Technological Innovation and Emerging Operational Applications in Digital Earth

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Atmosphere atmosphere	2.5	4.6	2010	15.8 Days	CHF 2400	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit

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Published Papers (7 papers)

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All Applied Sciences Atmosphere IJGI Remote Sensing Sustainability

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20 pages, 14310 KiB  

Open AccessArticle

Deep Learning Application for Biodiversity Conservation and Educational Tourism in Natural Reserves

by Marco Flórez, Oscar Becerra, Eduardo Carrillo, Manny Villa, Yuli Álvarez, Javier Suárez and Francisco Mendes

ISPRS Int. J. Geo-Inf. 2024, 13(10), 358; https://doi.org/10.3390/ijgi13100358 - 11 Oct 2024

Viewed by 341

Abstract

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these [...] Read more.

Natural reserves, such as the Santurbán Moor in Colombia, are ecologically important but face significant threats from activities like mining and agriculture. Preserving biodiversity in these ecosystems is essential for maintaining ecological balance and promoting sustainable tourism practices. Identifying plant species in these reserves accurately is challenging due to environmental variability and species similarities, complicating conservation efforts and educational tourism promotion. This study aims to create and assess a mobile application based on deep learning, called FloraBan, to autonomously identify plant species in natural reserves, enhancing biodiversity conservation and encouraging sustainable and educational tourism practices. The application employs the EfficientNet Lite4 model, trained on a comprehensive dataset of plant images taken in various field conditions. Designed to work offline, the application is particularly useful in remote areas. The model evaluation revealed an accuracy exceeding 90% in classifying plant images. FloraBan was effective under various lighting conditions and complex backgrounds, offering detailed information about each species, including scientific name, family, and conservation status. The ability to function without internet connectivity is a significant benefit, especially in isolated regions like natural reserves. FloraBan represents a notable improvement in the field of automated plant identification, supporting botanical research and efforts to preserve biodiversity in the Santurbán Moor. Additionally, it encourages educational and responsible tourism practices, which align with sustainability goals, providing a useful tool for both tourists and conservationists. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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25 pages, 16886 KiB  

Open AccessArticle

A Multiple Targets ISAR Imaging Method with Removal of Micro-Motion Connection Based on Joint Constraints

by Hongxu Li, Qinglang Guo, Zihan Xu, Xinfei Jin, Fulin Su and Xiaodi Li

Remote Sens. 2024, 16(19), 3647; https://doi.org/10.3390/rs16193647 - 29 Sep 2024

Viewed by 520

Abstract

Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation method. ISAR is typically utilized to monitor both military and civilian ships due [...] Read more.

Combining multiple data sources, Digital Earth is an integrated observation platform based on air–space–ground–sea monitoring systems. Among these data sources, the Inverse Synthetic Aperture Radar (ISAR) is a crucial observation method. ISAR is typically utilized to monitor both military and civilian ships due to its all-day and all-weather superiority. However, in complex scenarios, multiple targets may exist within the same radar antenna beam, resulting in severe defocusing due to different motion conditions. Therefore, this paper proposes a multiple-target ISAR imaging method with the removal of micro-motion connections based on the integration of joint constraints. The fully motion-compensated targets exhibit low rank and local similarity in the high-resolution range profile (HRRP) domain, while the micro-motion components possess sparsity. Additionally, targets display sparsity in the image domain. Inspired by this, we formulate a novel optimization by promoting the low-rank, the Laplacian, and the sparsity constraints of targets and the sparsity constraints of the micro-motion components. This optimization problem is solved by the linearized alternative direction method with adaptive penalty (LADMAP). Furthermore, the different motions of various targets degrade their inherent characteristics. Therefore, we integrate motion compensation transformation into the optimization, accordingly achieving the separation of rigid bodies and the micro-motion components of different targets. Experiments based on simulated data demonstrate the effectiveness of the proposed method. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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25 pages, 13967 KiB  

Open AccessArticle

Few-Shot Hyperspectral Remote Sensing Image Classification via an Ensemble of Meta-Optimizers with Update Integration

by Tao Hao, Zhihua Zhang and M. James C. Crabbe

Remote Sens. 2024, 16(16), 2988; https://doi.org/10.3390/rs16162988 - 14 Aug 2024

Viewed by 728

Abstract

Hyperspectral images (HSIs) with abundant spectra and high spatial resolution can satisfy the demand for the classification of adjacent homogeneous regions and accurately determine their specific land-cover classes. Due to the potentially large variance within the same class in hyperspectral images, classifying HSIs [...] Read more.

Hyperspectral images (HSIs) with abundant spectra and high spatial resolution can satisfy the demand for the classification of adjacent homogeneous regions and accurately determine their specific land-cover classes. Due to the potentially large variance within the same class in hyperspectral images, classifying HSIs with limited training samples (i.e., few-shot HSI classification) has become especially difficult. To solve this issue without adding training costs, we propose an ensemble of meta-optimizers that were generated one by one through utilizing periodic annealing on the learning rate during the meta-training process. Such a combination of meta-learning and ensemble learning demonstrates a powerful ability to optimize the deep network on few-shot HSI training. In order to further improve the classification performance, we introduced a novel update integration process to determine the most appropriate update for network parameters during the model training process. Compared with popular human-designed optimizers (Adam, AdaGrad, RMSprop, SGD, etc.), our proposed model performed better in convergence speed, final loss value, overall accuracy, average accuracy, and Kappa coefficient on five HSI benchmarks in a few-shot learning setting. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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29 pages, 15331 KiB  

Open AccessArticle

Dynamic Construction of Spherical Raster Voronoi Diagrams Based on Ordered Dilation

by Qingping Liu, Xuesheng Zhao, Yuanzheng Duan, Mengmeng Qin, Wenlan Xie and Wenbin Sun

ISPRS Int. J. Geo-Inf. 2024, 13(6), 202; https://doi.org/10.3390/ijgi13060202 - 14 Jun 2024

Viewed by 1156

Abstract

The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis. However, traditional algorithms for generating spherical raster [...] Read more.

The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis. However, traditional algorithms for generating spherical raster Voronoi diagrams find it challenging to dynamically adjust the Voronoi diagram while maintaining precision and efficiency. The efficient and accurate construction of the spherical Voronoi diagram has become one of the bottleneck issues limiting its further large-scale application. To this end, this paper proposes a dynamic construction scheme for the spherical Voronoi diagram based on the QTM (Quaternary Triangular Mesh) system, with the aim of enabling efficient generation, local updates, and multi-scale visualization of the spherical Voronoi diagrams. In this paper, canonical ordering is introduced. Tailored for the properties of the spherical triangular grid, it constructs a unified and standardized sorting strategy for the dilation of the spherical grids. The construction and updating of the spherical Voronoi diagram are achieved through the ordered dilation of sites. Furthermore, the multi-scale visualization of the spherical Voronoi diagram is realized through the hierarchical structure of the QTM. The paper presents our algorithm intuitively through pseudocode, conducts comparative experiments on the feasibility and efficiency, and designs an experiment for the dynamic navigation and management of ocean-going vessels based on the global multi-resolution Voronoi diagram. The experimental results demonstrate that our algorithm effectively controls the error of the generation of the raster Voronoi diagram and has a significant efficiency advantage when processing dynamic environments. Full article

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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18 pages, 1585 KiB  

Open AccessArticle

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 - 4 Feb 2024

Viewed by 1769

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

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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14 pages, 257 KiB  

Open AccessArticle

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 2003

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

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

14 pages, 7327 KiB  

Open AccessArticle

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 1471

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

(This article belongs to the Topic Technological Innovation and Emerging Operational Applications in Digital Earth)

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