# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images  
[PERSON]  
1 Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China 1  
[PERSON]  
1 Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China 1  
[PERSON]  
2 Sherzhen Graduate School, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China 2  
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3 National Supercomputing Center in Shenzhen, Shenzhen 518055, China 3  
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4 CCSTC (China Construction Science and Technology Cooperation), Low-Carbon & Smart City Technology Co., Ltd., Beijing 100195, China 4  
[PERSON]  
1 Institute of Future Human Habitats, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China 1  
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Though the roof-based distributed PV demonstrates enormous potential for power generation and carbon emission reduction, the roofs of many buildings around the world that can install photovoltaic are idle, which can be proven by taking China's roof condition as an example. In 2020, the total building area of China was approximately 66 billion m\({}^{2}\), including 29.2 billion m\({}^{2}\) for urban residential areas, 22.7 billion m\({}^{2}\) for rural residential areas, and 14 billion m\({}^{2}\) for public buildings. Although the installed roof area of distributed photovoltaic in China has continued to grow in recent years, there are still a large number of building roofs that are idle, which, in theory, could be installed with 830 million kilowatts of photovoltaic, with 123 trillion kWh of power generation annually [14]. As a matter of fact, photovoltaic power generation largely depends on the actual photovoltaic installed capacity, which is strongly correlated with the building roof area. Therefore, to fully utilize these idle roofs, an efficient and accurate method for estimating city-scale building roof area is crucial, which is an important basis for urban energy transformation planning and decision making [15]. Evaluating roof area using satellite images involves two steps: roof segmentation and area mapping. Roof segmentation refers to the use of semantic image segmentation techniques to identify pixels belonging to the roof from satellite remote sensing images, which is crucial for evaluating the roof area. After identifying the pixels belonging to the roof, the area of the roof can be calculated based on parameters such as the scale and shooting angle of the satellite image, which is called area mapping. Therefore, using semantic segmentation technology to accurately identify pixels belonging to the roof from satellite remote sensing images is the core step in evaluating the roof area, and the accuracy of semantic image segmentation is crucial for evaluating the roof area. Researchers have developed several methods to estimate the roof area [16; 17] over the past few decades. Among these methods, machine learning algorithms that integrate with geographic information systems (GIS) are the most popular approach for estimating the roof solar photovoltaic potential of a large region. The general process of machine learning-based methods includes the following: (1) data collection and preprocessing; (2) model design and training; (3) model reasoning and result post-processing. Several satellite imagery segmentation datasets containing buildings such as Massachusetts [18], Inria Aerial Image Labeling [19], WHU [20], LoveDA [21], and ref. [22] have been published by researchers.