The Extraction Research of Urban Road Information Based on the High Resolution QuickBird Image

2013 ◽  
Vol 718-720 ◽  
pp. 2136-2141
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Dong Hua Lu ◽  
Dong Hui Zhang
Keyword(s):  
High Resolution ◽  
Information Extraction ◽  
Segmentation Algorithm ◽  
Image Clustering ◽  
Urban Road ◽  
The Road ◽  
Construction Of Knowledge ◽  
Merging Algorithm ◽  
Extraction Algorithm ◽  
Image Merging

This paper studied the key technology of the extraction of urban road information using high-resolution QuickBird image. And this paper discussed the road information extraction algorithms, K-mean image clustering and segmentation algorithm, lambda-schedule image merging algorithm and the construction of knowledge database using spectral and shape features, from three angles, image segmentation algorithm, image merging algorithm and specific road information extraction algorithm. Studies showed that, after the introduction of the spectral and texture information, road extraction method reached a higher area and shape consistency, and provide a valuable reference for related research in the field.

scholarly journals Bridge Extraction Algorithm Based on Deep Learning and High-Resolution Satellite Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Wenbing Yang ◽  
Xiaoqi Gao ◽  
Chunlei Zhang ◽  
Feng Tong ◽  
Guantian Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Satellite Image ◽  
Structural Similarity ◽  
Remote Sensing Images ◽  
The Road ◽  
Extraction Algorithm ◽  
Length Width ◽  
On The Road

This paper proposes a novel method of extracting roads and bridges from high-resolution remote sensing images based on deep learning. Edge detection is performed on the images in the road area along with the road skeleton line, and the result of the detected binary edge is vectorized. The interference of protective belts on both sides of the road, road vehicles, road green belts, traffic signs, etc. and the shadow interference of the bridge itself are eliminated to determine the parallel sides of the road. The bridge features on the road are used to locate the detected bridge and obtain information such as the location, length, width, and direction of the bridge, verifying the experimental results of the Shaoguan Le point images. In addition, in order to learn higher-level road feature information, the algorithm in this paper introduces the hollow convolution and multicore pooling modules. Secondly, the residual refinement network further refines the output of the prediction network to improve the ambiguity of the prediction network results. In addition, in view of the small proportion of road pixels in remote sensing images, the network also integrates binary cross entropy, structural similarity, and intersection ratio loss function to reduce road information loss. The applicability of the proposed study was tested, and the results show that the algorithm is very effective for the extraction of road and bridge targets.

Theoretical progress in the H2+ molecular ion: towards electron to proton mass ratio determinationThis paper was presented at the International Conference on Precision Physics of Simple Atomic Systems, held at École de Physique, les Houches, France, 30 May – 4 June, 2010.

2011 ◽  
Vol 89 (1) ◽  
pp. 103-107 ◽  
Author(s):  
J.-Ph. Karr ◽  
L. Hilico ◽  
V. I. Korobov
Keyword(s):  
High Resolution ◽  
Mass Ratio ◽  
Theoretical Determination ◽  
Atomic Systems ◽  
The Road ◽  
Theoretical Progress ◽  
Proton Mass ◽  
Accuracy Level ◽  
Ratio Determination

High resolution ro-vibrational spectroscopy of H 2+ or HD+ can lead to a significantly improved determination of the electron to proton mass ratio me/mp if the theoretical determination of transition frequencies becomes sufficiently accurate. We report on recent theoretical progress in the description of the hyperfine structure of H 2+ , as well as first steps in the evaluation of radiative corrections at order mα7. Completion of the latter calculation should allow us to reach the projected 10−10 accuracy level and open the road to mass ratio determination.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images
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