An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization

Qiu, Linyao; Zhu, Qing; Du, Zhiqiang; Wang, Meng; Fan, Yida

doi:10.3390/ijgi6010008

Cited by 5 publications

(1 citation statement)

References 40 publications

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“…The core of 3D model data organisation is the 3D spatial index taking into account the LOD. Common index structures include binary space partitioning, K-tree, bounding volume hierarchies, grid partitioning, quadtree, octree, R-tree etc [11][12][13][14][15]. The nodes of the R-tree usually represent bounding boxes, which are used to organise and manage spatial objects.…”

Section: Introductionmentioning

confidence: 99%

A large‐scale urban 3D model organisation method considering spatial distribution of buildings

Yang,

Huo,

Shen

et al. 2023

IET Smart Cities

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The rendering of urban 3D scenes involves a large number of models. In order to render scenes more efficiently, the main solution is to build a level of detail model (LOD). This may have the problem of building fragmentation, while relying on building a level of detail model (LOD) alone cannot meet the accuracy and fluency of large‐scale scene visualisation. Effective and reasonable data organisation has important research significance for the authors to achieve accurate and fast rendering of scenes. Therefore, the authors propose a large‐scale city model data organisation method considering building distribution to solve the above problems. This method first classifies the buildings in the scene at macro‐, meso‐ and microscales and records the classification using R‐trees. Then an adaptive quadtree is used to construct the data index of the city model. Finally, the data organisation of the large‐scale 3D city model is achieved by using the information of each node of the R‐tree as a constraint and combining with the adaptive quadtree. The results show that the method not only ensures the integrity of the user's area of interest but also can improve the efficiency of 3D scene construction.

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Section: Introductionmentioning

confidence: 99%

A large‐scale urban 3D model organisation method considering spatial distribution of buildings

Yang,

Huo,

Shen

et al. 2023

IET Smart Cities

View full text Add to dashboard Cite

show abstract

The latest progress of data fusion for integrated disaster reduction intelligence service

Liu

Konečný

et al. 2021

International Journal of Image and Data Fusion

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How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

338

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization

Cited by 5 publications

References 40 publications

A large‐scale urban 3D model organisation method considering spatial distribution of buildings

A large‐scale urban 3D model organisation method considering spatial distribution of buildings

The latest progress of data fusion for integrated disaster reduction intelligence service

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

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