Using Unmanned Aerial Vehicles (UAVs) combined with various sensors brings the benefits associated with fast, automatic, and contactless spatial data collection with high resolution and accuracy. The most frequent application is the possibility of effectively creating spatial models based on photogrammetric and lidar data. This review analyzes the current possibilities of UAVs. It provides an overview of the current state of the art and research on selected parameters regarding their history and development, classification, regulation, and application in surveying with creating spatial models. Classification and regulation are based on national sources. The importance and usability of this review are also carried out by analyzing the UAV application with selected photogrammetric and lidar sensors. The study explores and discusses results achieved by many authors in recent years, synthesizing essential facts. By analyzing the network of co-occurring High-Frequency Words, in addition, we visualized the importance of the primary keyword UAV in the context of other keywords in the literary sources processed.

Thanks to the development of geodetic methods and equipment, there has been a transition from conventional methods to modern technologies, which can efficiently and accurately acquire a large amount of data in a short time without the need for direct contact with the measured object. Combined technologies such as Structure from Motion (SfM), Multi-View Stereo (MVS) photogrammetry using Unmanned Aerial Systems (UAS), and terrestrial laser scanning (TLS) are often used for monitoring geohazards and documenting objects in quarries to obtain detailed and accurate information about their condition and changes. This article deals with the analysis of point clouds obtained with different settings in terms of average absolute point distance, average point density, and time range for surveying and office work. The numerical and graphical results of the research lead to conclusions for scientific and practical applications for activities in the mining industry.

This article focuses on the assessment of point clouds obtained by various laser scanning methods as a tool for 3D mapping and Digital Twin concepts. The presented research employed terrestrial and mobile laser scanning methods to obtain high-precision spatial data, enabling efficient spatial documentation of urban structures and infrastructure. As a reference method, static terrestrial laser scanning (TLS) was chosen. Mobile laser scanning (MLS) data obtained by devices such as Lidaretto, the Stonex X120GO laser scanning device, and an iPhone 13 Pro with an Emlid scanning kit and GNSS antenna Reach RX were evaluated. Analyses based on comparing methods of classification, differences in individual objects, detail/density, and noise were performed. The results confirm the high accuracy of the methods and their ability to support the development of digital twins and smart solutions that enhance the efficiency of infrastructure management and planning.

The alpine environment with a high degree of nature protection is characterized by complete non-intervention. The processes and phenomena occurring in it are exclusively of a natural origin. Related geohazards are threatening the safety of people’s movement. They arise as a result of a combination of meteorological, hydrological, and geological–morphological factors permanently operating in the country. Therefore, the prevention of fatal events is limited to monitoring and predicting changes in selected objects where we expect change. Changes in the shape and dimension, or the object’s deformation, can be documented using geodetic and photogrammetric measurements. Our research focuses on monitoring a rock talus cone in High Tatras, Slovakia, at an altitude of 1700 m above sea level (ASL), created mainly due to erosion and seasonal torrential rains. To monitor changes in selected objects, we used mass non-contact methods of terrestrial laser scanning (TLS), UAS photogrammetry based on the principle of structure-from-motion–multi-view stereo (SfM–MVS), and airborne laser scanning (ALS). From the selective measurement methods, spatial measurement by a total station (TS) and height measurement based on the principle of precise leveling were used in the monitoring deformation network on a stand-alone boulder. The research results so far analyze and evaluate the possibilities, limits, effectiveness, and accuracy of the measurement and data processing methods used. As a result, we propose a complex methodology for monitoring similar phenomena in alpine environments.

Quarries represent critical sites for raw material extraction, for which regular monitoring and mine surveying documentation, along with its updating, is essential to ensuring safety, environmental protection, and effective management of the mining process. This article aims to evaluate the modern approach to quarry surveying and the creation of a base mining map using advanced laser scanning methods, such as terrestrial laser scanning (TLS) and simultaneous localization and mapping (SLAM)-based mobile laser scanning (MLS). Particular attention is given to the analysis of noise generated using TLS and SLAM-based MLS methods. An analysis of mutual differences between point clouds is presented to compare the spatial accuracy of the point clouds obtained using MLS technology against those from the reference TLS method on both horizontally and vertically oriented test areas. To assess the quality and usability of data obtained using the TLS and MLS methods, a selected section of the mining wall was analyzed based on the distance between points (Cloud-to-Cloud analysis), cross-section analysis, and volume calculations based on 3D mesh models generated from stage edges and point clouds. The findings offer valuable insights into the effective use of each method in quarry surveying, contributing to the development of innovative approaches to spatial data collection as a base for creating Digital Twins of quarries. The article also evaluates the efficiency of both measurement approaches in terms of accuracy, measurement speed, and practical applicability in mining practices. The results show that the point cloud obtained by the TLS Leica RTC360 device, compared to that by the MLS method using the FARO Orbis device (FARO Technologies, Inc., Lakemary, FL, USA), achieves better values in terms of average noise level, standard deviation, interval of highest point density, and RMSD (Root Mean Square Deviation) in test areas. Our conclusions highlight the high potential of laser scanning for the modernization of mining documentation and the improvement of surveying processes in the smart mining industry, particularly for updating Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), Digital Surface Models (DSMs), and other 3D models of quarries for the creation of their Digital Twins.

Mapping and assessing Blue-Green Infrastructure (BGI) is crucial for identifying opportunities to enhance urban planning strategies that support ecological health and im-prove the quality of urban living. This study presents a novel methodology for BGI mapping and evaluation, leveraging freely available datasets in Slovakia integrated with advanced GIS tools, including deep learning methods. The proposed approach combines Open-StreetMap (OSM), LiDAR data, and orthophoto mosaics to provide a comprehensive frame-work for assessing urban BGI. The first section evaluates green and blue areas in the study area, highlighting differences in dataset quality. OSM data underrepresented green spaces, while water bodies were reasonably well-represented. Orthophotomosaics, analyzed using deep learning, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), offered more precise results, with deep learning achieving higher ac-curacy but requiring significant computational resources. LiDAR data accurately identified trees and shrubs but was less effective in mapping water bodies. In the second phase, we developed a BGI index by integrating the analyzed datasets and using object extraction techniques from the mentioned datasets. The final BGI index of 0.41 places the study area on the border between "Moderate" and "Good" BGI quality. This result suggests that BGI quality can be enhanced through urban interventions such as green roofs and permeable surfaces. The novelty of this study lies in the creation of a new methodology for BGI mapping in Slovakia using freely accessible datasets, providing a replicable model for similar re-gions. This work contributes to the field by laying the foundation for future research aimed at mapping and evaluating BGI.