Urban area-of-interest (AOI) refers to an integrated urban functional zone with defined boundaries. The rapid development of urban commerce has resulted in an increased demand for more precise requirements in defining AOIs. However, existing research primarily concentrates on broad AOI mining for urban planning or regional economic analysis, failing to cater to the precise requirements of mobile Internet online-to-offline businesses. These businesses necessitate accuracy down to a specific community, school, or hospital. In this paper, we propose an end-to-end multimodal deep learning algorithm for detecting AOI fence polygon using remote sensing images and multi-semantics reference information. We then evaluate its timeliness through a cascaded module that incorporates dynamic human mobility and logistics address information. Specifically, we begin by selecting a point-of-interest (POI) of specific category, and use it to recall corresponding remote sensing images, nearby POIs, road nodes, human mobility, and logistics addresses to build a multimodal detection model based on transformer encoder-decoder architecture, titled AOITR. In the model, in addition to the remote sensing images, multi-semantic information including core POI and road nodes is embedded and reorganized as the query content part for the transformer decoder to generate the AOI polygon. Meanwhile, relatively dynamic distribution features of human mobility, nearby POIs, and logistics addresses are used for AOI reliability evaluation through a cascaded feedforward network. The experimental results demonstrate that our algorithm significantly outperforms two existing methods.

翻译：城市兴趣区（AOI）是指具有明确边界的综合性城市功能区。城市商业的快速发展导致对AOI定义精度的需求日益提升。然而现有研究主要聚焦于面向城市规划或区域经济分析的宏观AOI挖掘，无法满足移动互联网线上线下商业场景的精准需求——这类业务需要精确到具体社区、学校或医院的粒度。本文提出一种基于遥感图像与多语义参考信息的端到端多模态深度学习算法，用于检测AOI围栏多边形，并通过融合动态人流与物流地址信息的级联模块评估其时效性。具体地，我们首先选取特定类别的兴趣点（POI），并据此召回对应遥感图像、周边POI、道路节点、人流及物流地址，进而构建基于Transformer编码器-解码器架构的多模态检测模型AOITR。该模型中，除遥感图像外，核心POI与道路节点等多语义信息被嵌入重组为Transformer解码器的查询内容，用于生成AOI多边形；同时，通过级联前馈网络利用人流、周边POI及物流地址的相对动态分布特征评估AOI可靠性。实验结果表明，本算法显著优于现有两种方法。