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

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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Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

Remote Sensing ◽

10.3390/rs13050974 ◽

2021 ◽

Vol 13 (5) ◽

pp. 974

Author(s):

Lorena Alves Santos ◽

Karine Ferreira ◽

Michelle Picoli ◽

Gilberto Camara ◽

Raul Zurita-Milla ◽

...

Keyword(s):

Machine Learning ◽

Land Use ◽

Time Series ◽

Satellite Image ◽

Spatiotemporal Patterns ◽

Supervised Machine Learning ◽

Learning Methods ◽

Self Organizing Maps ◽

Machine Learning Methods ◽

Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

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

Remote Sensing ◽

10.3390/rs12244033 ◽

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.

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Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review

Remote Sensing ◽

10.3390/rs13234822 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4822

Author(s):

Waytehad Rose Moskolaï ◽

Wahabou Abdou ◽

Albert Dipanda ◽

Kolyang

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Satellite Images ◽

Spatial Information ◽

Time Series Prediction ◽

Satellite Image ◽

Machine Learning Algorithms ◽

Temporal Dimension ◽

Prediction Problems

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.

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Towards a typology of land-cover/land-use evolutions using high resolution satellite image time series: application to the metropolitan area of Strasbourg (France)

GEOBIA 2016 : Solutions and Synergies ◽

10.3990/2.391 ◽

2016 ◽

Author(s):

F.N. Guttler ◽

A. Puissant ◽

P. Gancarski

Keyword(s):

Land Use ◽

Time Series ◽

High Resolution ◽

Land Cover ◽

Metropolitan Area ◽

Satellite Image ◽

High Resolution Satellite Image

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Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

10.21528/cbic2021-9 ◽

2021 ◽

Author(s):

Hugo Abreu Mendes ◽

João Fausto Lorenzato Oliveira ◽

Paulo Salgado Gomes Mattos Neto ◽

Alex Coutinho Pereira ◽

Eduardo Boudoux Jatoba ◽

...

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Time Series ◽

Solar Radiation ◽

Statistical Models ◽

Clean Energy ◽

Machine Learning Techniques ◽

Exogenous Variables ◽

Learning Techniques ◽

Photovoltaic Plants

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.

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Electricity Load Forecasting Using Machine Learning Techniques

Business Intelligence in Economic Forecasting ◽

10.4018/978-1-61520-629-2.ch017 ◽

2010 ◽

pp. 318-336

Author(s):

Manuel Martín-Merino Acera

Keyword(s):

Machine Learning ◽

Time Series ◽

Load Forecasting ◽

Machine Learning Techniques ◽

Support Vector ◽

Strong Impact ◽

Local Character ◽

Nonlinear Character ◽

Electricity Load ◽

Electricity Load Forecasting

Electricity load forecasting has become increasingly important due to the strong impact on the operational efficiency of the power system. However, the accurate load prediction remains a challenging task due to several issues such as the nonlinear character of the time series or the seasonal patterns it exhibits. A large variety of techniques have been proposed to this aim, such as statistical models, fuzzy systems or artificial neural networks. The Support Vector Machines (SVM) have been widely applied to the electricity load forecasting with remarkable results. In this chapter, the authors study the performance of the classical SVM in the problem of electricity load forecasting. Next, an algorithm is developed that takes advantage of the local character of the time series. The method proposed first splits the time series into homogeneous regions using the Self Organizing Maps (SOM) and next trains a Support Vector Machine (SVM) locally in each region. The methods presented have been applied to the prediction of the maximum daily electricity demand. The properties of the time series are analyzed in depth. All the models are compared rigorously through several objective functions. The experimental results show that the local model proposed outperforms several statistical and machine learning forecasting techniques.

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