Using convolutional autoencoders to extract visual features of leisure and retail environments

Comber, Sam; Arribas‐Bel, Daniel; Singleton, Alex; Dolega, Les

doi:10.1016/j.landurbplan.2020.103887

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…Another study extracted the latent features from street view imagery to allow for a more interpretable method of predicting street quality and street network attributes [33]. Further work described the areas around leisure and retail amenities using latent image features extracted from storefront images [34]. One common thread among these works is the use of dimension reduction techniques such as k-means or principal component analysis (PCA) to allow for an easier interpretation of the latent image features.…”

Section: Literature Reviewmentioning

confidence: 99%

Unsupervised feature extraction and clustering of aerial images for understanding hazardous road segments

Francis

Bright

Esnaashari

et al. 2023

Preprint

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Satellite and aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. Through the clustering of hazardous road segments with these latent image features, this work demonstrates how aerial images and machine learning can provide a data-driven aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.

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Section: Literature Reviewmentioning

confidence: 99%

Unsupervised feature extraction and clustering of aerial images for understanding hazardous road segments

Francis

Bright

Esnaashari

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Another study extracted the latent features from street view imagery to allow for a more interpretable method of predicting street quality and street network attributes 33 . Further work described the areas around leisure and retail amenities using latent image features extracted from storefront images 34 . One common thread among these works is the use of dimension reduction techniques such as k-means or principal component analysis (PCA) to allow for an easier interpretation of the latent image features.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments

Francis

Bright

Esnaashari

et al. 2023

Sci Rep

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Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.

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Unsupervised machine learning in urban studies: A systematic review of applications

Wang

Biljecki²

2022

Cities

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Using convolutional autoencoders to extract visual features of leisure and retail environments

Cited by 10 publications

References 23 publications

Unsupervised feature extraction and clustering of aerial images for understanding hazardous road segments

Unsupervised feature extraction and clustering of aerial images for understanding hazardous road segments

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments

Unsupervised machine learning in urban studies: A systematic review of applications

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