ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume VI-4/W2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W2-2020, 17–24, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-17-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W2-2020, 17–24, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-17-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Sep 2020

15 Sep 2020

OPEN GEOSPATIAL DATA FOR URBAN GREEN AREAS

M. Baučić, F. Gilić, S. Bačić, and T. Duplančić-Leder M. Baučić et al.
  • Faculty of Civil Engineering, Architecture and Geodesy, Split, Croatia

Keywords: geospatial open data, green infrastructure, green view index, Google Street View, land cover, Sentinel, urban, vegetation

Abstract. More than half of the world’s population lives in big urbanized areas. It is not rare that those areas are lacking natural green spaces. Green spaces improve different aspects of life in cities and they are becoming so important that lately more and more attention is given to the so-called green infrastructure. The first step in planning green infrastructure is acquiring information about current city greenery. In this paper, it was investigated how can airborne, spaceborne, and street-level images be used in gathering information about greenery. As spaceborne images, Sentinel-2 satellite images were used and as street-level images, Google Street View 360° photospheres have been utilized. From both sources, information about current greenery status was automatically extracted. Gathered data was aggregated on different spatial units that are suitable for decision making that aims at further developing the green spaces. These top-down and street-level images complement each other in a way that top-down images can be used to track the percentage of green area and its changing over time, while street-level images give information about greenery that is perceived by pedestrians. With proposed methods, it is possible to detect areas that should be considered for greening and also to identify areas that should have priority in that process.