This research seeks to develop and test a rapid mapping approach using unmanned aerial vehicles (UAVs) and terrestrial laser scanning to provide precise, high-resolution spatial data for urban areas right after disasters. This mapping aims to support efforts to protect the population and infrastructure while analyzing the situation in affected areas. It focuses on flood-prone regions lacking modern hydrological data and where regular monitoring is absent. This study was conducted in resort villages and adjacent catchments in Bulgaria’s southern Black Sea coast with leading maritime tourism features, after a flash flood on 5 September 2023 caused human casualties and severe material damage. The resulting field data with a spatial resolution of 3 to 5 cm/px were used to trace the effects of the flood on topographic surface changes and structural disturbances. Flood simulation using UAV data and a digital elevation model was performed. The appropriateness of contemporary land use forms and infrastructure location in catchments is discussed. The role of spatial data in the analysis of genetic factors in risk assessment is commented on. The results confirm the applicability of rapid mapping in informing the activities of responders in a period of increased vulnerability following a flood. The results were used by Bulgaria’s Ministry of Environment and Water to analyze the situation shortly after the disaster.

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.

Spatial accessibility of healthcare facilities may result in substantial differences in the population’s access to medical care. This is a topic of great public importance that needs effective planning based on contemporary spatial technologies and analytical approaches. The aim of the present study is to demonstrate how the application of spatial analyses, tailored to the specific context, contributes to identifying gaps in the existing organization of emergency medical services in mountainous areas and enables a more comprehensive and robust assessment of transport accessibility. By integrating three complementary analytical approaches—Service Area, Closest Facility, and Origin–Destination Cost Matrix analyses within a single methodological framework, the present study provides a comprehensive assessment of travel times from emergency medical care branches to the settlements they serve, as well as an evaluation of branch service areas in terms of their spatial extent. The analyses identified shortcomings that could be addressed with reorganization and low-cost optimization measures, thus demonstrating their efficiency in identifying limitations in the current allocation of service areas among the emergency medical care branches. The study is based on carefully selected data and was conducted with a strong emphasis on maximizing both accuracy and practical applicability.

Reliable detection and mapping of burned areas are critical for managing the growing impact of fires on agricultural land and natural ecosystems. In southern and eastern Europe, fires cause economic losses and environmental damage through deforestation, habitat loss and carbon emissions, while disrupting ecosystem services, accelerating soil erosion and altering soil moisture and microclimate in ways that hinder recovery. Addressing these consequences requires effective, spatially explicit tools for monitoring, post-fire recovery, land-use planning, and climate-change adaptation. This study proposes a methodological approach for operational burned-area mapping based on Sentinel-2 data, designed for heterogeneous landscapes, where coarse-resolution products systematically under-report burns across mixed forest, shrub, grassland and agricultural cover. The core is a new composite multispectral index, NBR_BG, embedded in a three-component pixel rule combining pre-fire vegetation state, post-fire spectral signal and their signed difference. The rationale is that the classical Normalised Burn Ratio (NBR) and the Šebela SWIR-difference index have complementary limitations, and that adding pre-fire vegetation state as a third axis suppresses false positives typical of heterogeneous landscapes. The method was developed and validated on 96 candidate NBR_BG detections (94 confirmed as real fires) across two Sentinel-2 Military Grid Reference System (MGRS) tiles in Bulgaria, reaching an F1-score of 98.43% on the field-verified sample. Applied without recalibration to a nationwide Bulgarian assessment for August 2023, it mapped 639 fires, demonstrating scalability. The approach offers a practical, CPU-tractable tool aligned with Land Use, Land-Use Change and Forestry (LULUCF) reporting and land-use planning under Intergovernmental Panel on Climate Change (IPCC) guidance.