Urban Region Pre-training and Prompting: A Graph-based Approach

• URL: http://arxiv.org/abs/2408.05920v3
• Date: Mon, 26 Aug 2024 11:41:28 GMT
• Title: Urban Region Pre-training and Prompting: A Graph-based Approach
• Authors: Jiahui Jin, Yifan Song, Dong Kan, Haojia Zhu, Xiangguo Sun, Zhicheng Li, Xigang Sun, Jinghui Zhang,
• Abstract summary: We propose a $textbfG$raph-based $textbfU$rban $textbfR$egion $textbfP$re-training and $textbfP$rompting framework for region representation learning.
• Score: 10.375941950028938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Previous work often neglects the spatial structures and functional layouts between entities, limiting their ability to capture transferable knowledge across regions. Further, these methods struggle to adapt effectively to specific downstream tasks, as they do not adequately address the unique features and relationships required for different downstream tasks. In this paper, we propose a $\textbf{G}$raph-based $\textbf{U}$rban $\textbf{R}$egion $\textbf{P}$re-training and $\textbf{P}$rompting framework ($\textbf{GURPP}$) for region representation learning. Specifically, we first construct an urban region graph that integrates detailed spatial entity data for more effective urban region representation. Then, we develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of interactions among entities. To further enhance the adaptability of these embeddings to different tasks, we design two graph-based prompting methods to incorporate explicit/hidden task knowledge. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our GURPP framework.

Related papers

• Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction [1.5156879440024378]
  Commuting flow prediction is an essential task for municipal operations in the real world. We develop a heterogeneous graph-based model to generate meaningful region embeddings for predicting different types of inter-level OD flows. Our proposed model outperforms existing models in terms of a uniform urban structure.
  arXiv  Detail & Related papers  (2024-08-27T03:30:01Z)
• RegionGPT: Towards Region Understanding Vision Language Model [88.42271128373191]
  RegionGPT (short as RGPT) is a novel framework designed for complex region-level captioning and understanding. We develop an automated region caption data generation pipeline, enriching the training set with detailed region-level captions. We demonstrate that a universal RGPT model can be effectively applied and significantly enhancing performance across a range of region-level tasks.
  arXiv  Detail & Related papers  (2024-03-04T18:58:08Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• Attentive Graph Enhanced Region Representation Learning [7.4106801792345705]
  Representing urban regions accurately and comprehensively is essential for various urban planning and analysis tasks. We propose the Attentive Graph Enhanced Region Representation Learning (ATGRL) model, which aims to capture comprehensive dependencies from multiple graphs and learn rich semantic representations of urban regions.
  arXiv  Detail & Related papers  (2023-07-06T16:38:43Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)
• 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)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  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.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• 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)
• Urban Region Profiling via A Multi-Graph Representation Learning Framework [0.0]
  We propose a multi-graph representative learning framework, called Region2Vec, for urban region profiling. Experiments on real-world datasets show that Region2Vec can be employed in three applications and outperforms all state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-02-04T11:05:37Z)
• Learning Neighborhood Representation from Multi-Modal Multi-Graph: Image, Text, Mobility Graph and Beyond [20.014906526266795]
  We propose a novel approach to integrate multi-modal geotagged inputs as either node or edge features of a multi-graph. Specifically, we use street view images and POI features to characterize neighborhoods (nodes) and use human mobility to characterize the relationship between neighborhoods (directed edges) The embedding we trained outperforms the ones using only unimodal data as regional inputs.
  arXiv  Detail & Related papers  (2021-05-06T07:44:05Z)
• 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.