Assessment is the initial step for experts on geoheritage and geotourism when designating geosites in a certain area. During this process, geologically interesting outcrops, formations and places are examined with the use of different criteria to see if they are suitable for geoconservation and geotourism purposes. A quantitative assessment method—Modified Geosite Assessment Model (M-GAM)—was applied in the study area, which is part of the Bakony–Balaton UNESCO Global Geopark in Hungary. M-GAM uses a weight factor (importance) that expresses the opinion of geotourists about 27 infrastructural, tourism and scientific indicators. This factor was examined by questionnaires at nine geosites in the area. At each site, we determined a unique importance value, which shows significant difference from site-to-site and reflects the opinions of visitors about the geosite. The M-GAM method is originally aimed at applying a common weight on each of the 27 criteria during the assessment of selected sites. While this approach is valid, we demonstrated that the method can be extended because the weights spatially vary and can be used to draw conclusions on geosite management. Practically, the evaluation of the factors obtained in this way offers an individual development plan for every site. The current state of improvement direction, the level of communication and the interpretability of the geo-objects can also be determined. In this way, we can get a more realistic development strategy for the geosites.

Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

Geodiversity is the variety of natural elements that are excluded from biodiversity, such as: geological, geomorphological, and soil features including their properties, systems, and relationships. Geodiversity assessment measures these features, emphasising the characteristics and physical fragility of the examined areas. In this study, a quantitative methodology has been applied in Bakony–Balaton UGGp, Hungary. The Geopark’s area was divided into 2 × 2 km cells in which geodiversity indices were calculated using various data: maps, spatial databases, and elevation models. However, data sources differ significantly in each country: thematic information may not be entirely public or does not have the appropriate scale and complexity. We proposed to use universal data—geomorphons and a watercourse network—derived from Digital Elevation Models (DEMs) to calculate geomorphological diversity. Making a balance between the base materials was also an aim of this research. As sources with different data densities are used, some abiotic elements may be overrepresented, while others seem to have less significance. The normalisation of thematic layers solves this problem: it gives a proportion to each sub-element and creates a balanced index. By applying worldwide accessible digital base data and statistical standardization methods, abiotic nature quantification may open new perspectives in geoconservation.

The Buda Thermal Karst System is located under the densely populated hills of Budapest. One of its caves (Pál-völgy Cave System) is the longest cave of Hungary with 30.1 km. The research was done in this area as a methodological study to estimate karst porosity parameters of aquifers or hydrocarbon reservoirs. In this study two modeling methods are demonstrated. The volumetric modeling method was primarily aimed to determine the macro-scale (>0.5 m) conduit porosity (referred as macroporosity) of the study area as percentage of the incorporating limestone and marl. This method is based on archive survey data (maps and records). Through this method, morphometric parameters and the approximate sizes of the unexplored cave parts were also calculated. The porosity modeling was aimed at the estimation of the meso-scale (0.02–0.5 m) and macro-scale conduit porosity. It is based on rock face measurements near the Pál-völgy Cave System and image analysis. The matching macroporosity estimations of the two methods suggest that the map-based volumetric modeling method can be a useful tool for karstologists and modeling experts to extract as much information as possible from existing cave maps, even if the records of the original survey were lost or scanty. With the matrix porosity data published earlier from this area, and the conduit porosity calculated from the models, hydrological models of the area can be completed.