Kavli Affiliate: Xiang Zhang
| First 5 Authors: Weijia Li, Jinhua Yu, Dairong Chen, Yi Lin, Runmin Dong
| Summary:
In this work, we propose a geometry-aware semi-supervised framework for
fine-grained building function recognition, utilizing geometric relationships
among multi-source data to enhance pseudo-label accuracy in semi-supervised
learning, broadening its applicability to various building function
categorization systems. Firstly, we design an online semi-supervised
pre-training stage, which facilitates the precise acquisition of building
facade location information in street-view images. In the second stage, we
propose a geometry-aware coarse annotation generation module. This module
effectively combines GIS data and street-view data based on the geometric
relationships, improving the accuracy of pseudo annotations. In the third
stage, we combine the newly generated coarse annotations with the existing
labeled dataset to achieve fine-grained functional recognition of buildings
across multiple cities at a large scale. Extensive experiments demonstrate that
our proposed framework exhibits superior performance in fine-grained functional
recognition of buildings. Within the same categorization system, it achieves
improvements of 7.6% and 4.8% compared to fully-supervised methods and
state-of-the-art semi-supervised methods, respectively. Additionally, our
method also performs well in cross-city scenarios, i.e., extending the model
trained on OmniCity (New York) to new cities (i.e., Los Angeles and Boston)
with different building function categorization systems. This study offers a
new solution for large-scale multi-city applications with minimal annotation
requirements, facilitating more efficient data updates and resource allocation
in urban management.
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