Overview of Logistics Equilibrium Distribution Networks System: An Urban Perspective

Wang, Wei; Rahman, Arafatur; Ali, Jahan; Bhuiyan, Zakirul Alam; Yao, Liu; Tao, Hai

doi:10.1007/978-3-030-05345-1_24

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…learning methods are beginning to be applied at the fogend [1]- [4]. While, a comprehensive dataset for geometric and semantic understanding from bird' eye view still hasn't appeared, it's mainly because large-scale 3D acquiring and 2D labeling are all costly.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Synthetic Urban Dataset for Aerial Scene Understanding

2020

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The geometric extraction and semantic understanding in bird's eye view plays an important role in cyber-physical-social systems (CPSS), because it can help human or intelligent agents (IAs) to perceive larger range of environment. Moreover, due to lack of comprehensive dataset from oblique perspective, fogend deep learning algorithms for this purpose is still in blank. In this paper, we propose a novel method to generate synthetic large-scale dataset for geometric and semantic urban scene understanding from bird's eye view. There are two main steps involved, one is modeling and the other is rendering, which are processed by CityEngine and UnrealEngine4 respectively. In this way, synthetic aligned multi-model data are obtained efficiently, including spectral images, semantic labels, depth and normal maps. Specifically, terrain elevation, street graph, building style and trees distribution are all randomly generated according realistic situation, a few of handcrafted semantic labels annotated by colors spread throughout the scene, virtual cameras moved according to realistic trajectories of unmanned aerial vehicles (UAVs). For evaluation of practicability of our dataset, we manually labeled tens of aerial images downloaded from internet. And the experiment result show that, in both pure and combined mode, the dataset can improve the performance significantly.

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Section: Introductionmentioning

confidence: 99%