English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Detailed weather and climate modeling for urban areas is in demand in various scientific and applied tasks, starting from a numerical weather forecast and ending with an assessment of the bioclimatic conditions of the city and long-term urban planning. The application of modern meteorological models for urban areas requires the determination of a set of parameters characterizing the urban environment and urban canopy features. One of these parameters is the area fraction, occupied by buildings. In this paper, we propose a universal method for determining building area fraction based on the interpretation of high-resolution satellite images from the Sentinel-2 satellites. The methodology was tested on the example of the territory of the city of Moscow, characterized by a variety of forms of urban development. The calculation of the building area fraction was performed for the cells of the computational grid of the COSMO mesoscale numerical meteorological model with 1 km spacing. To verify the developed method, we used an alternative estimate of the building area fraction based on the crowdsourcing cartographic data OpenStreetMap. The data on the building area fraction derived from the satellite images and from OpenStreetMap data have shown a good mutual agreement, which confirms the promise of using the proposed methodology. On the example of territories where the two methods show significant differences, their typical causes are identified, namely the lack of information about buildings in the OpenStreetMap database, or the masking of buildings by trees, which does not allow them to be revealed from satellite images.

Experience in the determining the building area using satellite images for the purposes of meteorological modeling (case of Moscow city)