Scale-Invariant Multi-Level Context Aggregation Network for Weakly Supervised Building Extraction

Weakly supervised semantic segmentation (WSSS) methods, utilizing only image-level annotations, are gaining popularity for automated building extraction due to their advantages in eliminating the need for costly and time-consuming pixel-level labeling. Class activation maps (CAMs) are crucial for weakly supervised methods to generate pseudo-pixel-level labels for training networks in semantic segmentation. However, CAMs only activate the most discriminative regions, leading to inaccurate and incomplete results. To alleviate this, we propose a scale-invariant multi-level context aggregation network to improve the quality of CAMs in terms of fineness and completeness. The proposed method has integrated two novel modules into a Siamese network: (a) a self-attentive multi-level context aggregation module that generates and attentively aggregates multi-level CAMs to create fine-structured CAMs and (b) a scale-invariant optimization module that cooperates with mutual learning and coarse-to-fine optimization to improve the completeness of CAMs. The results of the experiments on two open building datasets demonstrate that our method achieves new state-of-the-art building extraction results using only image-level labels, producing more complete and accurate CAMs with an IoU of 0.6339 on the WHU dataset and 0.5887 on the Chicago dataset, respectively.

Automated summary

Weakly supervised semantic segmentation methods using only image-level annotations are gaining popularity in automated building extraction. Class activation maps (CAMs) are crucial for these methods but often result in inaccurate and incomplete results. We propose a scale-invariant multi-level context aggregation network to improve the quality of CAMs, achieving state-of-the-art results on building extraction with high completeness.