Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 2: Software developments

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overco...

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Published in:Big earth data Vol. ahead-of-print; no. ahead-of-print; pp. 1 - 118
Main Authors: Baraldi, Andrea, Sapia, Luca D., Tiede, Dirk, Sudmanns, Martin, Augustin, Hannah, Lang, Stefan
Format: Journal Article
Language:English
Published: Taylor & Francis 03.07.2023
Taylor & Francis Group
Subjects:
2D spatial topology-preserving/retinotopic image mapping
Analysis Ready Data
Artificial General Intelligence
Artificial Narrow Intelligence
big data
cognitive science
computer vision
Earth observation
essential climate variables
Global Earth Observation System of (component) Systems
inductive/ deductive/ hybrid inference
radiometric corrections of optical imagery from atmospheric, topographic, adjacency and bidirectional reflectance distribution function effects
Scene Classification Map
semantic content-based image retrieval
Space Economy 4.0
world ontology (synonym for conceptual/ mental/ perceptual model of the world)
ISSN:2096-4471, 2574-5417
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Abstract Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System's Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.
AbstractList ABSTRACTAiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.
Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous Part 1, existing EO optical sensory image-derived Level 2/Analysis Ready Data (ARD) products and processes are critically compared, to overcome their lack of harmonization/ standardization/ interoperability and suitability in a new notion of Space Economy 4.0. In the present Part 2, original contributions comprise, at the Marr five levels of system understanding: (1) an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification. First, in the pursuit of third-level semantic/ontological interoperability, a novel ARD symbolic (categorical and semantic) co-product, known as Scene Classification Map (SCM), adopts an augmented Cloud versus Not-Cloud taxonomy, whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System's Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization. Second, a novel ARD subsymbolic numerical co-product, specifically, a panchromatic or multi-spectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure, ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values, in a five-stage radiometric correction sequence. (2) An original ARD process requirements specification. (3) An innovative ARD processing system design (architecture), where stepwi se SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence. (4) An original modular hierarchical hybrid (combined deductive and inductive) computer vision subsystem design, provided with feedback loops, where software solutions at the Marr two shallowest levels of system understanding, specifically, algorithm and implementation, are selected from the scientific literature, to benefit from their technology readiness level as proof of feasibility, required in addition to proven suitability. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.
Author Sudmanns, Martin
Augustin, Hannah
Lang, Stefan
Sapia, Luca D.
Baraldi, Andrea
Tiede, Dirk
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Snippet Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the previous...
ABSTRACTAiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this paper consists of two parts. In the...
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SubjectTerms 2D spatial topology-preserving/retinotopic image mapping
Analysis Ready Data
Artificial General Intelligence
Artificial Narrow Intelligence
big data
cognitive science
computer vision
Earth observation
essential climate variables
Global Earth Observation System of (component) Systems
inductive/ deductive/ hybrid inference
radiometric corrections of optical imagery from atmospheric, topographic, adjacency and bidirectional reflectance distribution function effects
Scene Classification Map
semantic content-based image retrieval
Space Economy 4.0
world ontology (synonym for conceptual/ mental/ perceptual model of the world)
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Title Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 2: Software developments
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