scholarly journals Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

2020 ◽  
Vol 12 (24) ◽  
pp. 4033
Author(s):  
Karine R. Ferreira ◽  
Gilberto R. Queiroz ◽  
Lubia Vinhas ◽  
Rennan F. B. Marujo ◽  
Rolf E. O. Simoes ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Time Series Analysis ◽  
Multidimensional Data ◽  
Observation Data ◽  
Data Cubes ◽  
Series Analysis ◽  
Earth Observation Data ◽  
Multidimensional Data Cubes ◽  
Land Use And Cover

Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

scholarly journals Satellite Image Time Series Analysis for Big Earth Observation Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Time Series Analysis ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Data ◽  
Accuracy Measurement ◽  
Series Analysis ◽  
Earth Observation Data

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.

scholarly journals CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES

2020 ◽  
Vol V-3-2020 ◽  
pp. 533-539
Author(s):  
M. C. A. Picoli ◽  
R. Simoes ◽  
M. Chaves ◽  
L. A. Santos ◽  
A. Sanchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Data Cube ◽  
Multidimensional Data ◽  
Observation Data ◽  
Earth Observation Data ◽  
Processing And Storage ◽  
Multidimensional Data Cubes ◽  
And Storage

Abstract. Currently, the overwhelming amount of Earth Observation data demands new solutions regarding processing and storage. To reduce the amount of time spent in searching, downloading and pre-processing data, the remote Sensing community is coming to an agreement on the minimum amount of corrections satellite images must convey in order to reach the broadest range of applications. Satellite imagery meeting such criteria (which usually include atmospheric, radiometric and topographic corrections) are generically called Analysis Ready Data (ARD). Furthermore, ARD is being assembled into multidimensional data cubes, minimising preprocessing tasks and allowing scientists and users in general to focus on analysis. A particular instance of this is the Brazil Data Cube (BDC) project, which is processing remote sensing images of medium spatial resolution into ARD datasets and assembling them as multidimensional cubes of the Brazilian territory. For example, BDC users are released from performing tasks such as image co-registration , aerosol interference correction. This work presents a BDC proof of concept, by analysing a BDC data cube made with images from the fourth China-Brazil Earth Resources Satellite (CBERS-4) of one of the largest biodiversity hotspot in the world, the Cerrado biome. It also shows how to map and monitor land use and land cover using the CBERS data cube. We demonstrate that the CBERS data cube is effective in resolving land use and and land cover issues to meet local and national needs related to the landscape dynamics, including deforestation, carbon emissions, and public policies.

A Residual Variance Based Discriminant Function and its Algorithm

2015 ◽  
Vol 738-739 ◽  
pp. 354-360
Author(s):  
Jian Jun Zhang ◽  
Ye Xin Song ◽  
Yong Qu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Discriminant Function ◽  
Time Complexity ◽  
Reference Model ◽  
Computation Time ◽  
Observation Data ◽  
Residual Variance ◽  
Series Analysis

Time series analysis is advantageous since it offers insight into the underlying dynamics and forecasts system behavior. The construction of the discriminant function is of vital importance in the time series analysis based fault diagnosis. Aiming at the problem that some of the time series analysis based fault diagnosis methods exist the weakness of higher time complexity, weaker discriminant ability and insufficient online diagnosis power, this paper proposes an approach which makes full use of the characteristics of the model and observation data to construct the discriminant function, and presents an efficient algorithm which can effectively recognize the system state by the proposed discriminant function. As compared to the related work, it has the characteristics of lower time complexity, shorter computation time and stronger distinguished ability, without the requirement of same orders of the reference model and the detected model. The fault diagnosis steps based on the proposed discriminant function and its algorithm are also suggested.

scholarly journals SCIDB BASED FRAMEWORK FOR STORAGE AND ANALYSIS OF REMOTE SENSING BIG DATA

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 43-47
Author(s):  
A. Joshi ◽  
E. Pebesma ◽  
R. Henriques ◽  
M. Appel
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Big Data ◽  
Time Series Analysis ◽  
Data Storage ◽  
Satellite Imagery ◽  
Distributed Storage ◽  
Remote Sensing Data ◽  
Observation Data ◽  
Series Analysis

Abstract. Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied resolution and usages. These big data have variety of applications including climate change study, forestry application, agricultural application and urban planning. However, these big data also possess challenge of data storage, management and high computational requirement for processing. The solution to this computational and data management requirements is database system with distributed storage and parallel computation.In this study SciDB, an array-based database is used to store, manage and process multitemporal satellite imagery. The major aim of this study is to develop SciDB based scalable solution to store and perform time series analysis on multi-temporal satellite imagery. Total 148 scene of landsat image of 10 years period between 2006 and 2016 were stored as SciDB array. The data was then retrieved, processed and visualized. This study provides solution for storage of big RS data and also provides workflow for time series analysis of remote sensing data no matter how large is the size.

scholarly journals Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

2021 ◽  
Vol 73 (4) ◽  
pp. 1036-1047
Author(s):  
Felipe Menino Carlos ◽  
Vitor Conrado Faria Gomes ◽  
Gilberto Ribeiro de Queiroz ◽  
Felipe Carvalho de Souza ◽  
Karine Reis Ferreira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Open Data ◽  
Data Cube ◽  
Machine Learning Techniques ◽  
Multidimensional Data ◽  
Temporal Dimension ◽  
Data Cubes

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.

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