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The article presents a method for determining and evaluating the threat to railway infrastructure from falling trees. The main objective was to identify the degree of threat according to three parameters: the height of tree stands, species composition, and vegetation health. Identification of the threat to individual railway sections would allow the creation of measures that could increase operational safety. Evaluation should be carried out at regular intervals to assess vegetation health during certain seasons. We, therefore, recommend the use of satellite images to provide a data source and regular data updates. The potential benefit is a reduction in railway infrastructure damage and an increase in traffic flow, especially during periods of exceptional weather conditions involving high wind, ice or heavy snow. The new method presented in the paper, based on data for railway infrastructure, law and other data sources in the Czech Republic, determines a Vegetation Threat Index for Railways (VTIR). We selected the Liberec Region as an example case study because of its large spread of wooded areas around railway lines and high incidence of railway traffic accidents. [ABSTRACT FROM AUTHOR]

Standardized delimiting of local climate zones (LCZ) will be better applicable to the urban adaptation to climate change when the ecohydrological properties of LCZ units are known. Therefore, the properties of LCZ units based on the methodology of ecohydrological zoning of the urban landscape, which was created in GIS as a basis for planning blue-green infrastructure of cities in the Czech Republic, are presented in the paper. The goal of this study is to compare approaches and results of our own ecohydrological zonation and standardized LCZ delimiting in the city of Pilsen. Both methodological approaches differ in input data, resolution details and parameters used. The results showed that the areas of the individual LCZ classes show different levels of ecohydrological qualities. Internal heterogeneity of LCZ classes demonstrated by variance of ecohydrological parameters' values can be partly explained by different techniques and data sources for delimitation of both zonations, but by different sets of delimitation criteria. The discussion is held on the importance of terrain slope for supplementing the LCZ classification. A case study can be a stimulus for further development of holistic urban zoning methodologies that would take into account both climatological and ecohydrological conditions. [ABSTRACT FROM AUTHOR]

The growth of international migration and its societal and political impacts bring a greater need for accurate data to measure, understand and control migration flows. However, in the Czech immigration database, the birthplaces of immigrants are only kept in freeform text fields, a substantial obstacle to their further processing due to numerous errors in transcription and spelling. This study overcomes this obstacle by deploying a custom geocoding engine based on GeoNames, tailored transcription rules and fuzzy matching in order to achieve good accuracy even for noisy data while not depending on third-party services, resulting in lower costs than the comparable approaches. The results are presented on a subnational level for the immigrants coming to Czechia from the USA, Ukraine, Moldova and Vietnam, revealing important spatial patterns that are invisible on the national level. [ABSTRACT FROM AUTHOR]

The article presents a process of collecting unstandardized toponyms, in particular urbanonyms (place names denoting objects located in the cadastre of the city), within the territory of two municipalities in the Czech Republic. The collecting process was performed in two phases by crowdsourcing, using a web map application created especially for this purpose. In the first phase (October 2019–September 2020) it was collecting as many unstandardized toponyms as possible. In the second phase (October 2020–January 2021) we focused on the degree of the knowledge of these toponyms among the population living within the studied territory. The interest on the side of the general public was surprising in both phases. In the first phase, over five hundred respondents submitted more than two and a half thousand place names, most of them during the first two weeks. More than nine hundred respondents actively participated in the second phase, thanks to which we received an average of 200 responses for each place name. As regards the motivation of the public, it was most often altruism, patriotism, and curiosity that stimulated them; in the second phase, the element of gamification, embedded into the map application, also had a positive effect. The collected data can be used, for instance, in the activities of local authorities in the process of standardization of place names or as reference data for maps used within the integrated rescue system. [ABSTRACT FROM AUTHOR]

Land use and land cover are continuously changing in today's world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon's entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met. [ABSTRACT FROM AUTHOR]