Google Street View and Machine Learning—Useful Tools for a Street-Level Remote Survey: A Case Study in Ho Chi Minh, Vietnam and Ichikawa, Japan

Ta, Duy Thong; Furuya, Katsunori

doi:10.3390/land11122254

Cited by 4 publications

(2 citation statements)

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“…Comparing the judgements of experts and the SVM showed a high level of accuracy with Cohen's Kappa coefficient values of 0.910 and 0.925. The study authored by [43], aimed to investigate the relationship between urban greenery and the time spent walking by pedestrians. The study specifically looked at the Green View Index (GVI), which measures the visibility of greenery from a specific position in neighbourhood streets.…”

Section: Green Space Quality Analysis Using Machine Learningmentioning

confidence: 99%

Green Space Quality Analysis Using Machine Learning Approaches

Rustamov

Zaki

2023

Sustainability

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Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application.

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Section: Green Space Quality Analysis Using Machine Learningmentioning

confidence: 99%

Green Space Quality Analysis Using Machine Learning Approaches

Rustamov

Zaki

2023

Sustainability

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“…They can, however, gauge the psychological stress that people perceive in a constructed setting. Moreover, their sample size is limited, making them unsuitable for large-scale constructed environments and only appropriate for small-scale investigations [10]. Furthermore, deep learning is being used more and more frequently because to the quick advancement of computer technology [11].…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks optimized with Gazelle Optimization Algorithm for Urban Plantscape Design Based on Large-scale Street View

Yue Liu

2024

jes

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The center of human activity and a key point of contact for social interaction between city people and the built environment is the street. With more street-level photos becoming available, urban landscape studies have more chances to examine and evaluate streetscapes closely and from various angles. This manuscript present the graph neural networks (GNN) optimized with gazelle optimization algorithm (GOA) for urban plants cape design based on large-scale street view (UPLS-GNN-GOA). Initially data is taken from ADE20K dataset. Afterward the data is fed to General Robust Subband Adaptive Filtering (GRSAF) based pre-processing process. The pre-processing output is given to Discrete Fractional Fourier Transforms (DFrFT) to extract the street greenery and openness of street canyons. After that, the extracted features are fed to Graph Neural Networks (GNN) to classify the public health, urban microclimate, human perception. The weight parameters of the GNN are optimized using Gazelle Optimization Algorithm (GOA).The UPLS-GNN-GOA method is implemented in Python, and its efficiency is determined using a series of performance evaluation metrics analysis, including accuracy, precision, recall, f1-score, and sensitivity. The proposed UPLS-GNN-GOA method shows the highest accuracy of 98%, precision of 98%, and F1-score of 97% while comparing other existing methods such as urban plantscape design based on convolutional neural network (UPLS-CNN), urban plantscape design based on deep convolutional neural network (UPLS-DCNN) and urban plantscape design based on deep learning and convolutional neural network (UPLS-DL-CNN) respectively.

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