Topic Editors

School of Mathematics, Shandong University, Jinan 250100, China
Wolfson College, Oxford University, Oxford OX2 6UD, UK

Technological Innovation and Emerging Operational Applications in Digital Earth

Abstract submission deadline
17 July 2024
Manuscript submission deadline
17 October 2024
Viewed by
3768

Topic Information

Dear Colleagues,

Data science has unanimously been recognized as a core driver for achieving the Sustainable Development Goals (SDGs) of the United Nations. Digital Earth can fully utilize data-driven technology to understand the inner mechanisms of the global environmental system as well as its impacts on economy and society. With the explosive growth of air–space–ground–sea integrated monitoring systems, the integrated observation platform of Digital Earth is developed to incorporate various active and passive microwave, visible, and infrared satellite remote sensing data sources as well as in situ observation sources. The size of observation data warehouses in Digital Earth systems is increasing sharply on terabyte, petabyte, and even exabyte scales. At the same time, with the rapid development of next-generation Earth simulators, the simulator platform of the Digital Earth is used to predict future environmental evolution and assess resulting social and societal impacts. Since more complex physical, chemical, and biological processes need to be included in higher-resolution modeling, increasing the resolution of Earth simulators by a factor of two means that about ten times as much computing power will be needed and results in the size of simulation data warehouses for Digital Earth also increasing sharply. Currently, emerging technologies, including data mining, deep learning, blockchain, cloud computing, and the internet of things, are being incorporated into Digital Earth. The Digital Earth is becoming a multidimensional, multiscale, multitemporal, and multilayered data-driven dynamic platform to model the Earth system, drive new geoinformation discoveries, and support social service issues within the three United Nations frameworks of sustainable development goals, climate change, and disaster risk reduction, as well as in the development of digital economies. This topic collection will provide an efficient and high-quality platform for promoting Digital Earth sharing, processing, mining, and analyses across the entire spectrum of Earth and environmental sciences, thereby revolutionizing the cognition of the Earth's systems.

Prof. Dr. Zhihua Zhang
Prof. Dr. M. James C. Crabbe
Topic Editors

Keywords

  • digital earth
  • climate change
  • environmental evolution
  • remote sensing observation
  • geo-information system

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Atmosphere
atmosphere
2.9 4.1 2010 17.7 Days CHF 2400 Submit
ISPRS International Journal of Geo-Information
ijgi
3.4 6.2 2012 35.5 Days CHF 1700 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (3 papers)

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18 pages, 1585 KiB  
Article
A Time-Identified R-Tree: A Workload-Controllable Dynamic Spatio-Temporal Index Scheme for Streaming Processing
by Weichen Peng, Luo Chen, Xue Ouyang and Wei Xiong
ISPRS Int. J. Geo-Inf. 2024, 13(2), 49; https://doi.org/10.3390/ijgi13020049 - 04 Feb 2024
Viewed by 1205
Abstract
Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughput, and real-time performance. The use of streaming processing [...] Read more.
Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughput, and real-time performance. The use of streaming processing techniques for spatio-temporal data applications is a promising research direction. However, due to the strong dynamic nature of data in streaming processing systems, traditional spatio-temporal indexing techniques based on relatively static data cannot be used directly in stream-processing environments. It is necessary to study and design new spatio-temporal indexing strategies. Hence, we propose a workload-controllable dynamic spatio-temporal index based on the R-tree. In order to restrict memory usage, we formulate an INSERT and batch-REMOVE (I&BR) method and append a collection mechanism to the traditional R-tree. To improve the updating performance, we propose a time-identified R-tree (TIR). Moreover, we propose a distributed system prototype called a time-identified R-tree farm (TIRF). Experiments show that the TIR could work in a scenario with a controllable usage of memory and a stable response time. The throughput of the TIRF could reach 1 million points per second. The performance of a range search in the TIRF is many times better than in PostgreSQL, which is a widely used database system for spatio-temporal applications. Full article
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14 pages, 257 KiB  
Article
Digital Mergers and Acquisitions and Enterprise Innovation Quality: Analysis Based on Research and Development Investment and Overseas Subsidiaries
by Helian Xu and Shiqi Deng
Sustainability 2024, 16(3), 1120; https://doi.org/10.3390/su16031120 - 29 Jan 2024
Viewed by 875
Abstract
Utilizing a hand-collected dataset on digital cross-border mergers and acquisitions (M&As), we conducted an exploratory study about the effect of digital overseas M&As on the innovative quality of acquiring enterprises. Based on the digital cross-border M&A behavior of Chinese listed firms from 2010 [...] Read more.
Utilizing a hand-collected dataset on digital cross-border mergers and acquisitions (M&As), we conducted an exploratory study about the effect of digital overseas M&As on the innovative quality of acquiring enterprises. Based on the digital cross-border M&A behavior of Chinese listed firms from 2010 to 2022, we offer original and robust evidence that reveals that enterprises engaging in digital cross-border M&As are more likely to produce high-quality innovations and services, and this effect may be moderated by human capital. Our explorations specifically reveal that the increase in quality of innovation from digital cross-border M&As could occur through research and development (R&D) investment and overseas subsidiaries. In addition, we found that the positive effect is especially pronounced in enterprises located in the Eastern and Western regions, and it also exists among high-tech enterprises, relatively large-scale enterprises, and digital-acquiring enterprises. We conclude by discussing how important it is for M&A enterprises to use digital technology to shape innovation quality. Full article
14 pages, 7327 KiB  
Article
Radar-SR3: A Weather Radar Image Super-Resolution Generation Model Based on SR3
by Zhanpeng Shi, Huantong Geng, Fangli Wu, Liangchao Geng and Xiaoran Zhuang
Atmosphere 2024, 15(1), 40; https://doi.org/10.3390/atmos15010040 - 29 Dec 2023
Viewed by 852
Abstract
To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via image restoration and recognition) for radar images is proposed. [...] Read more.
To solve the problems of the current deep learning radar extrapolation model consuming many resources and the final prediction result lacking details, a weather radar image super-resolution weather model based on SR3 (super-resolution via image restoration and recognition) for radar images is proposed. This model uses a diffusion model to super-resolve weather radar images to generate high-definition images and optimizes the performance of the U-Net denoising network on the basis of SR3 to further improve image quality. The model receives high-resolution images with Gaussian noise added and performs channel splicing with low-resolution images for conditional generation. The experimental results showed that the introduction of the diffusion model significantly improved the spatial resolution of weather radar images, providing new technical means for applications in related fields; when the amplification factor was 8, Radar-SR3, compared with the image super-resolution model based on the generative adversarial network (SRGAN) and the bicubic interpolation algorithm, the peak signal-to-noise ratio (PSNR) increased by 146% and 52% on average. According to this model, it is possible to train radar extrapolation models with limited computing resources with high-resolution images. Full article
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