# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 7 Conclusions  
In this study, we analyze the difficulties of the existing task of segmenting roofs from satellite images, including the shortcomings of existing models and the problems of existing datasets in this task. To address the shortcomings of existing models, we propose an attention-based full-scale fusion (AFSF) network to segment roofs more accurately from satellite images. Specifically, we develop an attention-based residual ulbock to model the channel relationship of the feature maps. By integrating attention mechanisms in multi-scale feature fusion, our model learns different weights for features of different scales. In addition, we propose the ladder-like network to utilize weakly labeled data; therefore, the pixel-level semantic segmentation tasks are assisted by the image-level classification tasks. In order to train the model to segment the roof instead of the entire building, we constructed a new roof segmentation dataset that provides accurate roof segmentation masks. The experimental results on the new roof segmentation dataset and WHU dataset demonstrate the effectiveness of the proposed AFSF network. Conceptualization, H.Q., Y.Z. and L.C. ; Methodology, L.C., Q.M. and F.Q. ; Dataset, Z.L. ; Experiment, L.C. and F.Q. ; Writing--original draft, L.C. ; Writing--review and editing, Y.Z., Q.M. and Z.L. All authors have read and agreed to the published version of the manuscript. This research was supported in part by Key Research Program of China Construction (CSCEC-2021-J-2); Key Research Program of China Southern Power Grid (090000 KK52210134); Tsinghua SIGS-CCSTG Future City Joint Lab funding; Research and Development Project of Ministry of Housing and Urban-Rural Development of the People's Republic China (Grant No. 2022-K-121). Not applicable.Not applicable.Not applicable.Not applicable.Not applicable.Not applicable.Not available.Not available. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations. _Sustainability_**2023**, _15_, 3380. [CrossRef]
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