Coupling of Earth science models and earth observations through OGC interoperability specifications

Di, Liping; Sun, Ziheng; Yu, Genong; Song, Jia; Tong, Daniel; Huang, Haosheng; Wu, Xiaoqing; Domenico, B.

doi:10.1109/igarss.2016.7729933

Cited by 4 publications

(10 citation statements)

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“…A crucial step in geographic modeling is preparing input data for geographic models. Input data, including preliminary data or raw input data (e.g., digital elevation model (DEM)) and derived information (e.g., topographic properties such as slope and area), are not only a prerequisite for model setup, calibration, and validation, but the quantity and quality also directly affect simulation results [1,2,[5][6][7][8]. Insufficient and inappropriate input data (e.g., lack of observations and inappropriate DEM resolution) might limit both the accuracy of the model results and the applications of geographic models [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Input data preparation in geographic modeling is particularly challenging due to the input data needed by geographic models often being obtained from distributed data sources and being syntactically and semantically heterogeneous [1,5,6,13,14]. Modelers have to assess the quality, relevance, and suitability of preliminary data for geographic modeling.…”

Section: Introductionmentioning

confidence: 99%

“…These data preparation steps often contain many operations that are repeated with a traditional manual method for most cases of geographic modeling. This means that considerable time, expertise, and effort are required to set up a new model application, which restricts the reproducibility of previous studies, particularly for those non-expert stakeholders (e.g., policymakers from local government) [5,6,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

Hou

Qin

Zhu

et al. 2019

IJGI

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One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

Hou

Qin

Zhu

et al. 2019

IJGI

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show abstract

“…Each granule contains only the spatiotemporal extent metadata. We prototyped the model as an online catalog system within EarthCube CyberConnector [17][18][19][20]. We have made the final system available online at: http://cube.csiss.gmu.edu/CyberConnector/web/covali.…”

Section: Introductionmentioning

confidence: 99%

Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS

Gaigalas

Sun

2019

IJGI

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Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due to the tremendously increasing data size. This paper discusses the methods and techniques for solving the inter-related problems encountered when transmitting, processing, and serving metadata for heterogeneous Earth System Observation and Modeling (ESOM) data. A cyberinfrastructure-based solution is proposed to enable effective cataloging and two-step search on big climatic datasets by leveraging state-of-the-art web service technologies and crawling the existing data centers. To validate its feasibility, the big dataset served by UCAR THREDDS Data Server (TDS), which provides Petabyte-level ESOM data and updates hundreds of terabytes of data every day, is used as the case study dataset. A complete workflow is designed to analyze the metadata structure in TDS and create an index for data parameters. A simplified registration model which defines constant information, delimits secondary information, and exploits spatial and temporal coherence in metadata is constructed. The model derives a sampling strategy for a high-performance concurrent web crawler bot which is used to mirror the essential metadata of the big data archive without overwhelming network and computing resources. The metadata model, crawler, and standard-compliant catalog service form an incremental search cyberinfrastructure, allowing scientists to search the big climatic datasets in near real-time. The proposed approach has been tested on UCAR TDS and the results prove that it achieves its design goal by at least boosting the crawling speed by 10 times and reducing the redundant metadata from 1.85 gigabytes to 2.2 megabytes, which is a significant breakthrough for making the current most non-searchable climate data servers searchable.

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“…By using web-based maps, geospatial data, geoprocessing services, and sensor web services, researchers can efficiently use geographic information resources to support spatial decision-making and geoscience applications [17][18][19][20][21][22]. Di demonstrated that the framework based on the OGC web service (OWS) facilitates interoperability between Earth observation data and geoprocessing modeling [23]. Specialists have also conducted geographic processing research that is based on grid technology and a cloud computing environment [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network

Tan

Zhong

et al. 2018

IJGI

Self Cite

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To understand and solve various natural environmental problems, geoscience research activities are becoming increasingly dependent on the integration of knowledge, data, and algorithms from scientists at different institutes and with multiple perspectives. However, the facilitation of these integrations remains a challenge because such scientific activities require gathering numerous geoscience researchers to provide data, knowledge, algorithms, and tools from different institutes and geographically distributed locations. The pivotal issue that needs to be addressed is the identification of a method to effectively combine geoscience algorithms in a distributed environment to promote cooperation. To address this issue, in this paper, a scheme for building a distributed geoscience algorithm integration based on the Open Geospatial Consortium web service (OWS) specifications is proposed. The architecture of the geoscience algorithm integration, algorithm service management mechanism, XML description method for algorithm integration, and integrated model execution strategy are designed and implemented. The experiment implements the integration of geoscience algorithms in a distributed cloud environment and evaluates the feasibility and efficiency of the integrated geoscience model. The proposed method provides a theoretical basis and practical guidance for promoting the integration of distributed geoscience algorithms; this approach can help to aggregate the distributed geoscience capabilities to address natural challenges.

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Coupling of Earth science models and earth observations through OGC interoperability specifications

Cited by 4 publications

References 10 publications

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS

Distributed Geoscience Algorithm Integration Based on OWS Specifications: A Case Study of the Extraction of a River Network

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