A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection

• URL: http://arxiv.org/abs/2211.14633v1
• Date: Sat, 26 Nov 2022 18:17:39 GMT
• Title: A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection
• Authors: Congxi Xiao, Jingbo Zhou, Jizhou Huang, Hengshu Zhu, Tong Xu, Dejing Dou, Hui Xiong
• Abstract summary: We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
• Score: 68.84486900183853
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have not fully addressed the unique challenges in UV detection such as the scarcity of labeled UVs and the diverse urban patterns in different regions. To this end, we first build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The core idea of such a framework is to firstly pre-train a basis (or master) model over the URG, and then to adaptively derive specific (or slave) models from the basis model for different regions. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area. Finally, we conduct extensive experiments in three cities to demonstrate the effectiveness of our approach.

Related papers

• Urban Region Pre-training and Prompting: A Graph-based Approach [10.375941950028938]
  We propose a $textbfG$raph-based $textbfU$rban $textbfR$egion $textbfP$re-training and $textbfP$rompting framework for region representation learning.
  arXiv  Detail & Related papers  (2024-08-12T05:00:23Z)
• COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation [1.5745692520785073]
  We introduce a novel graph-based masked autoencoder (GMAE) for city-scale urban layout generation. The method encodes attributed buildings, city blocks, communities and cities into a unified graph structure. Our approach achieves good realism, semantic consistency, and correctness across the heterogeneous urban styles in 330 US cities.
  arXiv  Detail & Related papers  (2024-07-16T00:49:53Z)
• UVMap-ID: A Controllable and Personalized UV Map Generative Model [67.71022515856653]
  We introduce UVMap-ID, a controllable and personalized UV Map generative model. Unlike traditional large-scale training methods in 2D, we propose to fine-tune a pre-trained text-to-image diffusion model. Both quantitative and qualitative analyses demonstrate the effectiveness of our method in controllable and personalized UV Map generation.
  arXiv  Detail & Related papers  (2024-04-22T20:30:45Z)
• UV-SAM: Adapting Segment Anything Model for Urban Village Identification [25.286722125746902]
  Governments heavily depend on field survey methods to monitor the urban villages. To accurately identify urban village boundaries from satellite images, we adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification.
  arXiv  Detail & Related papers  (2024-01-16T03:21:42Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning [57.91323079939641]
  We develop a novel human-instructed deep hierarchical generative model to generate optimal urban plans. The first stage is to label the grids of a target area with latent functionalities to discover functional zones. The second stage is to perceive the planning requirements to form urban functionality projections. The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
  arXiv  Detail & Related papers  (2022-12-01T23:06:41Z)
• Urban form and COVID-19 cases and deaths in Greater London: an urban morphometric approach [63.29165619502806]
  The COVID-19 pandemic generated a considerable debate in relation to urban density. This is an old debate, originated in mid 19th century's England with the emergence of public health and urban planning disciplines. We describe urban form at individual building level and then aggregate information for official neighbourhoods.
  arXiv  Detail & Related papers  (2022-10-16T10:01:10Z)
• Automated Urban Planning aware Spatial Hierarchies and Human Instructions [33.06221365923015]
  We propose a novel, deep, human-instructed urban planner based on generative adversarial networks (GANs) GANs build urban functional zones based on information from human instructions and surrounding contexts. We conduct extensive experiments to validate the efficacy of our work.
  arXiv  Detail & Related papers  (2022-09-26T20:37:02Z)
• Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT) [0.0]
  We propose heterogeneous urban graph attention network (HUGAT) for learning the representations of urban regions. In our experiments on NYC data, HUGAT outperformed all the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-02-18T04:59:20Z)
• 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)

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.