Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments

Gadipudi, Nivesh; Elamvazuthi, Irraivan; Lu, Cheng-Kai; Paramasivam, Sivajothi; Su, Steven W.

doi:10.1007/s00521-022-07484-y

Cited by 2 publications

(1 citation statement)

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“…If improperly treated, the sludge with heavy metals and other toxic and harmful substances will inevitably cause secondary pollution.Nivesh Gadipudi, through a field survey of Wenzhou city, illustrates the current situation and effectiveness of the environmental management of urban sludge in Wenzhou city, and analyzes the problems and dilemmas in the environmental treatment of urban sludge in China. Finally, the development prospect of urban sludge treatment industry in China is predicted from four aspects [7]. Therefore, it is of practical significance to study the coordinated development of urban development and nature conservation environment based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

2022

NEP

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A good environment can provide material security for urban development, facilitate the efficient use of energy resources, and attract large amounts of investment, bring technological innovation, and thus promote the pace of urbanization. On the contrary, a bad ecological environment restricts the development of urbanization, and the image, development speed, construction scale, investment environment and resource supply of cities are affected. The purpose of this paper is to study the coordinated development of urban development and nature conservation environment based on machine learning. The ecological city theory and the kinds of machine learning algorithms are analyzed. Among the mutual constraints of urbanization and ecological environment studied by using gray correlation matrix as well as coordination degree, the experimental results show that the unreasonable industrial structure is an important factor causing serious industrial pollution in M city.

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Section: Introductionmentioning

confidence: 99%

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

2022

NEP

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A review on monocular tracking and mapping: from model-based to data-driven methods

et al. 2022

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Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments

Cited by 2 publications

References 44 publications

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

Harmonization of Urban Development and Nature Conservation Environment Based on Machine Learning

A review on monocular tracking and mapping: from model-based to data-driven methods

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