  Urban area-of-interest (AOI) refers to an integrated urban functional zone
with defined polygonal boundaries. The rapid development of urban commerce has
led to increasing demands for highly accurate and timely AOI data. However,
existing research primarily focuses on coarse-grained functional zones for
urban planning or regional economic analysis, and often neglects the expiration
of AOI in the real world. They fail to fulfill the precision demands of Mobile
Internet Online-to-Offline (O2O) businesses. These businesses require accuracy
down to a specific community, school, or hospital. In this paper, we propose a
comprehensive end-to-end multimodal deep learning framework designed for
simultaneously detecting accurate AOI boundaries and validating the reliability
of AOI by leveraging remote sensing imagery coupled with geographical prior,
titled AOITR. Unlike conventional AOI generation methods, such as the Road-cut
method that segments road networks at various levels, our approach diverges
from semantic segmentation algorithms that depend on pixel-level
classification. Instead, our AOITR begins by selecting a point-of-interest
(POI) of specific category, and uses it to retrieve corresponding remote
sensing imagery and geographical prior such as entrance POIs and road nodes.
This information helps to build a multimodal detection model based on
transformer encoder-decoder architecture to regress the AOI polygon.
Additionally, we utilize the dynamic features from human mobility, nearby POIs,
and logistics addresses for AOI reliability evaluation via a cascaded network
module. The experimental results reveal that our algorithm achieves a
significant improvement on Intersection over Union (IoU) metric, surpassing
previous methods by a large margin.
