Multi-dimension Geospatial feature learning for urban region function recognition

TL;DR

This paper introduces a multi-dimension feature learning model that combines remote sensing images and geospatial big data to improve urban region function recognition, achieving high accuracy and outperforming existing methods.

Contribution

The paper proposes a novel MDFL model integrating high-dimensional GBD data with RS images and a decision fusion network for enhanced urban function classification.

Findings

Achieves 92.75% overall accuracy.

Outperforms state-of-the-art by 10%.

Effectively combines social and visual data.

Abstract

Urban region function recognition plays a vital character in monitoring and managing the limited urban areas. Since urban functions are complex and full of social-economic properties, simply using remote sensing~(RS) images equipped with physical and optical information cannot completely solve the classification task. On the other hand, with the development of mobile communication and the internet, the acquisition of geospatial big data~(GBD) becomes possible. In this paper, we propose a Multi-dimension Feature Learning Model~(MDFL) using high-dimensional GBD data in conjunction with RS images for urban region function recognition. When extracting multi-dimension features, our model considers the user-related information modeled by their activity, as well as the region-based information abstracted from the region graph. Furthermore, we propose a decision fusion network that integrates the decisions from several neural networks and machine learning classifiers, and the final decision is made considering both the visual cue from the RS images and the social information from the GBD data. Through quantitative evaluation, we demonstrate that our model achieves overall accuracy at 92.75, outperforming the state-of-the-art by 10 percent.