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

Veronica Sri Moertini; Fritz Humphrey Silalahi

doi:doi:10.11648/j.ijdsa.20220804.12

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

Veronica Sri Moertini, Fritz Humphrey Silalahi

Published in International Journal of Data Science and Analysis (Volume 8, Issue 4)

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), 2024. Published by Science Publishing Group

Keywords

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

References

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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

Veronica Sri Moertini

Department of Informatics, Parahyangan Catholic University, Bandung, Indonesia
Fritz Humphrey Silalahi

Department of Informatics, Parahyangan Catholic University, Bandung, Indonesia

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Plain Text BibTeX RIS

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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