# MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
## 1 Introduction  
The automatic extraction of roads maps from very high-resolution remote sensing images is an essential and hot research domain, which can be applied to numerous applications that rely on the efficient and real-time updating of road maps, such as navigation, cartography, urban planning, location-based mobile services and autonomous driving. In disaster zones, especially in developing countries, maps and accessibility information are crucial for crisis response. Extracting roads from RSIs is a promising approach and has been studied for decades. Recently, the wide use of convolutional neural networks (CNNs) [1], especially networks with fully-convolutional network (FCN) [2] architecture, has greatly improved the accuracy of road extraction and made the task end-to-end trainable [3]. However, the existing extraction results of road maps are still not satisfactory, which is mainly due to the complex urban traffic environments and special characteristics of roads. Compared with most other ground natural objects with bulk shape, such as buildings and trees, the roads in remote sensing images are narrow, long-span, and can be broadly formulated as elongated regions with similar spectral and texture patterns. Therefore, the road extraction algorithms often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, cloud, etc. Additionally, the similarities often exist between the roads and other ground objects that are difficultto identify, as they look visually similar to targets. To match those features' geometric and physical features, the road extraction methods are expected to have a certain level of optimization of the results to reduce the missing connections and false alarms [4]. Conventional expert-knowledge-based methods, such as knowledge-driven-based, template-matching-based, and object-oriented-based, are mainly adopt to extract roads from remote sensing images [5; 6]. Those hand-crafted methods are often cumbersome in steps and lead to error accumulation problems, since they usually combine multiple algorithms to match the complex features of roads. The fixed hand-crafted criteria based on geometric and physical features are usually unsuitable and inefficient to large volumes of remote sensing data, and they also cannot guarantee the connectivity of roads. With the rapid development of deep learning technologies, the CNN-based methods make it possible to obtain the results of large-scale remote sensing data [4]. Accordingly, the CNN-based methods improve the accuracy of road extraction significantly compared with conventional methods and have been the mainstream in road extraction due to the great feature learning power of CNNs [7]. Road extraction can be viewed as a binary semantic segmentation problem in CNN-based methods. Several works have been proposed and applied successfully for road segmentation tasks, especially some with an encoder-decoder architecture. In [8], a Dense-UNet model with similar architecture of encoder-decoder and dense connection units is proposed to extract the road network from remote sensing images. Combining the deep residual network, pyramid pooling module, and deep decoder, [PERSON] et al. [9] propose a novel deep residual and pyramid pooling network (DRPPNet) for extracting road regions from high-resolution remote sensing images. A dual-attention capsule U-Net (DA-CapsUNet) in [10] is designed for road maps extraction by combining the properties of capsule representations and the features of attention mechanisms. Some other typical works based on modified encoder-decoder structure include [4; 11]. However, there are still challenges in accurately extracting road maps, which are mainly due to the special characteristics of thin and elongated roads that are easily interpreted by tress, buildings and shadows, etc. These difficulties lead to fragmentation of the road segmentation, and the above methods cannot guarantee the connectivity of roads.