A Scientific Perspective on Big Data in Earth Observation

2020 ◽  
pp. 155-188
Author(s):  
Corina Vaduva ◽  
Michele Iapaolo ◽  
Mihai Datcu
Keyword(s):  
Big Data ◽  
Earth Observation

Big Data Management in Earth Observation: The German satellite data archive at the German Aerospace Center

2016 ◽  
Vol 4 (3) ◽  
pp. 51-58 ◽  
Author(s):  
Stephan Kiemle ◽  
Katrin Molch ◽  
Stephan Schropp ◽  
Nicolas Weiland ◽  
Eberhard Mikusch
Keyword(s):  
Big Data ◽  
Data Management ◽  
Satellite Data ◽  
Earth Observation ◽  
Data Archive ◽  
German Aerospace

scholarly journals AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software Toolbox for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery Eligible for Systematic ESA EO Level 2 Product Generation

2018 ◽  
Author(s):  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Big Data ◽  
Spatial Information ◽  
European Space Agency ◽  
Earth Observation ◽  
Topographic Effects ◽  
Human Machine Interaction ◽  
Product Generation ◽  
Ill Posed ◽  
Level 2 ◽  
Software Toolbox

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

scholarly journals AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software Toolbox for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery Eligible for Systematic ESA EO Level 2 Product Generation

2018 ◽  
Author(s):  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Big Data ◽  
Spatial Information ◽  
European Space Agency ◽  
Earth Observation ◽  
Topographic Effects ◽  
Human Machine Interaction ◽  
Product Generation ◽  
Ill Posed ◽  
Level 2 ◽  
Software Toolbox

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

scholarly journals Copernicus Data and CAP Subsidies Control

2021 ◽  
pp. 265-290
Author(s):  
Olimpia Copăcenaru ◽  
Adrian Stoica ◽  
Antonella Catucci ◽  
Laura De Vendictis ◽  
Alessia Tricomi ◽  
...  
Keyword(s):  
Big Data ◽  
Satellite Tracking ◽  
Earth Observation ◽  
Classical Field ◽  
Data Sources ◽  
Use Case ◽  
Control Methods ◽  
Horizon 2020 ◽  
Food Market ◽  
Verification Process

AbstractThis chapter integrates the results of three pilots developed within the framework of the Horizon 2020 DataBio project. It aims to provide a broad picture of how products based on Earth Observation techniques can support the European Union’s Common Agricultural Policy requirements, whose fulfillments are supervised by National and Local Paying Agencies operating in Romania, Italy and Greece. The concept involves the use of the same data sources, mainly multitemporal series of Copernicus Sentinel-2 imagery, but through three different Big Data processing chains, tailored to each paying agency’s needs in terms of farm compliance assessment. Particularities of each workflow are presented together with examples of the results and their accuracy, calculated by validation against independent sources. Business value aspects for each use case are also discussed, emphasizing the way in which the automation of the CAP requests verification process through satellite technologies has increased the efficiency and reduced cost and time resources for the subsidy process. We end the chapter by highlighting the benefits of continuous satellite tracking as a substitute, but also complementary to the classical field control methods, and also the enormous potential of Earth Observation-based products for the agri-food market.

scholarly journals Remote Sensing

2021 ◽  
pp. 49-61
Author(s):  
Miguel Ángel Esbrí
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Information Access ◽  
Open Data ◽  
Earth Observation ◽  
Observation Data ◽  
Related Data ◽  
Data Formats ◽  
Earth Observation Data ◽  
Access Services

AbstractIn this chapter we present the concepts of remote sensing and Earth Observation and, explain why several of their characteristics (volume, variety and velocity) make us consider Earth Observation as Big Data. Thereafter, we discuss the most commonly open data formats used to store and share the data. The main sources of Earth Observation data are also described, with particular focus on the constellation of Sentinel satellites, Copernicus Hub and its six thematic services, as well as other private initiatives like the five Copernicus-related Data and Information Access Services and  Sentinel Hub. Next, we present an overview of representative software technologies for efficiently describing, storing, querying and accessing Earth Observation datasets. The chapter concludes with a summary of the Earth Observation datasets used in each DataBio pilot.

scholarly journals COPERNICUS EARTH OBSERVATION FOR CULTURAL HERITAGE

2021 ◽  
Author(s):  
Athos Agapiou ◽  
Vasiliki Lysandrou ◽  
Branka Cuca
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Cultural Heritage ◽  
Open Data ◽  
Earth Observation ◽  
Theoretical Research ◽  
New Developments ◽  
Data Policy ◽  
European Projects ◽  
Research Domain

This article summarises the contribution of the European Union's Earth Observation Copernicus Programme, and other satellite-based platforms for cultural heritage applications and highlights the results of some national and European projects conducted in this domain. Even though the Copernicus Programme has recently been introduced for cultural heritage use (i.e., after the second half of this decade), the full, free, and open data policy, known as FFO, has increased the number of relevant applications. Since then, several studies dedicated to cultural heritage monitoring, management, etc., have been presented in the literature. Despite the medium spatial resolution of the radar and optical Sentinel sensors, the high-temporal revisit time of these sensors in combination with other legacy space programmes (e.g., the Landsat space programme) can provide useful information to site-managers, local and regional stakeholders, and other national agencies. Also, new developments in the domain of image processing like the use of big-data earth observation cloud platforms already showcased the potentials for dedicated space-based cultural heritage services. However, this technological shift remains still within the scientific and theoretical research domain. Therefore, much effort is still needed towards the implementation of national policies.

scholarly journals AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

2018 ◽  
Vol 7 (12) ◽  
pp. 457 ◽  
Author(s):  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Big Data ◽  
Operating Mode ◽  
Earth Observation ◽  
Broad Context ◽  
Software System ◽  
Information Product ◽  
Data Cubes ◽  
Ground Segment ◽  
Product Generation ◽  
Level 2

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are that ESA EO Level 2 information product is regarded as: (I) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (II) State-of-the-art definition of EO Analysis Ready Data (ARD) format. (III) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2, the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.

MORERA: latest Earth observation system to translate big data to agriculture

2021 ◽  
Author(s):  
Angel Alvaro Sanchez ◽  
José Antonio Sobrino ◽  
Concepcion Mira Rodado ◽  
Victoria Gonzalez-Dugo ◽  
Tomás Belenguer Dávila ◽  
...  
Keyword(s):  
Big Data ◽  
Earth Observation ◽  
Observation System ◽  
Earth Observation System
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