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  1. Article ; Online: HexTile

    Xiaochuang Yao / Guojiang Yu / Guoqing Li / Shuai Yan / Long Zhao / Dehai Zhu

    ISPRS International Journal of Geo-Information, Vol 12, Iss 89, p

    A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

    2023  Volume 89

    Abstract: The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an ... ...

    Abstract The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.
    Keywords HexTile ; DGGS ; big remote sensing data ; visualization ; Spark ; Geography (General) ; G1-922
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities

    Guojiang Yu / Zixuan Xie / Xuecao Li / Yixuan Wang / Jianxi Huang / Xiaochuang Yao

    Remote Sensing, Vol 14, Iss 2087, p

    A Case Study from 38 Cities in China

    2022  Volume 2087

    Abstract: Urban forms are closely related to the urban environment, providing great potential to analyze human socioeconomic activities. However, limited studies have investigated the impacts of three-dimensional (3-D) urban forms on socioeconomic activities ... ...

    Abstract Urban forms are closely related to the urban environment, providing great potential to analyze human socioeconomic activities. However, limited studies have investigated the impacts of three-dimensional (3-D) urban forms on socioeconomic activities across cities. In this paper, we explored the relationship between urban form and socioeconomic activities using 3-D building height data from 38 cities in China. First, we aggregated the building footprint data and calculated three building indicators at the grid scale, based on which the spatial patterns of building height and road density were analyzed. Then, we examined the capacities of two-dimensional (2D)/3D urban forms in characterizing socioeconomic activities using satellite-derived nighttime light (NTL) data. Finally, we analyzed the relationship between road density distributions and building heights across 38 cities in China. Our results suggest that the building height information can improve the correlation between urban form and NTL. Different patterns of road distribution were revealed according to the distribution of road density change from the building hotspots, showing the capacity of 3-D building height data in helping characterize socioeconomic activities. Our study indicates that the 3-D building height information is of great potential to support a variety of studies in urban domains, such as population distribution and carbon emissions, with significantly improved capacities.
    Keywords 3-D urban form ; socioeconomic activities ; linear regression ; kernel density ; road distribution pattern ; Science ; Q
    Subject code 720
    Language English
    Publishing date 2022-04-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube

    Qianqian Cao / Guoqing Li / Xiaochuang Yao / Tao Jia / Guojiang Yu / Lianchong Zhang / Dan Xu / Hao Zhang / Xiaojun Shan

    Applied Sciences, Vol 12, Iss 15, p

    2022  Volume 7816

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Open Data Cube ; GF-1 data ; water change mapping ; remote sensing data management ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 710
    Language English
    Publishing date 2022-08-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Glacier classification from Sentinel-2 imagery using spatial-spectral attention convolutional model

    Shuai Yan / Linlin Xu / Guojiang Yu / Longshan Yang / Wenju Yun / Dehai Zhu / Sijing Ye / Xiaochuang Yao

    International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102445- (2021)

    2021  

    Abstract: Glaciers are critical components of the cryosphere and most sources of water. The Sentinel-2 imagery provides an efficient and cost-effective way for the production of inventories of glaciers. Efficient conventional glacial methods can overcome the ... ...

    Abstract Glaciers are critical components of the cryosphere and most sources of water. The Sentinel-2 imagery provides an efficient and cost-effective way for the production of inventories of glaciers. Efficient conventional glacial methods can overcome the drawbacks of manual interpretation by generating glacier maps in a fast and accurate manner. However, these methods are difficult to obtain discriminative spatial-spectral features, so that it is difficult capture the subtle differences between glaciers and surrounding material on Sentinel-2 imagery. In this paper, a novel spatial-spectral attention neural network approach, which integrates a novel spatial-spectral attention module with UNet architecture, is designed for better addressing these challenges in glacier mapping. The proposed approach builds an interdependencies between spatial-spectral domains via adaptively recalibrating the weighs of spatial-spectral features for each pixel, which is benefit to highlight the difference between glaciers and surroundings. The proposed module is effectively integrated in UNet architecture, and the optimal model overcomes the noise effect on Sentinel-2 imagery, which outperforms the original UNet approach about 1 percent. The result indicates that a consistent improvement of glacier feature extraction. Furthermore, the proposed approach is compared with several existing glacier mapping algorithm, and results demonstrate that the proposed approach has stronger generalization ability and can obtain satisfactory accuracy for glacier mapping with test accuracy about 98.61%.
    Keywords Glacier classification ; Sentinel-2 ; Spectral signal ; Spatial correlation effect ; Deep learning ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
    Subject code 006
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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