## 1 Introduction
UAS can rapidly obtain high-resolution hyperspectral images, but complex geometric and radiometric corrections are required, which are particularly important in geological applications such as raw material detection. The authors presented a new toolbox for processing drone-borne hyperspectral data, including automatic co-registration, mosaicking, georeferencing, and topographic and illumination correction. For the first time, the usefulness of such data for geological studies was demonstrated. The authors of [32] discuss the application of UAVs and machine learning algorithms for topographic modeling and the classification of a detailed geological model, using a case study of a phosphate mine in Brazil. An automated classification model between lithological groups was developed. The authors of [33] present the first application of UAVs in geological research on carbonates in inaccessible and hazardous outcrops. The authors proposed a method used to document the spatial distribution and dimensions of diagenetic dolomite geobodies in a Carboniferous limestone host rock. They emphasized the need for further work on drone remote control, increasing payload capacity, and improving software flexibility. An example of using drones for lithological and structural geological mapping in mining areas was described in [34]. The authors clearly highlight the significant improvements in geological operations through UAV applications, which enhance reliability, safety, and efficiency. The acquired imagery was processed using spectroscopic algorithms and machine learning to generate meaningful 2.5D surface maps. Ground-based techniques and UAVs were used to obtain photogrammetry and hyperspectral VNIR, SWIR, and LWIR images. The developed method allowed for much more efficient ground surveys and the creation of an optimal sampling strategy for further structural, geochemical, and petrological studies. Although drones are commonly used in photogrammetry, their use in geological studies faces challenges related to geometric and radiometric correction. The authors in [35] demonstrated that precise corrections are essential for geological mapping and presented a tool for processing drone-borne hyperspectral data, including geometric and topographic corrections. This was the first time the effectiveness of such data in lithological mapping and mineral exploration was proven. As mentioned in [36; 37], point clouds collected by drones can be used to gather, process, analyze, and interpret geological and geotechnical data. These can be applied to rock outcrop analysis and discontinuum modeling. Point clouds obtained from rock outcrops provide insights into fracture orientation, roughness, persistence, and spacing. Furthermore, rock mass quality indicators (e.g., Rock Quality Designation--RQD, or Geological Strength Index--CSI) can be determined. The authors of [38] presented the use of drones for the geotechnical characterization of rock masses in underground mines, where access to unsupported workings is hazardous for personnel. Photogrammetric and thermal imaging (FLIR) techniques were used for structural rock analysis and the identification of loose fragments that may pose a hazard. The research was conducted at the Barrick Golden Sunlight Mine, where UAV flights generated 3D models and collected geological data. The results confirm that using available technologies can be an effective geotechnical tool in underground environments. UAVs have wide applications in search, planning, and rescue operations, which can also be successfully utilized in mining [39; 40]. In [41], the authors present a solution for detecting coal fires, monitoring their progress, and identifying potential environmental hazards and risks to local communities. The case study focuses on the Jharia coalfield in India, where fires have been present since mining began in the mid-19 th century, both on the surface and underground.