The article considers examples of mapping urban areas using remote sensing data (Landsat 4-5, 7, 8 and Sentinel 2) based on the QGIS open information system. On the basis of several projects carried out at the Institute of Earth Sciences of St. Petersburg State University, the main steps of remote sensing data processing and the use of GIS are shown, as well as significant differences in details, which vary depending on the purpose of the project and the study area. The authors performed data processing and analysis, as well as dynamic mapping of the city of Volgograd, a settlement with a linear urban planning with a large extent, studied the ecological state (in particular, analysis of thermal anomalies) and compiled a map of the city of Balakovo as a city-forming nuclear center of the Saratov region of the Russian Federation, analyzed the recreational areas of the cities of the Stavropol Territory, and also compiled the urbanized territories of one of the foreign states of the Asia-Pacific region. Attention is focused on the relevance and necessity of popularization in the management circles of algorithms and methods for using available software and open data to improve the efficiency of decisions made in various regions of the Russian Federation.

Olga V. Artemeva
Candidate of Geographical Sciences, Associate Professor
Department of Cartography and Geoinformatics of St. Petersburg
State University, Institute of Earth Sciences
7-9, Universitetskaya nab., St. Petersburg, 199034, Russia
e-mail: ovartemyeva@mail.ru
ORCID: 0000-0002-5741-6598
Web of Science ResearcherID: L-7778-2015
SPIN-code: 7813-9220

Nataliya A. Pozdnjakova
Senior Lecturer
Department of Cartography and Geoinformatics of St. Petersburg
State University, Institute of Earth Sciences
7-9, Universitetskaya nab., St. Petersburg, 199034, Russia
e-mail: n.pozdnyakova@spbu.ru
ORCID: 0000-0003-2097-3371
Web of Science ResearcherID: K-9195-2015
SPIN-code: 4957-1955

Fiodor A. Gnevashev
Student
Department of Cartography and Geoinformatics of St. Petersburg
State University, Institute of Earth Sciences
7-9, Universitetskaya nab., St. Petersburg, 199034, Russia
e-mail: 45fn@mail.ru
ORCID: 0000-0001-8742-8031

1. Federal State Statistics Service. Available at: https://eng.rosstat.gov.ru/ (accessed: 10.04.2022).
2. Federal’naya gosudarstvennaya informatsionnaya sistema territorial’nogo planirovaniya [Federal state information system of territorial planning]. Available at: https://fgistp.economy.gov.ru/ (accessed: 10.04.2022).
3. Prokhorova A. Sotsial’no-ehkonomicheskie karty: uchebnoe posobie [Socio-economic maps: a study guide]. Moscow: KDU, Dobrosvet; 2018. 424 p.
4. Valentei D.I. (ed.) Demograficheskii ehntsiklopedicheskii slovar’ [Demographic Encyclopedic Dictionary]. Moscow: Sovetskaya ehntsiklopediya; 1985. 608 p.
5. Soboleva N.P., Zamyatina Yu.L., Baranovskaya N.V., Zhornyak L.V. Geourbanistika: uchebnoe posobie [Geourbanistics: a textbook]. Tomsk: Izd-vo Tomskogo politekhnicheskogo universiteta; 2012. 301 p.
6. Lillesand T., Kiefer R., Chipman J. Remote sensing and image interpretation. 7 edn. New York: Wiley, Hoboken; 2015. 720 p.
7. Miller H.M. Users and uses of Landsat 8 satellite imagery—2014 survey results: U.S. Geological Survey Open-File Report 2016–1032. Reston: U.S. Geological Survey, 2016. 27 p.
8. Quinn J. Summary of Band Combinations. 2001. Available at: https://web.pdx.edu/~nauna/resources/10_BandCombinations.htm (accessed: 07.09.2022).
9. Seger M. Remote-sensing cartography with satellite photography – digital methods, relief map-making, examples of geographical science. Mitteilungen der Österreichischen Geographischen Gesellschaft. 1989;131:311–312.
10. Grigoriev А.N. Мethod of radiometric distortion correction of multispectral data for the earth remote sensing. Scientific and Technical Journal of Information Technologies, Mechanics and Optics. 2015;15(4): 595–602. DOI:10.17586/2226-1494-2015-15-4-595-602.
11. Schowengerdt R.A. Remote sensing. Models and methods of image processing. 3 edn. Oxford; Academic, 2006. 515 p.
12. Artemeva O. V., Zareie S., Elhaei Y., Pozdnyakova N.A., Vasilev N.D. Using remote sensing data to create maps of vegetation and relief for natural resource management of a large administrative region. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019;XLII-4/W18:103–109. DOI: 10.5194/isprs-archives-XLII-4-W18-103-2019.
13. EarthExplorer. United States Geological Survey (USGS), Data and Tools. 2018. Available at: https://earthexplorer.usgs.gov (accessed: 01.02.2022).
14. Landsat Collections. United States Geological Survey (USGS). 2018. Available at: https://www.usgs.gov/landsat-missions/landsatcollections
    (accessed: 01.02.2022).
15. Estoque R.C., Murayama Y. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: а comparative assessment of various spectral indices. Ecological Indicators. 2015;56:205–217. DOI: 10.1016/j.ecolind.2015.03.037.
16. Sentinel-2 MSI User Guide. Available at: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-2-msi (accessed: 11.03.2022).