Classification of Urban Morphology with Deep Learning: Application on Urban Vitality

• URL: http://arxiv.org/abs/2105.09908v1
• Date: Fri, 7 May 2021 08:53:31 GMT
• Title: Classification of Urban Morphology with Deep Learning: Application on Urban Vitality
• Authors: Wangyang Chen, Abraham Noah Wu, Filip Biljecki
• Abstract summary: We propose a deep learning-based technique to automatically classify road networks into four classes on a visual basis. Nine cities around the world are selected as the study areas and their road networks are acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through a clustering on the percentage of each road network category. An advanced tree-based regression model is for the first time designated to establish the relationship between morphological indices and vitality indicators.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from human's visual and intuitive perspective. We take the first step to bridge the gap by proposing a deep learning-based technique to automatically classify road networks into four classes on a visual basis. The method is implemented by generating an image of the street network (Colored Road Hierarchy Diagram), which we introduce in this paper, and classifying it using a deep convolutional neural network (ResNet-34). The model achieves an overall classification accuracy of 0.875. Nine cities around the world are selected as the study areas and their road networks are acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through a clustering on the percentage of each road network category. In the subsequent part of the paper, we focus on the usability of such classification: the effectiveness of our human perception augmentation is examined by a case study of urban vitality prediction. An advanced tree-based regression model is for the first time designated to establish the relationship between morphological indices and vitality indicators. A positive effect of human perception augmentation is detected in the comparative experiment of baseline model and augmented model. This work expands the toolkit of quantitative urban morphology study with new techniques, supporting further studies in the future.

Related papers

• Ensembling convolutional neural networks for human skin segmentation [2.8391355909797644]
  We propose to train a convolutional network using the datasets focused on different features to create an ensemble whose individual outcomes are effectively combined. We expect that this study will help in developing new ensemble-based techniques that will improve the performance of semantic segmentation systems.
  arXiv  Detail & Related papers  (2024-07-27T17:55:28Z)
• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Humans Social Relationship Classification during Accompaniment [6.071490877668864]
  This paper presents the design of deep learning architectures which allow to classify the social relationship existing between two people who are walking in a side-by-side formation into four possible categories --colleagues, couple, family or friendship. The models are developed using Neural Networks or Recurrent Neural Networks to achieve the classification and are trained and evaluated using a database of readings obtained from humans performing an accompaniment process in an urban environment.
  arXiv  Detail & Related papers  (2022-07-06T18:07:36Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• A Comprehensive Survey on Community Detection with Deep Learning [93.40332347374712]
  A community reveals the features and connections of its members that are different from those in other communities in a network. This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders.
  arXiv  Detail & Related papers  (2021-05-26T14:37:07Z)
• 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)
• Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity [0.0]
  We present a new semantic segmentation model for historical city maps based on convolutional neural networks. We show that these networks are able to semantically segment map data of a very large figurative diversity with efficiency.
  arXiv  Detail & Related papers  (2021-01-29T09:08:12Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Deep Learning for Community Detection: Progress, Challenges and Opportunities [79.26787486888549]
  Article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks. This article summarizes the contributions of the various frameworks, models, and algorithms in deep neural networks.
  arXiv  Detail & Related papers  (2020-05-17T11:22:11Z)

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.