Enriching Location Representation with Detailed Semantic Information

• URL: http://arxiv.org/abs/2506.02744v1
• Date: Tue, 03 Jun 2025 11:06:51 GMT
• Title: Enriching Location Representation with Detailed Semantic Information
• Authors: Junyuan Liu, Xinglei Wang, Tao Cheng,
• Abstract summary: CaLLiPer+ is an extension of the CaLLiPer model that integrates Point-of-Interest (POI) names alongside categorical labels.<n>We evaluate its effectiveness on two downstream tasks, land use classification and socioeconomic status distribution mapping.
• Score: 0.6554326244334866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spatial representations that capture both structural and semantic characteristics of urban environments are essential for urban modeling. Traditional spatial embeddings often prioritize spatial proximity while underutilizing fine-grained contextual information from places. To address this limitation, we introduce CaLLiPer+, an extension of the CaLLiPer model that systematically integrates Point-of-Interest (POI) names alongside categorical labels within a multimodal contrastive learning framework. We evaluate its effectiveness on two downstream tasks, land use classification and socioeconomic status distribution mapping, demonstrating consistent performance gains of 4% to 11% over baseline methods. Additionally, we show that incorporating POI names enhances location retrieval, enabling models to capture complex urban concepts with greater precision. Ablation studies further reveal the complementary role of POI names and the advantages of leveraging pretrained text encoders for spatial representations. Overall, our findings highlight the potential of integrating fine-grained semantic attributes and multimodal learning techniques to advance the development of urban foundation models.

Related papers

• Multimodal Contrastive Learning of Urban Space Representations from POI Data [2.695321027513952]
  CaLLiPer (Contrastive Language-Location Pre-training) is a representation learning model that embeds continuous urban spaces into vector representations. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK.
  arXiv  Detail & Related papers  (2024-11-09T16:24:07Z)
• Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data [10.968721742000653]
  We propose an effective deep-learning method within a two-step prediction framework. Our method first incorporates remote sensing data, capturing pivotal environmental context. We construct the QR-P graph for the user's historical trajectories to encapsulate historical travel knowledge.
  arXiv  Detail & Related papers  (2024-03-22T04:22:36Z)
• Prospector Heads: Generalized Feature Attribution for Large Models & Data [82.02696069543454]
  We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods. We demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data.
  arXiv  Detail & Related papers  (2024-02-18T23:01:28Z)
• Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning for Robust Forecasting and Security [12.8405655328298]
  Existing methods often struggle with issues such as noise, data incompleteness, and security vulnerabilities.<n>This paper proposes a novel framework, Enhanced Urban Region Profiling with Adversarial Self-Supervised Learning (EUPAS)<n>EUPAS ensures robust performance across various forecasting tasks such as crime prediction, check-in prediction, and land use classification.
  arXiv  Detail & Related papers  (2024-02-02T06:06:45Z)
• Recognize Any Regions [55.76437190434433]
  RegionSpot integrates position-aware localization knowledge from a localization foundation model with semantic information from a ViL model.<n>Experiments in open-world object recognition show that our RegionSpot achieves significant performance gain over prior alternatives.
  arXiv  Detail & Related papers  (2023-11-02T16:31:49Z)
• 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)
• Robust Saliency-Aware Distillation for Few-shot Fine-grained Visual Recognition [57.08108545219043]
  Recognizing novel sub-categories with scarce samples is an essential and challenging research topic in computer vision. Existing literature addresses this challenge by employing local-based representation approaches. This article proposes a novel model, Robust Saliency-aware Distillation (RSaD), for few-shot fine-grained visual recognition.
  arXiv  Detail & Related papers  (2023-05-12T00:13:17Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• 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)
• SIRI: Spatial Relation Induced Network For Spatial Description Resolution [64.38872296406211]
  We propose a novel relationship induced (SIRI) network for language-guided localization. We show that our method is around 24% better than the state-of-the-art method in terms of accuracy, measured by an 80-pixel radius. Our method also generalizes well on our proposed extended dataset collected using the same settings as Touchdown.
  arXiv  Detail & Related papers  (2020-10-27T14:04:05Z)
• Learning to Predict Context-adaptive Convolution for Semantic Segmentation [66.27139797427147]
  Long-range contextual information is essential for achieving high-performance semantic segmentation. We propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector. Our CaC-Net achieves superior segmentation performance on three public datasets.
  arXiv  Detail & Related papers  (2020-04-17T13:09:17Z)

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.