scholarly journals The Austrian Semantic EO Data Cube Infrastructure

2021 ◽  
Vol 13 (23) ◽  
pp. 4807
Author(s):  
Martin Sudmanns ◽  
Hannah Augustin ◽  
Lucas van der Meer ◽  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Land Cover ◽  
Knowledge Engineering ◽  
A Priori ◽  
Data Cube ◽  
Color Information ◽  
Data Cubes ◽  
Semantic Enrichment ◽  
Knowledge Based ◽  
Geographical Regions ◽  
Semantic Models

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.

The Sen2Cube.at national semantic Earth observation data cube for Austria 

2021 ◽  
Author(s):  
Martin Sudmanns ◽  
Hannah Augustin ◽  
Lucas van der Meer ◽  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Inference Engine ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Semantic Enrichment ◽  
Area Of Interest ◽  
Information Layer ◽  
Semantic Models ◽  
Earth Observation Data ◽  
Sentinel 2

<div> <p>The Sen2Cube.at is a Sentinel-2 semantic Earth observation (EO) data and information cube that combines an EO data cube with an AI-based inference engine by integrating a computer-vision approach to infer new information. Our approach uses semantic enrichment of optical images and makes the data and information directly available and accessible for further use within an EO data cube. The architecture is based on an expert system, in which domain-knowledge can be encoded in semantic models (knowledgebase) and applied to the Sentinel-2 data as well as semantically enriched, data-derived information (factbase).  </p> </div><div> <p>The initial semantic enrichment in the Sen2Cube.at system is general-purpose, user- and application-independent, derived directly from optical EO images as an initial step towards a scene classification map. These information layers are automatically generated from Sentinel-2 images with the SIAM software (Satellite Image Automated Mapper). SIAM is a knowledge-based and physical-model-based decision tree that produces a set of information layers in a fully automated process that is applicable worldwide and does not require any samples. A graphical inference engine allows application-specific Web-based semantic querying based on the generic information layer as a replicable and explainable approach to produce information. The graphical inference engine is a new Browser-based graphical user interface (GUI) developed in-house with a semantic querying language. Users formulate semantic models in a graphical way and can execute them on any area-of-interest and time interval, which will be evaluated by the core of the inference engine attached to the data cube. This also enables non-expert users to formulate analyses without requiring programming skills.  </p> </div><div> <p>While the methodology is software-independent, the prototype is based on the Open Data Cube and additional in-house developed components in the Python programming language. Scaling is possible depending on the available infrastructure resources due to the system’s Docker-based container architecture. Through its fully automated semantic enrichment, innovative graphical querying language in the GUI for semantic querying and analysis as well as the implementation as a scalable infrastructure, this approach is suited for big data analysis of Earth observation data. It was successfully scaled to a national data cube for Austria, containing all available Sentinel-2 images from the platforms A and B. </p> </div>

Application of Expert Systems Technology in Water Quality Modeling

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1045-1056 ◽  
Author(s):  
Thomas O. Barnwell ◽  
Linfield C. Brown ◽  
Wiktor Marek
Keyword(s):  
Water Quality ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Stream Water ◽  
Environmental Modeling ◽  
Water Quality Modeling ◽  
Stream Water Quality ◽  
Quality Model ◽  
Knowledge Based ◽  
Available Technology

Computerized modeling is becoming an integral part of decision making in water pollution control. Expert systems is an innovative methodology that can assist in building, using, and interpreting the output of these models. This paper reviews the use and evaluates the potential of expert systems technology in environmental modeling and describes elements of an expert advisor for the stream water quality model QUAL2E. Some general conclusions are presented about the tools available to develop this system, the level of available technology in knowledge-based engineering, and the value of approaching problems from a knowledge engineering perspective.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0

2021 ◽  
Vol 258 ◽  
pp. 112364
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Xi Wang ◽  
Grant Ning ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Data Cubes ◽  
Global Land Cover ◽  
Global Land

A priori knowledge based particle filter for estimating 3-D pose position of implanted knee

2010 ◽  
Author(s):  
Yusuke Nakajima ◽  
Syoji Kobashi ◽  
Yohei Tsumori ◽  
Nao Shibanuma ◽  
Fumiaki Imamura ◽  
...  
Keyword(s):  
Particle Filter ◽  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge

Necessity, Certainty, and the A Priori

1988 ◽  
Vol 18 (1) ◽  
pp. 43-66 ◽  
Author(s):  
Albert Casullo
Keyword(s):  
Cognitive Processes ◽  
Mathematical Knowledge ◽  
A Priori ◽  
Theory Of Knowledge ◽  
A Posteriori ◽  
Unitary Theory ◽  
Knowledge Based

Empiricist theories of knowledge are attractive for they offer the prospect of a unitary theory of knowledge based on relatively well understood physiological and cognitive processes. Mathematical knowledge, however, has been a traditional stumbling block for such theories. There are three primary features of mathematical knowledge which have led epistemologists to the conclusion that it cannot be accommodated within an empiricist framework: 1) mathematical propositions appear to be immune from empirical disconfirmation; 2) mathematical propositions appear to be known with certainty; and 3) mathematical propositions are necessary. Epistemologists who believe that some nonmathematical propositions, such as logical or ethical propositions, cannot be known a posteriori also typically appeal to the three factors cited above in defending their position. The primary purpose of this paper is to examine whether any of these alleged features of mathematical propositions establishes that knowledge of such propositions cannot be a posteriori.

scholarly journals A META-TOOL TO SUPPORT THE DEVELOPMENT OF KNOWLEDGE ENGINEERING METHODOLOGIES AND PROJECTS

2012 ◽  
Vol 22 (08) ◽  
pp. 1055-1083
Author(s):  
JOSÉ ELOY FLÓREZ ◽  
JAVIER CARBÓ ◽  
FERNANDO FERNÁNDEZ
Keyword(s):  
Knowledge Acquisition ◽  
Ad Hoc ◽  
Knowledge Engineering ◽  
Domain Specific ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Combined Application ◽  
Processing Information ◽  
Knowledge Acquisition Methods ◽  
Applied Knowledge

Knowledge-based systems (KBSs) or expert systems (ESs) are able to solve problems generally through the application of knowledge representing a domain and a set of inference rules. In knowledge engineering (KE), the use of KBSs in the real world, three principal disadvantages have been encountered. First, the knowledge acquisition process has a very high cost in terms of money and time. Second, processing information provided by experts is often difficult and tedious. Third, the establishment of mark times associated with each project phase is difficult due to the complexity described in the previous two points. In response to these obstacles, many methodologies have been developed, most of which include a tool to support the application of the given methodology. Nevertheless, there are advantages and disadvantages inherent in KE methodologies, as well. For instance, particular phases or components of certain methodologies seem to be better equipped than others to respond to a given problem. However, since KE tools currently available support just one methodology the joint use of these phases or components from different methodologies for the solution of a particular problem is hindered. This paper presents KEManager, a generic meta-tool that facilitates the definition and combined application of phases or components from different methodologies. Although other methodologies could be defined and combined in the KEManager, this paper focuses on the combination of two well-known KE methodologies, CommonKADS and IDEAL, together with the most commonly-applied knowledge acquisition methods. The result is an example of the ad hoc creation of a new methodology from pre-existing methodologies, allowing for the adaptation of the KE process to an organization or domain-specific characteristics. The tool was evaluated by students at Carlos III University of Madrid (Spain).

A MULTILEVEL FUSION APPROACH TO OBJECT IDENTIFICATION IN OUTDOOR ROAD SCENES

1995 ◽  
Vol 09 (01) ◽  
pp. 23-65 ◽  
Author(s):  
VITTORIO MURINO ◽  
CARLO S. REGAZZONI ◽  
GIAN LUCA FORESTI ◽  
GIANNI VERNAZZA
Keyword(s):  
Data Integration ◽  
Environmental Conditions ◽  
Autonomous Vehicle ◽  
A Priori ◽  
Object Identification ◽  
Distributed Knowledge ◽  
Knowledge Based ◽  
Multilevel Fusion ◽  
Fusion Framework ◽  
Fusion Approach

The task of object identification is fundamental to the operations of an autonomous vehicle. It can be accomplished by using techniques based on a Multisensor Fusion framework, which allows the integration of data coming from different sensors. In this paper, an approach to the synergic interpretation of data provided by thermal and visual sensors is proposed. Such integration is justified by the necessity for solving the ambiguities that may arise from separate data interpretations. The architecture of a distributed Knowledge-Based system is described. It performs an Intelligent Data Fusion process by integrating, in an opportunistic way, data acquired with a thermal and a video (b/w) camera. Data integration is performed at various architecture levels in order to increase the robustness of the whole recognition process. A priori models allow the system to obtain interesting data from both sensors; to transform such data into intermediate symbolic objects; and, finally, to recognize environmental situations on which to perform further processing. Some results are reported for different environmental conditions (i.e. a road scene by day and by night, with and without the presence of obstacles).

A Priori knowledge based secure payload estimation

2017 ◽  
Vol 77 (14) ◽  
pp. 17889-17911 ◽  
Author(s):  
Sai Ma ◽  
Xianfeng Zhao ◽  
Qingxiao Guan ◽  
Zhoujun Xu ◽  
Yi Ma
Keyword(s):  
A Priori ◽  
A Priori Knowledge ◽  
Knowledge Based ◽  
Priori Knowledge
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