A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

Yue, Peng; Gao, Fan; Shangguan, Boyi; Yan, Zheren

doi:10.1080/13658816.2020.1730850

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Data are the other source of computational complexity in LUCC‐CA models. There has been an explosive growth of high‐resolution geospatial data in recent years, with the development of data acquisition technology (Yue, Gao, Shangguan, & Yan, 2020). Using a massive amount of high‐resolution data poses challenges for these models.…”

Section: Introductionmentioning

confidence: 99%

Tensor‐CA: A high‐performance cellular automata model for land use simulation based on vectorization and GPU

Zhuang

Liang

et al. 2021

Transactions in GIS

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With the ability to understand linkages and feedbacks between land use dynamics and human–land relationships, cellular automata (CA) are extensively applied in land use/cover change (LUCC) simulation. However, with complex transition rules and a growing volume of spatial data, conventional serial CA models cannot meet the demands of efficient computation. In this article, a Tensor‐CA model using vectorization and Graphics Processing Unit (GPU) technology based on a tensor computation framework for optimizing multiple LUCC simulations is presented. Complex transition rules of LUCC‐CA models are vectorized and formalized to tensor operations which are effectively solved by GPU. The proposed Tensor‐CA model was applied to LUCC simulations in the Pearl River Delta of China. The experimental results indicate that the proposed model effectively improved the performance compared to Serial‐CA, Parallel‐CA, and GPU‐CA.

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Section: Introductionmentioning

confidence: 99%

Tensor‐CA: A high‐performance cellular automata model for land use simulation based on vectorization and GPU

Zhuang

Liang

et al. 2021

Transactions in GIS

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“…In the analysis of many complex natural systems, e.g. in Astrophysics, Earth and Planetary Sciences [28][29][30][31][32][33][34] , the Machine Learning (ML) paradigms allowed a rapid and growing diffusion of Artificial Intelligence (AI) in all sectors of anthropic and scientific activities. The real challenge of AI is that this sophisticated methodology proved to be decisive for computer tasks apparently easy for people, but difficult to describe formally and extremely time-consuming.…”

mentioning

confidence: 99%

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Donadio

Brescia

Riccardo

et al. 2021

Sci Rep

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Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields.

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A multi-source spatio-temporal data cube for large-scale geospatial analysis

Gao

Yue

Cao

et al. 2022

International Journal of Geographical Information Science

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A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

Cited by 5 publications

References 45 publications

Tensor‐CA: A high‐performance cellular automata model for land use simulation based on vectorization and GPU

Tensor‐CA: A high‐performance cellular automata model for land use simulation based on vectorization and GPU

A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

A multi-source spatio-temporal data cube for large-scale geospatial analysis

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