Urban Atlas for Ukrainian cities on the basis of high-resolution satellite data

1Kussul, NM, 2Shelestov, AYu., 1Yailymov, BYa., 3Shumilo, LL, 2Yailymova, HO, 4Lavreniuk, MS, 1Kolos, LM, 1Pidgorodetska, LV, 1Bilokonska, YV
1Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine, Kyiv, Ukraine
2National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Kyiv, Ukraine
3Space Research Institute of the NAS of Ukraine and SSA of Ukraine, Kyiv, Ukraine
4Space Research Institute of the National Academy of Sciences of Ukraine and the National Space Agency of Ukraine, Kyiv, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Space Sci. & Technol. 2019, 25 ;(6):51-60
Publication Language: Ukrainian
The issues addressed in the article relate to the development of modern technology based on open source data compatible with the Copernicus Urban Atlas service. The city atlas of Kyiv was developed within the framework of the project H2020 ERAPLANET SMURBS (SMART URBan Solutions for air quality, disasters, and city growth). Kyiv became the first city outside the EU for which such a product was created. This technology is based on free satellite data of Earth observations and land cover classification using in-house machine learning methods and geostatistical analysis of building density from open-source vector city maps, including OSM (Open Street Map) data. The distinctive features of the proposed solution are the use of opensource data only and the annual updating of city land cover / land use information. In the future, the developed technology can be applied to other cities.
Keywords: city atlas, growth of urban agglomerations, land cover classification, satellite monitoring, Urban Atlas
1. Copernicus Land Monitoring Service — Urban Atlas. European Environment Agency. URL: www.eea.europa. eu/data-and-maps/data/copernicus-land-monitoringservice-urban-atlas (Last accessed 18 July 2018).
2. CORINE Land Cover nomenclature conversion to Land Cover Classification system (2018). URL: https://land.copernicus.eu/eagle/files/eagle-related-projects/pt_clcconv... (Last accessed 18 July 2018).
3. Earth.esa.int. Sentinel-2 MSI — Technical Guide — Sentinel Online. URL: https://earth.esa.int/web/sentinel/technicalguides/sentinel-2-msi (Last accessed 18 July 2019).
4. Google Earth. URL: https://www.google.com/earth/(Last accessed 18 July 2019).
5. Kussul N., Lavreniuk M., Skakun S., Shelestov A. (2017). Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778—782.
6. Kussul N., Shelestov A., Lavreniuk M., Butko I., Skakun S. (2016). Deep learning approach for large scale land cover mapping based on remote sensing data fusion. 2016  IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
7. Lavreniuk M., Kussul N., Novikov A. (2018). Deep Learning Crop Classification Approach Based on Sparse Coding of Time Series of Satellite Data. IGARSS 2018 — 2018 IEEE International Geoscience and Remote Sensing Symposium.
8. Lavreniuk M., Kussul N., Shelestov A., Dubovyk O., Low F. (2018). Object-Based Postprocessing Method for Crop Classification MAPS. IGARSS 2018 — 2018 IEEE International Geoscience and Remote Sensing Symposium.
9. Lavreniuk M., Kussul N., Shelestov, A., Yailymov B., Oliinyk T., Kosteckyi A. (2016). Validation methods for regional retrospective high resolution land cover for Ukraine. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
10. Ong B. L. (2002). Green plot ratio: an ecological measure for architecture and urban planning. Landscape and Urban Planning, 63 (4), 197—211.
11. OpenStreetMap. URL: https://www.openstreetmap.org (Last accessed 18 July 2019).
12. Scihub.copernicus.eu. Open Access Hub. URL: https://scihub.copernicus.eu/dhus/#/home (Last accessed 18 July 2019).
13. Sentinel.esa.int. IW GRD Resolutions — Sentinel-1 SAR Technical Guide — Sentinel Online. URL: https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-1-sar/pr... (Last accessed 18 July 2019).
14. Shelestov A., Lavreniuk M., Kussul N., Novikov A., Skakun S. (2017). Large scale crop classification using Google earth engine platform. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
15. Shelestov A., Lavreniuk M., Kussul N., Novikov A., Skakun S. (2017). Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science, 5.
16. Shelestov A., Raudner A., Kolotii A., Marinosci I., Attanasio A., Munafò M., Lavreniuk M., Speyer O., Yailymov B., Kussul N. (2019). Urban Growth Services Within ERA-PLANET SMURBS Project. Living Planet Symposium. Milan, Italy.
17. Skakun S. V., Basarab R. M. (2014). Reconstruction of Missing Data in Time-Series of Optical Satellite Images Using Self-Organizing Kohonen Maps. J. Automation and Information Sciences, 46(12), 19—26. doi:10.1615/jautomatinfscien.v46.i12.30.
18. SNAP. STEP. (n.d.). URL: https://step.esa.int/main/toolboxes/snap/ (Last accessed 18 July 2019).
19. “Urban Atlas”. Urban Atlas — Copernicus Land Monitoring Service. URL: land.copernicus.eu/local/urban-atlas/view (Last accessed 26 Mar. 2019).