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

Cao, Qianqian; Li, Guoqing; Yao, Xiaochuang; Jia, Tao; Yu, Guojiang; Zhang, Lianchong; Xu, Dianguo; Zhang, Hao; Shan, Xiujuan

doi:10.3390/app12157816

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…GF-1 satellite can realize the combination of high resolution and large width at the same time since two panchromatic-multi-spectral cameras and four wide-field-viewing cameras are equipped on the satellite. 47 The images from GF-1 can be obtained from China Resource Satellite Data and Application Center for free. 48 Considering orchard growing calendar, GF-1 images acquired on July 29, 2021, April 14, 2020, and August 27, 2020 are selected.…”

Section: Data Sourcementioning

confidence: 99%

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Luo,

Guo,

Zhu

et al. 2024

J. Appl. Rem. Sens.

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Accurate orchard spatial distribution information can help government departments to formulate scientific and reasonable agricultural economic policies. However, it is prominent to apply remote sensing images to obtain orchard planting structure information. The traditional multidimensional remote sensing data processing, dimension reduction and classification, which are two separate steps, cannot guarantee that final classification results can be benefited from dimension reduction process. Consequently, to make connection between dimension reduction and classification, this work proposes two neural networks that fuse stack autoencoder and convolutional neural network (CNN) at one-dimension and three-dimension, namely one-dimension and three-dimension fusion stacked autoencoder (FSA) and CNN networks (1D-FSA-CNN and 3D-FSA-CNN). In both networks, the front-end uses a stacked autoencoder (SAE) for dimension reduction, and the back-end uses a CNN with a Softmax classifier for classification. In the experiments, based on Google Earth Engine platform, two groups of orchard datasets are constructed using multi-source remote sensing data (i.e., GaoFen-1, Sentinel-2 and GaoFen-1, and GaoFen-3). Meanwhile, DenseNet201, 3D-CNN, 1D-CNN, and SAE are used for conduct two comparative experiments. The experimental results show that the proposed fusion neural networks achieve the state-of-the-art performance, both accuracies of 3D-FSA-CNN and 1D-FSA-CNN are higher than 95%.

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Section: Data Sourcementioning

confidence: 99%

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Luo,

Guo,

Zhu

et al. 2024

J. Appl. Rem. Sens.

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“…The ODC serves as a dynamic gateway to vast repositories of geospatial information. Its versatility and adaptability make it a key asset for researchers, government agencies, and organizations seeking to harness the wealth of Earth observation data for a wide range of applications [5][6][7][8] .…”

Section: Introduction 11 Open Data Cubementioning

confidence: 99%

Research on the communication strategy of data hub

Wenyang,

yi,

Ruopeng

et al. 2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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The Open Data Cube (ODC) is an open-source platform for managing, analyzing, and visualizing Earth observation data, especially satellite Remote Sensing data. We proposed the Data Hub system because a single Data Cube cannot undertake the analysis and research works across various remote sensing domains. The Data Hub is an integrated system built upon multiple Data Cubes. This system is specifically designed to manage multiple Data Cubes and perform tasks such as data management, indexing, and processing of remote sensing data. This paper mainly focuses on the strategy of Data Hub communication with multiple Data Cubes. Communication includes two parts: KeepALive (heartbeat) transfer and metadata synchronization. In order to minimize the network workload on the Data Hub system and identify the optimal communication strategy, we conducted two experiments: experiments on KeepALive frequency and experiments on metadata synchronization strategies. We designed comparative experiments to simulate the computational and metadata synchronization tasks between the Data Hub server and multiple Data Cube servers. Based on the experimental results, we identified the optimal frequency for sending KeepALive signals and gained insights into the impact of network conditions on metadata synchronization strategies (Pull strategy, Push strategy, and Pull-Push hybrid strategy). Finally, we determined the sending frequency of KeepALive packets to be once every three hours and selected the pull-push hybrid strategy as the transfer strategy for the metadata synchronization module.

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“…Digital Earth Africa provides Sentinel-1 backscatter products developed to comply with the CARD4L specifications on AWS [31,32]. Cao et al [33,34] implemented the China Data Cube (CDC) framework for Landsat 8 and Gaofen-1 (GF-1) ARD based on ODC. The ODC-Spatio Temporal Asset Catalogs (STAC) API provides the functionality to access Sentinel-2 imagery on AWS [35].…”

Section: Introductionmentioning

confidence: 99%

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

Kim,

Lee

2023

Applied Sciences

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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.

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

Cited by 6 publications

References 47 publications

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Optimal feature extraction from multidimensional remote sensing data for orchard identification based on deep learning methods

Research on the communication strategy of data hub

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

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