Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning

Nicholas Karkut, Alexey Kiriluk, Zihao Zhang, Zhigang Zhu, Zhigang Zhu

2023

Abstract

This paper proposes a computer vision-based workflow that analyses Google 360-degree street views to understand the quality of urban spaces regarding vegetation coverage and accessibility of urban amenities such as benches. Image segmentation methods were utilized to produce an annotated image with the amount of vegetation, sky and street coloration. Two deep learning models were used -- Monodepth2 for depth detection and YoloV5 for object detection -- to create a 360-degree diagram of vegetation and benches at a given location. The automated workflow allows non-expert users like planners, designers, and communities to analyze and evaluate urban environments with Google Street Views. The workflow consists of three components: (1) user interface for location selection; (2) vegetation analysis, bench detection and depth estimation; and (3) visualization of vegetation coverage and amenities. The analysis and visualization could inform better urban design outcomes.

Paper Citation

in Harvard Style

Karkut N., Kiriluk A., Zhang Z. and Zhu Z. (2023). Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 67-75. DOI: 10.5220/0011705100003497

in Bibtex Style

@conference{improve23,
author={Nicholas Karkut and Alexey Kiriluk and Zihao Zhang and Zhigang Zhu},
title={Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={67-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011705100003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning
SN - 978-989-758-642-2
AU - Karkut N.
AU - Kiriluk A.
AU - Zhang Z.
AU - Zhu Z.
PY - 2023
SP - 67
EP - 75
DO - 10.5220/0011705100003497
PB - SciTePress