  In this research, we introduce the enhanced automated quality assessment
network (IBS-AQSNet), an innovative solution for assessing the quality of
interactive building segmentation within high-resolution remote sensing
imagery. This is a new challenge in segmentation quality assessment, and our
proposed IBS-AQSNet allievate this by identifying missed and mistaken segment
areas. First of all, to acquire robust image features, our method combines a
robust, pre-trained backbone with a lightweight counterpart for comprehensive
feature extraction from imagery and segmentation results. These features are
then fused through a simple combination of concatenation, convolution layers,
and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale
differential quality assessment decoder, proficient in pinpointing areas where
segmentation result is either missed or mistaken. Experiments on a newly-built
EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the
superiority of the proposed method in automating segmentation quality
assessment, thereby setting a new benchmark in the field.
