Quantifying urban streetscapes with deep learning: focus on aesthetic evaluation

• URL: http://arxiv.org/abs/2106.15361v1
• Date: Tue, 29 Jun 2021 12:51:00 GMT
• Title: Quantifying urban streetscapes with deep learning: focus on aesthetic evaluation
• Authors: Yusuke Kumakoshi, Shigeaki Onoda, Tetsuya Takahashi, Yuji Yoshimura
• Abstract summary: This paper reports the performance of our deep learning model on a unique data set prepared in Tokyo to recognize the areas covered by facades and billboards in streetscapes. The model achieved 63.17 % of accuracy, measured by Intersection-over-Union (IoU)
• Score: 4.129225533930966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The disorder of urban streetscapes would negatively affect people's perception of their aesthetic quality. The presence of billboards on building facades has been regarded as an important factor of the disorder, but its quantification methodology has not yet been developed in a scalable manner. To fill the gap, this paper reports the performance of our deep learning model on a unique data set prepared in Tokyo to recognize the areas covered by facades and billboards in streetscapes, respectively. The model achieved 63.17 % of accuracy, measured by Intersection-over-Union (IoU), thus enabling researchers and practitioners to obtain insights on urban streetscape design by combining data of people's preferences.

Related papers

• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series [12.621355888239359]
  Urban transformations have profound societal impact on both individuals and communities at large. We propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.
  arXiv  Detail & Related papers  (2024-01-02T08:57:09Z)
• City-Wide Perceptions of Neighbourhood Quality using Street View Images [5.340189314359048]
  This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents.
  arXiv  Detail & Related papers  (2022-11-22T10:16:35Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• Sidewalk Measurements from Satellite Images: Preliminary Findings [10.870041943009722]
  We train a computer vision model to detect sidewalks, roads, and buildings from remote-sensing imagery. We apply shape analysis techniques to study different attributes of the extracted sidewalks.
  arXiv  Detail & Related papers  (2021-12-12T02:22:46Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Repopulating Street Scenes [59.2621467759251]
  We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving.
  arXiv  Detail & Related papers  (2021-03-30T09:04:46Z)
• Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data [13.01318877814786]
  Urban Mosaic is a tool for exploring the urban fabric through a spatially and temporally dense data set of 7.7 million street-level images from New York City.
  arXiv  Detail & Related papers  (2020-08-31T02:23:12Z)
• Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation [57.68890534164427]
  In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation. We simply predict pseudo-labels for the unlabeled data and train subsequent models with both human-annotated and pseudo-labeled data. Our Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks.
  arXiv  Detail & Related papers  (2020-05-20T18:00:05Z)
• From Paris to Berlin: Discovering Fashion Style Influences Around the World [108.58097776743331]
  We propose to quantify fashion influences from everyday images of people wearing clothes. We introduce an approach that detects which cities influence which other cities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a forecasting model that predicts the popularity of any given style at any given city into the future.
  arXiv  Detail & Related papers  (2020-04-03T00:54:23Z)
• Indexical Cities: Articulating Personal Models of Urban Preference with Geotagged Data [0.0]
  This research characterizes personal preference in urban spaces and predicts a spectrum of unknown likeable places for a specific observer. Unlike most urban perception studies, our intention is not by any means to provide an objective measure of urban quality, but rather to portray personal views of the city or Cities of Cities.
  arXiv  Detail & Related papers  (2020-01-23T11:00:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.