TY - CHAP

T1 - Integration of Cloud and Desktop Platforms to Support Analysis of Big Geospatial Data Time Series

AU - Паниди, Евгений Александрович

AU - Рыкин, Иван

PY - 2024

Y1 - 2024

N2 - Despite the computing power growth of the cloud-based platforms providing the opportunity to process the big amounts of remote sensing data, there is still a need to process and store some kinds of data locally. During the last several years, scientists have been interested in makings to make the popular open-source GIS application QGIS and the most popular cloud-based platform Google Earth Engine (GEE) friends. However, today it is not comfortable to use different processing platforms because of their data format differences or some long converting data manipulations. Some platforms suggest their cloud-based storage services use, but some restrictions include limited storage space, upload/download speed limitations, or data format differences. This research uses the GEE as a well-designed platform with often updated remote sensing data storage, processing tools, and algorithms for interdisciplinary research using the cloud and desktop data processing opportunities. The main idea is to create a well-designed infrastructure that can be more flexible than separately used cloud and desktop platforms. An integrated cloud-desktop processing infrastructure is based on GEE cloud performance via the Python API, QGIS as a powerful desktop GIS software, and PostgreSQL with PostGIS extension to store and analyze the results of cloud-desktop processing of remote sensing data locally by database management system (DBMS). Integration of the cloud-desktop processing has allowed for increasing the data processing speed, ensuring the operational usage of the representative satellite observation data, and processing long observation series without using local capacities without needs.

AB - Despite the computing power growth of the cloud-based platforms providing the opportunity to process the big amounts of remote sensing data, there is still a need to process and store some kinds of data locally. During the last several years, scientists have been interested in makings to make the popular open-source GIS application QGIS and the most popular cloud-based platform Google Earth Engine (GEE) friends. However, today it is not comfortable to use different processing platforms because of their data format differences or some long converting data manipulations. Some platforms suggest their cloud-based storage services use, but some restrictions include limited storage space, upload/download speed limitations, or data format differences. This research uses the GEE as a well-designed platform with often updated remote sensing data storage, processing tools, and algorithms for interdisciplinary research using the cloud and desktop data processing opportunities. The main idea is to create a well-designed infrastructure that can be more flexible than separately used cloud and desktop platforms. An integrated cloud-desktop processing infrastructure is based on GEE cloud performance via the Python API, QGIS as a powerful desktop GIS software, and PostgreSQL with PostGIS extension to store and analyze the results of cloud-desktop processing of remote sensing data locally by database management system (DBMS). Integration of the cloud-desktop processing has allowed for increasing the data processing speed, ensuring the operational usage of the representative satellite observation data, and processing long observation series without using local capacities without needs.

KW - GIS

KW - Google Earth Engine

KW - Remote sensing

UR - https://www.mendeley.com/catalogue/a4761edf-3089-3a57-a0ef-30bf929e6257/

U2 - 10.1007/978-3-031-43218-7_65

DO - 10.1007/978-3-031-43218-7_65

M3 - Chapter

SN - 9783031432170

T3 - Advances in Science, Technology and Innovation

SP - 279

EP - 281

BT - Recent Research on Geotechnical Engineering, Remote Sensing, Geophysics and Earthquake Seismology (MedGU 2021)

Y2 - 25 November 2021 through 28 November 2021

ER -