Remote sensing cloud computing platform development and Earth science application

FU, Dongjie; XIAO, Han; Su, Fenzhen; Zhou, Chenghu; DONG, Jinwei; ZENG, Yelu; Yan, Kai; Li, S.; Wu, Jin; Wu, Wenbin; Yan, F.

doi:10.11834/jrs.20210447

Cited by 22 publications

(6 citation statements)

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“…σ 0 dB = 10 × lg P I (Quali f yValue/32767) 2 − K dB (4) where P I = I 2 + Q 2 and I and Q are the real and imaginary parts of the GF-3 SAR data, respectively, in the product of L1A. The QualifyValue and K dB , on the other hand, are constants that can be found in the field of <QualifyValue> and <CalibrationConst>, respectively, in the *.meta.xml file of the GF-3 L1A product.…”

Section: Methodology 231 Feature Extractionmentioning

confidence: 99%

“…When Albert Gore, former vice president of the United States, proposed "big data for the earth", satellite remote sensing was entering the era of big data [3]. Since the successful launch of the first US meteorological satellite in 1960, there have been 558 earth observation satellites launched, as of September 2020 [4]. China, for instance, has more than 500 satellites in orbit, as of 24 April 2022.…”

Section: Introduction 1remote-sensing Big Datamentioning

confidence: 99%

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Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

Cai

Tian

et al. 2023

Sustainability

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High-resolution remote-sensing images can be used in human activity analysis and criminal activity monitoring, especially in sparsely populated zones. In this paper, we explore the applicability of China’s Gaofen satellite images in the land cover classification of Xinjiang, China. First of all, the features of spectral reflectance and a normalized radar cross section (NRCS) for different types of land covers were analyzed. Moreover, the seasonal variation of the NRCS in SAR (Synthetic Aperture Radar) images for the study area, Dunkuotan Village of Yuli County, China, was demonstrated by the GEE (Google Earth Engine) platform accordingly. Finally, the CART (classification and regression trees) algorithm of a DT (decision tree) was applied to investigate the classification of land cover in the western area of China when both optical and SAR images were employed. An overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed by using GF-2/GF-3 images (2017–2021) in the study area. The DT-based classification procedure proposed in this investigation proved that Gaofen series remote-sensing images can be engaged to effectively promote the routine workflow of the administrative department.

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Section: Methodology 231 Feature Extractionmentioning

confidence: 99%

Section: Introduction 1remote-sensing Big Datamentioning

confidence: 99%

Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

Cai

Tian

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…These extensive high-resolution datasets provide more rich geographical information, allowing for a more thorough exploration of geographic features. This, in turn, facilitates comprehensive studies of the Earth system and the discovery of more intricate relationships [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

Zhang,

Ma,

Zhang

et al. 2024

Sensors

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Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms.

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“…Especially in batch processing and long-term data analysis, cloud platforms offer replicability, scalability, and efficient resource utilization [52,96,97], which substantially reduce the time required for long-term RSEI mapping. Meanwhile, remote sensing cloud computing platforms make it easier for researchers to publish their research results for decision-makers and even the public [98].…”

Section: Advantages Of Cloud Computing Platforms and Comparison With ...mentioning

confidence: 99%

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China

Zeng,

Dai,

et al. 2024

Sustainability

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The environmental quality of a mining city has a direct impact on regional sustainable development and has become a key indicator for assessing the effectiveness of national environmental policies. However, against the backdrop of accelerated urbanization, increased demand for resource development, and the promotion of the concept of ecological civilization, mining cities are faced with the major challenge of balancing economic development and ecological environmental protection. This study aims to deeply investigate the spatial and temporal variations of environmental quality and its driving mechanisms of mineral resource-based cities. This study utilizes the wide coverage and multitemporal capabilities of MODIS optical and thermal infrared remote sensing data. It innovatively develops the remote sensing ecological index (RSEI) algorithm on the PIE-Engine cloud platform to quickly obtain the RSEI, which reflects the quality of the ecological environment. The spatial and temporal evolution characteristics of the environmental quality in seven typical mining cities in China from 2001 to 2022 were analyzed. Combined with the vector mine surface data, the spatial and temporal variability of the impacts of mining activities on the ecological environment were quantitatively separated and explored. In particular, the characteristics of mining cities were taken into account by creating buffer zones and zoning statistics to analyze the response relationship between RSEI and these factors, including the distance to the mining area and the percentage of the mining area. In addition, the drivers and impacts of RSEI in 2019 were analyzed through Pearson correlation coefficients pixel by pixel with 10 factors, including natural, economic, and mining. Regression modeling of RSEI in 2019 was performed using the random forest (RF) model, and these drivers were ranked in order of importance through random forest factor importance assessment. The results showed that (1) the ecological quality of mining cities changed significantly during the study period, and the negative impacts of mining activities on the ecological environment were significant. (2) The areas with low RSEI values were closely related to the mining areas and cities. (3) The RSEI in the mining areas of mining cities was generally lower than the average level of the cities. The RSEI gradually increased as the distance to the mine site increased. (4) The increase in the size of the mine area initially exacerbates the impact on the ecological environment, but the impact is weakened beyond a certain threshold. (5) The distance to the mining area is the most important factor affecting the quality of the ecological environment, followed by DEM, GDP, and precipitation. This study is of great importance for advancing sustainable development in mining cities and formulating sustainable strategies.

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Remote sensing cloud computing platform development and Earth science application

Cited by 22 publications

References 0 publications

Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China

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