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Big Earth Observation Data: From Cloud Technologies to Insights and Foresight

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 35658

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Dr. Pieter Kempeneers

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European Commission Joint Research Centre, Via E. Fermi 2749, I-21027 Ispra, Italy
Interests: big data analytics; remote sensing; image processing; open source software

Special Issue Information

Dear Colleagues,

Among the many areas where big data have made their entrance, Earth Observation has been one of the most impacted. The pace at which the number of sensors, either ground-, air- or space-borne, is increasing is unprecedented. In addition, images are now acquired at a finer spatial resolution than ever before, with more frequent revisit times. Meanwhile, many archives are adopting a policy of free, full, and open data access. For instance, the data streams generated by the Sentinel satellites from the European Union Copernicus Programme are delivering more than 20 TB per day. All these data can be freely downloaded for scientific, commercial, or any other use. However, although the data are free, full, and open, computing resources and data communication are not. Bandwidth has increased as technology has improved, but not at the same pace as the data volume is growing. It is therefore a limiting factor that has paved the way for a paradigm shift from bringing data to the user to bringing the user to the data. Downloads are then reserved for transferring information-rich but low-volume data. The high volumes of data can be processed in parallel with high throughput computing (HTC) clusters with thousands of cores that have fast access to the data. Complex processing algorithms benefit from high performance computing (HPC). Machine learning for deep neural networks runs much faster on dedicated hardware, such as graphical processing units (GPU). Few organizations have the means to buy, set up, and maintain these highly specialized environments. They therefore move to cloud computing, where resources can be shared and scaled depending on the user needs.

In addition to the technical aspects with respect to cloud-based infrastructures, contributions regarding the analysis of big Earth Observation data on these infrastructures are welcome.

This Special Issue is linked to the Big Data from Space conference (https://www.bigdatafromspace2021.org/). The emphasis of the 2021 edition of this conference is “From Insights to Foresight”. In particular, this special issue aims for contributions dealing with applications whereby insights extracted from big Earth observation data are used as a basis for foresight of interest to societal challenges, such as the Sustainable Development Goals and climate change. Models integrating Earth Observation time series linked with other relevant data sources in support to policy and decision making are indeed most relevant within the context of our rapidly changing world.

Dr. Pieter Kempeneers
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Distributed cloud computing and data storage
(Cloud optimized image) data formats
Maintaining data integrity, security, and privacy
Data cube concepts and technology, analysis ready data
Discrete global grid systems (DGGS)
Open standards and application programming interfaces (API)
Processing Earth Observation data (HTC, HPC, schedulers)
Federated cloud platforms
Parallelizable algorithms
Data visualization
Integration of (EO and non-EO) data sources
Data assimilation
Knowledge extraction and data valorization
Modeling

Published Papers (5 papers)

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Research

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21 pages, 3109 KiB

Open AccessArticle

The Austrian Semantic EO Data Cube Infrastructure

by Martin Sudmanns, Hannah Augustin, Lucas van der Meer, Andrea Baraldi and Dirk Tiede

Remote Sens. 2021, 13(23), 4807; https://doi.org/10.3390/rs13234807 - 26 Nov 2021

Cited by 10 | Viewed by 3073

Abstract

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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20 pages, 5013 KiB

Open AccessArticle

Satellite Image Time Series Analysis for Big Earth Observation Data

by Rolf Simoes, Gilberto Camara, Gilberto Queiroz, Felipe Souza, Pedro R. Andrade, Lorena Santos, Alexandre Carvalho and Karine Ferreira

Remote Sens. 2021, 13(13), 2428; https://doi.org/10.3390/rs13132428 - 22 Jun 2021

Cited by 38 | Viewed by 15571

Abstract

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. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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21 pages, 17937 KiB

Open AccessEditor’s ChoiceArticle

The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities

by Matthias Schramm, Edzer Pebesma, Milutin Milenković, Luca Foresta, Jeroen Dries, Alexander Jacob, Wolfgang Wagner, Matthias Mohr, Markus Neteler, Miha Kadunc, Tomasz Miksa, Pieter Kempeneers, Jan Verbesselt, Bernhard Gößwein, Claudio Navacchi, Stefaan Lippens and Johannes Reiche

Remote Sens. 2021, 13(6), 1125; https://doi.org/10.3390/rs13061125 - 16 Mar 2021

Cited by 36 | Viewed by 8941

Abstract

At present, accessing and processing Earth Observation (EO) data on different cloud platforms requires users to exercise distinct communication strategies as each backend platform is designed differently. The openEO API (Application Programming Interface) standardises EO-related contracts between local clients (R, Python, and JavaScript) and cloud service providers regarding data access and processing, simplifying their direct comparability. Independent of the providers’ data storage system, the API mimics the functionalities of a virtual EO raster data cube. This article introduces the communication strategy and aspects of the data cube model applied by the openEO API. Two test cases show the potential and current limitations of processing similar workflows on different cloud platforms and a comparison of the result of a locally running workflow and its openEO-dependent cloud equivalent. The outcomes demonstrate the flexibility of the openEO API in enabling complex scientific analysis of EO data collections on cloud platforms in a homogenised way. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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14 pages, 2613 KiB

Open AccessTechnical Note

Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

by Pieter Kempeneers, Tomas Kliment, Luca Marletta and Pierre Soille

Remote Sens. 2022, 14(2), 398; https://doi.org/10.3390/rs14020398 - 15 Jan 2022

Cited by 1 | Viewed by 2136

Abstract

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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18 pages, 38399 KiB

Open AccessTechnical Note

A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications

by Wolfgang Wagner, Bernhard Bauer-Marschallinger, Claudio Navacchi, Felix Reuß, Senmao Cao, Christoph Reimer, Matthias Schramm and Christian Briese

Remote Sens. 2021, 13(22), 4622; https://doi.org/10.3390/rs13224622 - 17 Nov 2021

Cited by 17 | Viewed by 4521

Abstract

The Sentinel-1 Synthetic Aperture Radar (SAR) satellites allow global monitoring of the Earth’s land surface with unprecedented spatio-temporal coverage. Yet, implementing large-scale monitoring capabilities is a challenging task given the large volume of data from Sentinel-1 and the complex algorithms needed to convert the SAR intensity data into higher-level geophysical data products. While on-demand processing solutions have been proposed to cope with the petabyte-scale data volumes, in practice many applications require preprocessed datacubes that permit fast access to multi-year time series and image stacks. To serve near-real-time as well as offline land monitoring applications, we have created a Sentinel-1 backscatter datacube for all continents (except Antarctica) that is constantly being updated and maintained to ensure consistency and completeness of the data record over time. In this technical note, we present the technical specifications of the datacube, means of access and analysis capabilities, and its use in scientific and operational applications. Full article

(This article belongs to the Special Issue Big Earth Observation Data: From Cloud Technologies to Insights and Foresight)

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