In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.

/pdf/china-data-cube-cdc-for-big-earth-observation-data-practices-1q8j5fnb.pdf

China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned

This study introduces a multi-cloud model that combines private and public cloud services for processing and managing satellite images. The multi-cloud service is established by incorporating private clouds within organizations and integrating them with external public cloud services to utilize the data. Private clouds can maintain data security within an organization or between organizations, while public clouds offer easy processing options for general users with access accounts. The model for the private cloud service utilizes open-source OpenStack software to create virtual machines, allowing users to manage analysis ready data (ARD) of the Korea Multi-Purpose Satellite (KOMPSAT)-3/3A images simultaneously. The public cloud service through Amazon Web Services (AWS) offers four services and uses the Open Data Cube (ODC) to manage data and provide web-based time-series visualization and processing. The model utilizes OpenStack to create virtual machines, and the public cloud service through AWS offers various services using ODC to manage data. A system that handles large amounts of satellite imagery in a multi-cloud environment has benefits such as improved availability, cost savings through open-source, and enhanced scalability. We present a prototyped utilization model that can be used with the ODC user interface (UI) that applies the proposed multi-cloud model. The multi-cloud model of this study can be applied to constructing a country-scale data cube system, that deals with large-scale satellite image data. It can also be applied to systems that need to be built with data that is tailored to a specific user’s needs at any institution.

Prototyping of Utilization Model for KOMPSAT-3/3A Analysis Ready Data Based on the Open Data Cube Platform in Multi-Cloud Computing Environment: A Case Study

Satellite image processing is an intricate task that requires vast computation and data processing, which cannotbe handled by a single computer. Furthermore, the processing of the massive amount of data accumulated bythe satellite is a huge challenge for the end user. Hence, grid computing is the essential platform to provide highcomputing performance at the user end. This article reviews the grid services used for satellite image processingand significant data processing. 

A case study: Grid services for satellite image processing

Satellite image processing is an intricate task that requires vast computation and data processing, which cannot be handled by a single computer. Furthermore, the processing of the massive amount of data accumulated by the satellite is a huge challenge for the end user. Hence, grid computing is the essential platform to provide high computing performance at the user end. This article reviews the grid services used for satellite image processing and significant data processing. 

With the increasing growth of free and open data from the new generation of Earth-Observation (EO) satellites, Earth Observation has entered the Big Data era, and the traditional data management methods face many challenges to keep up with the growth rate of data. The Open Data Cube (ODC) is an open-source geospatial data management and analysis tool designed to enable users to process, analyze, and visualize large amounts of satellite imagery and climate data. However, most ODCs are applied independently in current, and due to difficulties with data transfer, and the managing domain restrictions on algorithms, it’s not easy to communicate with each other. This article proposed the Data Hub architecture for multiple ODC interconnection. Data Hub enables data interconnection and algorithm inter-operation between ODCs and supports the organization of workflow to build Remote Sensing applications based on multiple ODCs. To demonstrate the working mechanism of multiple ODC interconnection, we organized an application that performed the NDVI algorithm on Shennongjia data with the architecture above.

Research on the Architecture of Multiple Data Center Interconnection based on ODC

With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cube (ODC) grids, Analysis Ready Data (ARD) generation, and space subdivision, which extended the data loading and processing capacities of the ODC grids for Chinese satellite imagery data. Using the proposed framework, we conducted a case study to investigate the spatial and temporal changes in vegetation and water mapping with GF-1 data collected from 2014 to 2021 covering the Miyun Reservoir, Beijing, China. The experimental results showed that the proposed framework had significantly improved temporal and spatial efficiency compared with the traditional scene-based data management approach, thus demonstrating the advantages and potential of the ODC grids as a new data management paradigm.

/pdf/gf-1-satellite-imagery-data-service-and-application-based-on-272ltufa.pdf

GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube

Driven by the wave of urban informatization and the rise of data science, the development of smart city not only needs huge geospatial data as the carrier, but also needs efficient spatial retrieval technology to promote the development of smart city. As a branch of big data, geospatial big data is rising rapidly all over the world, and the era of spatial big data has come. In this paper, particle swarm optimization algorithm is introduced to improve the data clustering algorithm, and a big data optimization clustering algorithm based on particle swarm spatial reorganization is proposed. Particle swarm optimization algorithm is a population-based adaptive random optimization algorithm, which originates from Kennedy and eberbar’s research on the foraging behavior of bird groups. Now particle swarm optimization algorithm is widely used in the fields of pattern recognition, data mining and intelligent control. Based on particle swarm optimization, this paper studies the efficient index and retrieval algorithm of geospatial big data. Particle swarm spatial reorganization method is used to reconstruct and extract the feature vector of big data information flow and realize optimal clustering. Finally, the performance test is carried out through the simulation experiment which shows the better data clustering performance.

Efficient Index and Retrieval Algorithm of Geospatial Big Data based on Particle Swarm Optimization

The interactions between soil and water are ubiquitous in reality, and the infiltration or flush by water often leads to soil’s deformation and failure, which are very common in film scenes. In recent years, the material point method (MPM) has been applied to animate different materials with convincing effects, but soils, as a kind of widely spread natural material too, are relatively less considered yet. In this paper, we propose an optimized MLS-MPM to model soil-water mechanics. In our method, soil stress is updated with the increments of strain and vorticity, and a Drucker-Prager model with tension cut-off is employed to model the linear isotropic poroelasticity of soils. The plasticity model is implemented with a return mapping algorithm, being $$O(n^3)$$ faster than the traditional way. The soil-water coupling scheme is achieved by momenta exchange between them following Darcy’s law. The method shows a great boost by reducing the number of matrix multiplication operations when computing stress and updating deformation gradient. The efficacy of our method is validated by a set of test scenes and benchmarks. 

An Optimized Material Point Method for Soil-Water Coupled Simulation

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GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube

Efficient Index and Retrieval Algorithm of Geospatial Big Data based on Particle Swarm Optimization

An Optimized Material Point Method for Soil-Water Coupled Simulation