Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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Accessible Street-Level Greenery Assessment in Data-Scarce Environments with GreenviewCOMP

Authors
Sri Ramana Saketh Vasanthawada1, Harish Puppala2, *, Manoj Kumar Arora3, Pranav R. T. Peddinti4, Tata Babu Chukka5
1Department of Civil Engineering, BML Munjal University, 122413, Kapriwas, India
2Centre for Geospatial Technology, SRM University AP, 522502, Guntur, India
3Department of Civil Engineering, SRM University AP, 522502, Guntur, India
4Centre for Drone Technology, SRM University AP, 522502, Guntur, India
5ITE&C Department, Andhra Pradesh Space Applications Centre, 520010, Vijayawada, India
*Corresponding author. Email: harish.p@srmap.edu.in
Corresponding Author
Harish Puppala
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_5How to use a DOI?
Keywords
Street-level greenery; Green View Index; Urban Sustainability
Abstract

Quantifying greenery from an individual’s perspective is important due to its role in enhancing emotional well-being and reducing stress. While Google Street View (GSV) imagery has been widely used for street-level greenery assessments, its limited availability in many regions constrains such analysis. To address this gap, we developed GreenviewCOMP, a computationally efficient and user-friendly tool that quantifies greenery using 360° photospheres captured with digital cameras. The tool utilizes a binary thresholding method to classify green vegetation and compute Green View Index (GVI), offering a transparent and lightweight approach suitable for resource-limited contexts. A pilot study at a university campus in Gurugram, India, involved 106 photospheres, demonstrating spatial variations in GVI across the site. Classification accuracy was assessed, resulting in an overall accuracy of 0.97, recall of 0.98, and precision of 0.95. With its GUI, GreenviewCOMP enables novice users to efficiently assess street-level greenery, providing a practical alternative where GSV-based methods or computationally intensive algorithms are not feasible.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_5How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sri Ramana Saketh Vasanthawada
AU  - Harish Puppala
AU  - Manoj Kumar Arora
AU  - Pranav R. T. Peddinti
AU  - Tata Babu Chukka
PY  - 2025
DA  - 2025/12/31
TI  - Accessible Street-Level Greenery Assessment in Data-Scarce Environments with GreenviewCOMP
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 39
EP  - 50
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_5
DO  - 10.2991/978-94-6463-940-7_5
ID  - Vasanthawada2025
ER  -