scholarly journals Automated near real-time earth observation level 2 product generation for semantic querying

10.3990/2.417 ◽  
2016 ◽  
Author(s):  
A. de Baraldi ◽  
Dirk Tiede ◽  
Martin Sudmanss ◽  
Mariana Belgiu ◽  
Stefan Lang
Keyword(s):  
Real Time ◽  
Earth Observation ◽  
Observation Level ◽  
Product Generation ◽  
Level 2

scholarly journals GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory

2018 ◽  
Vol 4 (1) ◽  
pp. 1467357
Author(s):  
Andrea Baraldi ◽  
Michael Laurence Humber ◽  
Dirk Tiede ◽  
Stefan Lang ◽  
Louis-Noel Moresi
Keyword(s):  
Computer Program ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Level ◽  
Stage 4 ◽  
Product Generation ◽  
Level 2

scholarly journals GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

2018 ◽  
Vol 4 (1) ◽  
pp. 1467254 ◽  
Author(s):  
Andrea Baraldi ◽  
Michael Laurence Humber ◽  
Dirk Tiede ◽  
Stefan Lang ◽  
Louis-Noel Moresi
Keyword(s):  
Computer Program ◽  
Satellite Image ◽  
Earth Observation ◽  
Observation Level ◽  
Stage 4 ◽  
Product Generation ◽  
Level 2

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 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.

Improvements to the Simplified Level 2 Prototype Processor for Retrieving Canopy Biophysical Variables from Sentinel 2 Multispectral Instrument Data

2021 ◽  
Author(s):  
Richard Fernandes ◽  
Fred Baret ◽  
Luke Brown ◽  
Francis Canisius ◽  
Jadu Dash ◽  
...  
Keyword(s):  
North America ◽  
Google Earth ◽  
Model Complexity ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Wide Field ◽  
Area Index ◽  
Product Generation ◽  
Level 2 ◽  
Sentinel 2

<p>The Sentinel 2 (S2) constellation mission was designed to facilitate the systematic mapping canopy biophysical variables at medium resolution on a global basis and in a free and open manner.  The mission concept requires the development of downstream services to map variables such as the fraction of absorbed photosynthetically active radiation (fAPAR), fraction of canopy cover (fCOVER) and leaf area index (LAI) using Level 2A surface reflectance inputs from the S2 ground segment.  Currently, free and open products generation can be performed using the Simplified Level 2 Prototype Processor (SL2P) applied on a product granule basis.  Considering that the processor is a prototype this study addresses three questions: 1) Can the SL2P algorithm, or subsequent versions, be engineered to facilitate systematic product generation over large extents in a free and open manner? 2) What is the uncertainty of SL2P products over North America during the growing season? 3) Can the uncertainty be reduced by changing the calibration database used within SL2P?  </p><p><br><br></p><p>To facilitate validation and product generation, SL2P was ported to a Google Earth Engine application (the Landscape Evolution and Forecasting Toolbox).  This now allows mapping of up to one million square kilometers in near real time using either the original SL2P algorithm or updated versions.  SL2P uncertainty was quantified over North America using direct comparison to 20 in-situ sites within the National Environmental Observing Network in the continental United States of America and within a Canada wide field campaign over forests and shrublands conducted by Canada Centre for Remote Sensing. SL2P outputs were also compared to MODIS and Copernicus Global Land Service products over the Belmanip II regional sites and 30 additional forested regions in North America.  Results from NEON validation indicate SL2P is generally within uncertainty requirements except for forests; where it underestimates fAPAR, fCOVER and LAI.  Results for other sites will also be presented.  To address the forest bias, SL2P was recalibrated using simulations from the FLIGHT 3D radiative transfer model representative of North American forests.  The uncertainty of the recalibrated SL2P algorithm will be compared to baseline SL2P estimates to determine if increased model complexity is warranted.</p>

Towards Safety Analysis of ERTMS/ETCS Level 2 in Real-Time Maude

2016 ◽  
pp. 103-120 ◽  
Author(s):  
Phillip James ◽  
Andrew Lawrence ◽  
Markus Roggenbach ◽  
Monika Seisenberger
Keyword(s):  
Real Time ◽  
Safety Analysis ◽  
Level 2

Real-time implementation of digital stabilization for high-resolution Earth observation imaging

2017 ◽  
Author(s):  
Carole Thiebaut ◽  
Sophie Petit ◽  
Jean-Marc Delvit ◽  
Christophe Latry ◽  
Emma Bousquet ◽  
...  
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Earth Observation
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