Titlebook: Spatial Data and Intelligence; 4th International Co Xiaofeng Meng,Xiang Li,Yafei Li Conference proceedings 2023 The Editor(s) (if applicabl

一窝小鸟

阐明

DeepParking: Deep Learning-Based Planning Method for Autonomous Parkingred to be reasonably performed in a very limited space. Moreover, since unstructured parking scenarios are lack of significant common features, creating useful heuristics manually to adapt to changing conditions is a non-trivial task. Therefore, we propose a two-stage scheme, Deep Neural Networks ba

seruting

Recommendations for Urban Planning Based on Non-motorized Travel Data and Street Comfortn and global warming. Based on this, this paper is dedicated to conducting research on improving the attractiveness of outdoor environmental spaces and improving outdoor thermal comfort. The main work of this paper is first to propose a street comfort model by considering both environmental and clim

resilience

A Composite Grid Clustering Algorithm Based on Density and Balance Degreeof bikes and negatively impact the user experience and the operating costs of bike-sharing companies. To address these challenges, bike-sharing companies can create temporary parking stations or electronic fencing and implement bicycle rebalancing strategies across districts. However, these strategi

赤字

音乐学者

FAR

明确

Ship Classification Based on Trajectories Data and LightGBM Considering Offshore Distance Feature features extracted by the existing ship classification methods are motion features, which ignore the spatial relation between the vessels and the coastline, a Method based on LightGBM (Light Gradient Boosting Machine) for ship classification considering the offshore distance features is proposed. F

我不怕牺牲

CDGCN: An Effective and Efficient Algorithm Based on Community Detection for Training Deep and Largeor large scale graphs is trained by full-batch stochastic gradient descent, which causes two problems: over-smoothing and neighborhood expansion, which may lead to loss of model accuracy and high memory and computational overhead. To alleviate these two challenges, we propose CDGCN, a novel GCN algo

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