Understanding safety based on urban perception

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Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
"Currently, one important field on machine learning is Urban Perception Computing is to model the way in which humans can interact and understand the environment that surrounds them. This process is performed using convolutional models to learn and identify some insights which define the concept of perception of a place (e.g. a street image). One approach of this field is urban perception of street images, we will focus on this approach to study the safety perception of a city and try to explain why and how the perception can be predicted by a mathematical model. As result, we present an analysis about the influence and impact of the visual components on the safety criteria and also an explanation about why a certain decision on the perception of the safety of the streets, such as safe or unsafe. © 2021, Springer Nature Switzerland AG"
Description
Keywords
Cityscape , Computer vision , Deep learning , Grad-CAM , Interpretability , LIME , Perception computing , Perception learning , Street View , Street-level imagery , Urban computing , Urban perception , Visual processing
Citation
URI
https://hdl.handle.net/20.500.12590/17047
Collections
Artículos - Ciencias de la Computación