Authors: Chuanji Shi, Yingying Zhang, Jiaotuan Wang, Qiqi Zhu

Published on: January 12, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2401.0655

Summary

• What is new: The novel end-to-end multimodal deep learning algorithm for detecting AOI polygons that incorporates multiple data types for precision.
• Why this is important: Existing research does not meet the precise requirements for defining AOIs needed by mobile Internet businesses for entities like communities or hospitals.
• What the research proposes: The proposed AOITR model uses a transformer encoder-decoder architecture that processes remote sensing images and multi-semantic information to accurately detect AOIs.
• Results: The algorithm outperforms two existing methods in detecting AOI polygons.

Technical Details

Technological frameworks used: Transformer encoder-decoder architecture

Models used: Cascaded feedforward network, multimodal detection model

Data used: Remote sensing images, POIs, road nodes, human mobility, logistics addresses

Potential Impact

Mobile Internet businesses, particularly those focused on online-to-offline services, urban planning agencies, and commercial analysis firms.

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