Enhanced Urban Region Profiling with Adversarial Contrastive Learning

• URL: http://arxiv.org/abs/2402.01163v2
• Date: Sat, 27 Jul 2024 13:45:50 GMT
• Title: Enhanced Urban Region Profiling with Adversarial Contrastive Learning
• Authors: Weiliang Chen, Qianqian Ren, Lin Pan, Shengxi Fu, Jinbao Li,
• Abstract summary: EUPAC is a novel framework that enhances the robustness of urban region embeddings. Our model generates region embeddings that preserve intra-region and inter-region dependencies. Experiments on real-world datasets demonstrate the superiority of our model over state-of-the-art methods.
• Score: 7.62909500335772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Urban region profiling is influential for smart cities and sustainable development. However, extracting fine-grained semantics and generating robust urban region embeddings from noisy and incomplete urban data is challenging. In response, we present EUPAC (Enhanced Urban Region Profiling with Adversarial Contrastive Learning), a novel framework that enhances the robustness of urban region embeddings through joint optimization of attentive supervised and adversarial contrastive modules. Specifically, region heterogeneous graphs containing human mobility data, point of interest information, and geographic neighborhood details for each region are fed into our model, which generates region embeddings that preserve intra-region and inter-region dependencies through graph convolutional networks and multi-head attention. Meanwhile, we introduce spatially learnable augmentation to generate positive samples that are semantically similar and spatially close to the anchor, preparing for subsequent contrastive learning. Furthermore, we propose an adversarial training method to construct an effective pretext task by generating strong positive pairs and mining hard negative pairs for the region embeddings. Finally, we jointly optimize attentive supervised and adversarial contrastive learning to encourage the model to capture the high-level semantics of region embeddings while ignoring the noisy and irrelevant details. Extensive experiments on real-world datasets demonstrate the superiority of our model over state-of-the-art methods.

Related papers

• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction [47.336599393600046]
  textscFedNE is a novel approach that integrates the textscFedAvg framework with the contrastive NE technique. We conduct comprehensive experiments on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-09-17T19:23:24Z)
• 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)
• Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning [50.88504784466931]
  Multi-task dense prediction involves semantic segmentation, depth estimation, and surface normal estimation. Existing solutions typically rely on learning global image representations for global cross-task image matching. Our proposal involves modeling region-wise representations using Gaussian Distributions.
  arXiv  Detail & Related papers  (2024-03-15T12:41:30Z)
• 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)
• 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)
• Multi-Temporal Relationship Inference in Urban Areas [75.86026742632528]
  Finding temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. We propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation learning and further handle the relationship sparsity.
  arXiv  Detail & Related papers  (2023-06-15T07:48:32Z)
• Point-Level Region Contrast for Object Detection Pre-Training [147.47349344401806]
  We present point-level region contrast, a self-supervised pre-training approach for the task of object detection. Our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality.
  arXiv  Detail & Related papers  (2022-02-09T18:56:41Z)
• 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)
• Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities [7.148078723492643]
  We propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas. Using large-scale high-resolution mobility data sets from millions of aggregated and anonymized mobile phone users in 16 metropolitan counties in the United States, we demonstrate that our embeddings encode complex relationships among features related to urban components. We show that embeddings generated by a model trained on a different county can capture 50% to 60% of the emergent spatial structure in another county.
  arXiv  Detail & Related papers  (2021-10-24T07:13:14Z)
• 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.