Strip attention networks for road extraction
WebApr 8, 2024 · In general, existing deep learning road extraction methods mainly have the following improvement strategies: increasing the receptive field of the deep network, mining the spatial relationship of the road from the self-attention structure, and retaining feature information from multi-scale features. 2.3. Attention Mechanisms WebCoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery CoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery IEEE Trans Image Process. 2024;30:8540-8552. doi: 10.1109/TIP.2024.3117076. Epub 2024 Oct 13. Authors Jie Mei , Rou-Jing Li , Wang Gao , Ming-Ming Cheng PMID: 34618672
Strip attention networks for road extraction
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WebAt present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the … WebMar 8, 2024 · Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of …
WebOct 7, 2024 · The Seg-Road uses a transformer structure to Extract the long-range dependency and global contextual information to improve the fragmentation of road … WebJun 1, 2024 · Extracting road maps from high-resolution optical remote sensing images has received much attention recently, especially with the rapid development of deep learning methods. However, most of these CNN based approaches simply focused on multi-scale encoder architectures or multiple branches in neural networks, and ignored some inherent …
WebA novel road extraction network, abbreviated HsgNet, based on high-order spatial information global perception network using bilinear pooling is proposed, which has fewer … WebNov 19, 2024 · Since the strip convolution is more aligned with the shape of roads, which are long-span, narrow, and distributed continuously. We develop a strip convolution module (SCM) that leverages four strip convolutions to capture long-range context information from different directions and avoid interference from irrelevant regions.
WebAug 1, 2024 · The earliest neural network-based road extraction method in the last ten years in our review is the work proposed by Yuan et al. (2011), which designed a network named LEGION to stimulate local and suppress global. The deep learning-based methods have gap years between 2011 and 2024, during which few deep learning-based road extraction …
Web1) A new multistage framework is proposed for simultane- ous road surface and centerline extraction from remote sensing imagery, which aggregates both the semantic and topological information of road networks by com- bining the strengths of CNN-based segmentation and tracing. supersonic inc californiaWebApr 4, 2024 · A network (MSPFE-Net) based on multi-level strip pooling and feature enhancement, which aggregates long-range dependencies of different levels to ensure the connectivity of the road. Road extraction is a hot task in the field of remote sensing, and it has been widely concerned and applied by researchers, especially using deep learning … supersonic ionic hd03WebNov 29, 2024 · Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U … supersonic in mphWebA multi-stage road extraction method for surface and centerline detection - GitHub - astro-ck/Road-Extraction: A multi-stage road extraction method for surface and centerline detection ... which then are utilized to track consecutive and complete road networks through an iterative search strategy embedded in a convolutional neural network (CNN). supersonic intakeWebNov 19, 2024 · Extracting roads from satellite imagery is a promising approach to update the dynamic changes of road networks efficiently and timely. However, it is challenging due … supersonic internet south africaWebStrip Attention Networks for Road Extraction Hai Huan 1, * , Yu Sheng 2 , Yi Zhang 3 and Yuan Liu 2 1 School of Artificial Intelligence, Nanjing University of Information Science and Technology, supersonic iq-235twswhtWebWe developed a road augmentation module (RAM) to capture the semantic shape information of the road from four strip convolutions. Deformable attention module (DAM) combines the sparse sampling capability of deformable convolution with the spatial self-attention mechanism. supersonic iq-235tws