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Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm

Received: 29 July 2022     Accepted: 30 August 2022     Published: 14 September 2022
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Abstract

It is well known that trees in cities are important as they produce oxygen, absorb rainwater, and provide shades. Due to urban development, the trees sufficiency in many cities is threatened. To maintain cities healthy environment, it is imperative that the local authorities monitor the trees adequacy from time to time and then act accordingly. For doing so, they need to be supported with quantitative data representing the adequacy of trees that can easily be accessed. In Indonesia, the lowest level of governmental administrative area is urban village. We view that if the data is provided for this level, it would be more effective as the head of the urban village can act or create necessary programs accordingly. Google updates regularly and provides satellite images that can be freely downloaded with many zoom-level. In urban areas, trees are visible with zoom-level of 16 and beyond. This research aims to develop a method for detecting/mapping trees green areas from urban village satellite images with the final result of green area (approximated in hectare unit). The method include village images preparation, detecting green areas based on image segmentation approach (using k-Means clustering algorithm), mapping and computing green area for each village. The case study is Bandung city, which is one of the most populated cities in Indonesia. The findings are potentially be used by the local authorities to evaluate the adequacy of trees in their territories.

Published in International Journal of Data Science and Analysis (Volume 8, Issue 4)
DOI 10.11648/j.ijdsa.20220804.12
Page(s) 119-130
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Satellite Image Segmentation, Green Area Detection and Mapping, Urban Village Green Area

References
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Cite This Article
  • APA Style

    Veronica Sri Moertini, Fritz Humphrey Silalahi. (2022). Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm. International Journal of Data Science and Analysis, 8(4), 119-130. https://doi.org/10.11648/j.ijdsa.20220804.12

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    ACS Style

    Veronica Sri Moertini; Fritz Humphrey Silalahi. Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm. Int. J. Data Sci. Anal. 2022, 8(4), 119-130. doi: 10.11648/j.ijdsa.20220804.12

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    AMA Style

    Veronica Sri Moertini, Fritz Humphrey Silalahi. Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm. Int J Data Sci Anal. 2022;8(4):119-130. doi: 10.11648/j.ijdsa.20220804.12

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  • @article{10.11648/j.ijdsa.20220804.12,
      author = {Veronica Sri Moertini and Fritz Humphrey Silalahi},
      title = {Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm},
      journal = {International Journal of Data Science and Analysis},
      volume = {8},
      number = {4},
      pages = {119-130},
      doi = {10.11648/j.ijdsa.20220804.12},
      url = {https://doi.org/10.11648/j.ijdsa.20220804.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220804.12},
      abstract = {It is well known that trees in cities are important as they produce oxygen, absorb rainwater, and provide shades. Due to urban development, the trees sufficiency in many cities is threatened. To maintain cities healthy environment, it is imperative that the local authorities monitor the trees adequacy from time to time and then act accordingly. For doing so, they need to be supported with quantitative data representing the adequacy of trees that can easily be accessed. In Indonesia, the lowest level of governmental administrative area is urban village. We view that if the data is provided for this level, it would be more effective as the head of the urban village can act or create necessary programs accordingly. Google updates regularly and provides satellite images that can be freely downloaded with many zoom-level. In urban areas, trees are visible with zoom-level of 16 and beyond. This research aims to develop a method for detecting/mapping trees green areas from urban village satellite images with the final result of green area (approximated in hectare unit). The method include village images preparation, detecting green areas based on image segmentation approach (using k-Means clustering algorithm), mapping and computing green area for each village. The case study is Bandung city, which is one of the most populated cities in Indonesia. The findings are potentially be used by the local authorities to evaluate the adequacy of trees in their territories.},
     year = {2022}
    }
    

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  • TY  - JOUR
    T1  - Mapping and Quantifying Green Area of Urban Villages from Google Satellite Images Using k-Means Clustering Algorithm
    AU  - Veronica Sri Moertini
    AU  - Fritz Humphrey Silalahi
    Y1  - 2022/09/14
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijdsa.20220804.12
    DO  - 10.11648/j.ijdsa.20220804.12
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
    SP  - 119
    EP  - 130
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20220804.12
    AB  - It is well known that trees in cities are important as they produce oxygen, absorb rainwater, and provide shades. Due to urban development, the trees sufficiency in many cities is threatened. To maintain cities healthy environment, it is imperative that the local authorities monitor the trees adequacy from time to time and then act accordingly. For doing so, they need to be supported with quantitative data representing the adequacy of trees that can easily be accessed. In Indonesia, the lowest level of governmental administrative area is urban village. We view that if the data is provided for this level, it would be more effective as the head of the urban village can act or create necessary programs accordingly. Google updates regularly and provides satellite images that can be freely downloaded with many zoom-level. In urban areas, trees are visible with zoom-level of 16 and beyond. This research aims to develop a method for detecting/mapping trees green areas from urban village satellite images with the final result of green area (approximated in hectare unit). The method include village images preparation, detecting green areas based on image segmentation approach (using k-Means clustering algorithm), mapping and computing green area for each village. The case study is Bandung city, which is one of the most populated cities in Indonesia. The findings are potentially be used by the local authorities to evaluate the adequacy of trees in their territories.
    VL  - 8
    IS  - 4
    ER  - 

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Author Information
  • Department of Informatics, Parahyangan Catholic University, Bandung, Indonesia

  • Department of Informatics, Parahyangan Catholic University, Bandung, Indonesia

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