes, according to the shape of the object in question, kriging and Fourier series methods were selected. Particular attention has been paid to the accuracy aspect, which has enabled deformations to be determined within centimeter or subcentimeter accuracy. Results of the study revealed the greatest overall accuracy of spline functions. This accuracy however decreases quickly when the observations become more irregular. Both, the past and future studies of the authors focus on integrating the best features of the described methods in order to ensure a high accuracy of the created models. The results may be useful not only in surveying but also in other fields associated with the approximation of surfaces based on measurement data.