Abstract
The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.
Highlights
In recent years, the amount of Earth Observation (EO) data available has grown, driven by technological advances in acquisition and storage equipment and space agency policies that make their data repositories freely available. Soille et al (2018) estimate that the annual data volume produced by well-known EO platforms, including Landsat 7 and 8, Terra and Aqua, and the Sentinel 1, 2, and 3, can reach 04 petabytes
The second, odc-sits, is a tool written in the R programming language that adds some functionalities to sits that enable it to access Earth Observation Data Cubes (EODC) managed by Open Data Cube (ODC)
The Brazil Data Cube (BDC) project was created with the following objectives (FERREIRA et al, 2020): (i) Produce Analysis-Ready Data (ARD) and EODC from medium-resolution satellite images for Brazil; (ii) Creation of new methods and techniques for storing, processing, and analyzing large volumes of EO data; and (iii) Generation of land use and land cover (LULC) maps using satellite image time-series extracted from EODC data and Machine Learning (ML) techniques
Summary
The amount of Earth Observation (EO) data available has grown, driven by technological advances in acquisition and storage equipment and space agency policies that make their data repositories freely available. Soille et al (2018) estimate that the annual data volume produced by well-known EO platforms, including Landsat 7 and 8, Terra and Aqua, and the Sentinel 1, 2, and 3, can reach 04 petabytes. The EO scientific community has developed and made available many specialized tools to address the challenge of managing, processing, and analyzing these large volumes of data These tools are often provided as software infrastructures that manage large time-series of EO data using multidimensional array concepts, making it easy for users to access and use Analysis-Ready Data (ARD). ODC provides several tools to manage EODCs, it does not have ready-to-use functionalities for LULC mapping. The second, odc-sits, is a tool written in the R programming language that adds some functionalities to sits that enable it to access EODCs managed by ODC. Final considerations regarding the challenges encountered and the steps that will be developed
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