Urban Region Representation Learning: A Flexible Approach

2025-03-12

AI Summary

Existing urban region representation methods are constrained by fixed spatial partitioning and static feature inputs, limiting their adaptability to diverse downstream tasks. To address this, we propose a dynamically adjustable urban region representation learning framework. First, we design a spatial-grid-based adaptive aggregation mechanism that enables task-driven, dynamic regional partitioning. Second, we introduce a downstream-task-oriented prompt learning strategy to achieve semantic-aware fusion of heterogeneous multimodal data—including POIs, land-use patterns, satellite imagery, and street-view images. Extensive experiments across five real-world datasets and four downstream tasks (e.g., check-in prediction and crime forecasting) demonstrate up to a 202% improvement in accuracy over state-of-the-art methods. Our core contribution lies in the first unified modeling of both regional structural dynamics and task-specific semantic adaptability, bridging a critical gap in urban representation learning.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Representation Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which may not suit the needs of different downstream tasks. To address this limitation, we propose a model named FlexiReg for urban region representation learning that is flexible with both the formation of urban regions and the input region features. FlexiReg is based on a spatial grid partitioning over the spatial area of interest. It learns representations for the grid cells, leveraging publicly accessible data, including POI, land use, satellite imagery, and street view imagery. We propose adaptive aggregation to fuse the cell representations and prompt learning techniques to tailor the representations towards different tasks, addressing the needs of varying formations of urban regions and downstream tasks. Extensive experiments on five real-world datasets demonstrate that FlexiReg outperforms state-of-the-art models by up to 202% in term of the accuracy of four diverse downstream tasks using the produced urban region representations.

Problem

• Flexible urban region representation learning for diverse tasks.
• Adaptive aggregation and prompt learning for tailored representations.
• Outperforms state-of-the-art models in accuracy for downstream tasks.

Innovation

• Flexible urban region formation and feature input.
• Adaptive aggregation for cell representation fusion.
• Prompt learning for task-specific representation tailoring.