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  • Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning [Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning]
Title: Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning [Vegetation Coverage and Urban Amenity Mapping Using Computer Vision and Machine Learning]
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.  more » « less
Award ID(s):
2131186 1827505
PAR ID:
10440685
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 3rd International Conference on Image Processing and Vision Engineering (IMPROVE 2023)
Page Range / eLocation ID:
67 to 75
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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