SD-BASNet：a building extraction network for high-spatial-resolution remote sensing imagery

doi:10.6046/zrzyyg.2024209

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Abstract

In response to the challenges posed by substantial parameters and the loss of building details during downsampling，this study，inspired by lightweight networks，designed a building extraction network （SD-BASNet） incorporating depthwise separable residual blocks and dilated convolution. First，a depthwise separable residual block was designed in the prediction module of the deep supervision encoder-decoder. Depthwise separable convolution was incorporated into the backbone ResNet to prevent oversized convolutional kernels and reduce the number of network parameters. Second，to mitigate the potential decline in accuracy due to network lightweighting，dilated convolution was integrated into the encoder layer of the post-processing optimization module. This strategy effectively expands the receptive field of feature maps，thereby capturing broader contextual information and enhancing the accuracy of building feature extraction. Experiments on the WHU building dataset showed that the proposed network achieved an mIoU of 92.25%，an mPA of 96.59%，a Recall of 96.50%，a Precision of 93.79%，and a F1-score of 92.61%. Compared with current semantic segmentation networks，including PSPNet，SegNet，DeepLabV3，SE-UNet，and UNet++，the SD-BASNet demonstrated significantly improved accuracy and better completeness of building extraction. Compared with the baseline BASNet，the SD-BASNet also exhibited reductions in both parameter count and runtime，demonstrating its effectiveness.

Keywords building extraction high-spatial-resolution remote sensing imagery boundary-aware salient object detection （BASNet） depthwise separable residual block dilated convolution

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Issue Date: 28 October 2025

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	Juanjuan ZHU
	Liang HUANG
	Shasha ZHU

Cite this article:

Juanjuan ZHU,Liang HUANG,Shasha ZHU. SD-BASNet：a building extraction network for high-spatial-resolution remote sensing imagery[J]. Remote Sensing for Natural Resources, 2025, 37(5): 122-130.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024209 OR https://www.gtzyyg.com/EN/Y2025/V37/I5/122

Fig.1 Network structure of SD-BASNet

Fig.2 Comparision of standard convolution and DSC

Fig.3 Structures of DSRMs

Fig.4 Schematic diagram of receptive fields with different dilation rates^[21]

Tab.1 Analysis of parameter quantity in different networks

Tab.2 Detection results of different networks （%）

Fig.5 Example drawings of buildings in different scenarios

Tab.3 Detection results of small-scale building （%）

Fig.6 Extraction result of small-scale buildings

Tab.4 Detection results of multi-scale building （%）

Fig.7 Extraction result of multi-scale buildings

Tab.5 Detection results of large-scale building （%）

Fig.8 Extraction result of large-scale buildings

Tab.6 Detection results of ablation tests （%）

Fig.9 Comparison of ablation experiment results

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