# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 1 Introduction  
Building extraction using high-resolution remote sensing images is a current focus in research. High-resolution remote sensing imaging can achieve commendable results in geographical mapping, coastline extraction, land classification, and geological disaster monitoring. [PERSON], [PERSON] et al. summarized the latest advancements in extracting urban impervious surfaces using high-resolution remote sensing images and provided recommendations for high-resolution imagery [1]. [PERSON], [PERSON] et al. applied deep convolutional neural networks to the land-sea segmentation problem in high-resolution remote sensing images, significantly improving the segmentation results [2]. Investigating land use monitoring, [PERSON], [PERSON] et al. achieved favorable outcomes using a framework based on conditional random fields and fine-tuned CNNs [3]. [PERSON], [PERSON] et al. also employed high-resolution remote sensing images to assess landslide susceptibility [4]. High-resolution remote sensing datasets have become widely used in the remote sensing field and can primarily be categorized into drone remote sensing and satellite remote sensing varieties. Both types of high-resolution remote sensing images offer satisfactory display results. The aerial imagery is clearer since drone remote sensing images can be captured more flexibly, effectively avoiding weather impacts. Researchers such as [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. have preferred using high-resolution drone remote sensing images to build extraction [5, 6]. In practical applications, issues regarding UAV remote sensing coverage and aerial photography costs must still be resolved. Satellite remote sensing images provide large-area coverage, enabling long-termmonitoring of large territories in the process of urbanization. However, the quality of the images is still insufficient compared with aerial images. Since the two data sources are inconsistent, some methods of building extraction are difficult to simultaneously consider. The extraction of buildings from remote sensing images was initially based on the features of the buildings. [PERSON], [PERSON] et al. utilized the color, shape, texture, and shadow of buildings for extraction [7]. This method primarily relies on the features of facilities for extraction, making it inefficient and imprecise. As such, it requires personnel with extensive professional knowledge. In the early 21 st century, the concept of machine learning was introduced into remote sensing. [PERSON], [PERSON] et al. significantly improved the results of building extraction by designing unique feature maps and using random forest and support vector machine algorithms [8]. Using the random forest algorithm, [PERSON], [PERSON] et al. achieved the semantic segmentation of buildings by combining features such as the image's spectrum, texture, geometry, and spatial distribution [9]. Although traditional machine learning methods have improved segmentation accuracy, the manual selection of essential features remains an inevitable challenge [10]. Deep learning models have addressed the issue of feature selection inherent in traditional machine learning, and many researchers are keen on using deep convolutional networks for building extraction. [PERSON], [PERSON] et al. designed a deep convolutional network based on an attention mechanism [11]. They significantly improved the rough-edge segmentation of buildings in high-resolution remote sensing images drawn from the WHU dataset. [PERSON], [PERSON] et al. added a spatial attention mechanism to the intermediate layers of the deep convolutional network and adopted a residual structure to deepen the network [12]. This method achieved higher accuracy on the WHU and INRIA datasets than other mainstream networks. [PERSON], [PERSON] et al. proposed an efficient deep convolutional network with fewer parameters for building extraction and this model achieved commendable results on the Massachusetts and Potsdam datasets [13].