This work aims to create new scientific knowledge by developing new innovative technology for remote detection of unknown underground cavities and deep-seated rockslides. To achieve the goal, we intend to develop new innovative technology for remote localization of unknown underground cavities and deep-seated rockslides using a thermal camera mounted on a unmanned aerial vehicle (UAV). Technology is a defined sequence of operations and procedures under optimal conditions, varying within certain allowable limits, resulting in obtaining a particular result or product that meets certain requirements. Therefore, the development of new technology involves determining the allowable limits of deviation of the optimal conditions of operations and procedures for producing a good result that meets the established requirements. The development of new technology also involves determining the limits of its applicability under different external conditions. This paper includes determination of the allowable limits and the limits of applicability of the new technology being developed. Until now, no technology has been developed for remote detection of unknown underground cavities or deep-seated rockslides. The goal of this work is the development of such innovative technology with numerous applications in construction, environmental studies and protection, security, defence and infrastructure.

Obtaining three-dimensional data for the surface and underground cavities is a complex task that includes using scanning systems to collect accurate data, as well as their processing. In recent years, Unmanned Aerial Systems (UAS) have been very successfully used for capturing geospatial data for ground surface and orthophoto mapping. On the other hand, the use of UAS for underground scanning is a difficult task due to the limited spaces, the lack of GNSS coverage and the low level of illumination in underground terrains. In this regard, the use of Terrestrial Laser Scanning (TLS) is crucial. This work looks for intersection between these two types of scanning systems (UAS as a system for scanning the earth’s surface and LiDAR as a system for scanning underground surfaces) considered in the context of studying cavities and the ground surface above them as well as the possibilities for integration of data obtained from them.

Urban rooftop photovoltaic systems represent a substantial yet still underutilized renewable energy resource, particularly in high-density residential environments. Accurate large-scale assessment of rooftop solar potential, however, remains challenging due to the complex geometry of urban morphology and the limited availability of high-resolution geospatial data. This study presents a large-scale methodological framework for estimating the theoretical photovoltaic potential of urban rooftop spaces using Unmanned Aerial System (UAS)-based digital photogrammetry and GIS-based spatial analysis. The approach integrates centimeter-resolution Digital Surface Models (DSMs) and orthophotos derived from fixed-wing UAS surveys with detailed rooftop vectorization and solar radiation modeling implemented in a GIS environment. The methodology accounts for rooftop geometry, surface orientation, slope, shading effects, and rooftop-mounted obstacles. The methodology consists of data collection of high-resolution RGB imagery suitable for detailed three-dimensional reconstruction. The images are captured with a UAS equipped with a S.O.D.A. 3D photogrammetric camera, creating a dense, georeferenced three-dimensional point cloud based on UAS imagery. Based on the point cloud, a high-resolution Digital Surface Model (DSM) was produced. Rooftop boundaries and rooftop-mounted structures were digitized on the basis of an orthophoto created from UAS imagery. The analysis workflow consists of solar modeling using ArcGIS Pro, including calculating the solar radiation. The next methodological step is to filter low radiation rooftops, steep slopes, and northern-oriented rooftops. Finally, we calculate the potential electricity production. The framework was applied to high-density residential districts in Sofia, Bulgaria, dominated by prefabricated panel buildings with predominantly flat rooftops. Drone applications in such studies are typically restricted to modeling individual roofs, which severely limits their scalability for district-wide evaluations. To overcome this, the study employs a specialized fixed-wing UAS uniquely certified for legal operations over densely populated urban environments. This platform rapidly maps large territories, ensuring consistent lighting and shading conditions that significantly enhance the accuracy of subsequent rooftop digitization. Furthermore, the resulting centimeter-level precision enables the exact vectorization of micro-rooftop obstacles. Capturing these intricate details is a critical innovation that effectively prevents the overestimation of solar energy potential commonly observed in conventional large-scale models. Solar radiation was modeled at the pixel level for a full annual cycle and filtered using photovoltaic suitability criteria, including minimum annual radiation thresholds, slope, and aspect constraints. Theoretical electricity production was subsequently estimated using zonal statistics and system performance parameters representative of contemporary photovoltaic installations. The results indicate a total theoretical annual electricity potential of approximately 76.7 GWh for the analyzed rooftop spaces, with an average production of about 34 MWh per rooftop and pronounced spatial variability driven by rooftop geometry and exposure conditions. The findings demonstrate the significant renewable energy potential embedded in existing urban rooftop infrastructure and highlight the applicability of UAS-based photogrammetry for high-resolution, large-area solar potential assessments. The proposed framework provides actionable information for urban energy planning, municipal solar cadaster development, and the strategic integration of photovoltaic systems into dense urban environments, particularly in regions lacking open-access high-resolution geospatial datasets.

The purpose of this investigation is to review the currently known studies on the meteorological preconditions of COVID-19, and analyze and synthesize the results from the existing research, with a view to further substantiating a methodological approach to assessing the daily meteorological factors of viral particles detention in the open air. In pursuit of this goal, the present study attempts to derive and differentiate some findings of the recent research from all over the world about weather and climate factors of SARS-COV-2 dispersion in the atmosphere. The results are systematized according to the main climatic elements – air temperature, air humidity, solar radiation and wind speed. Some climatically sensitive social-demographic factors (such as gender, age structure, general morbidity and predisposition to chronic diseases, household conditions, personal habits, educational level and so on) that determine the resistance to COVID-19, are also commented in this study. Last but not least, the issue of scaling up and remote approaches to the study of the climate and COVID-19 relationship was also addressed.