Automated curation of spatial metadata in environmental monitoring data
Spatial data accuracy in environmental monitoring is crucial for practical large-scale data analytics and the development of AI models. In this context, spatial data is metadata and faces the same challenges as any other metadata, like missing values, false or contradicting information, formatting p...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000470 |
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| author | İlhan Mutlu Jörg Hackermüller Jana Schor |
| author_facet | İlhan Mutlu Jörg Hackermüller Jana Schor |
| author_sort | İlhan Mutlu |
| collection | DOAJ |
| description | Spatial data accuracy in environmental monitoring is crucial for practical large-scale data analytics and the development of AI models. In this context, spatial data is metadata and faces the same challenges as any other metadata, like missing values, false or contradicting information, formatting problems of textual data and numbers, the usage of different languages, and more. These issues severely limit the usability of the data.With this study, we provide an automatic approach, CleanGeoStreamR, to resolve as many of these issues as possible for the spatially annotated environmental monitoring database. We substantially increased the quality of the spatial metadata and, therefore, the quantity of data points that can be used in large-scale data analytics and AI applications.Further, our goal is to raise awareness about the issues related to spatial metadata and promote the implementation of our concepts in other environmental monitoring data sources. Advanced understanding and the availability of automatic approaches like the presented method will substantially contribute to making environmental monitoring data FAIR and enhance its usability in the era of Big Data and AI. |
| format | Article |
| id | doaj-art-92b0b80723cb48c580f7a695369ca84c |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-92b0b80723cb48c580f7a695369ca84c2025-01-31T05:10:57ZengElsevierEcological Informatics1574-95412025-05-0186103038Automated curation of spatial metadata in environmental monitoring dataİlhan Mutlu0Jörg Hackermüller1Jana Schor2Department of Computational Biology and Chemistry, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany; Corresponding authors.Department of Computational Biology and Chemistry, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany; Department of Computer Science, Faculty of Mathematics and Computer Science, Leipzig University, 04109 Leipzig, Germany; Corresponding authors.Department of Computational Biology and Chemistry, Helmholtz Centre for Environmental Research - UFZ, 04318 Leipzig, Germany; Department of Computer Science, Faculty of Mathematics and Computer Science, Leipzig University, 04109 Leipzig, GermanySpatial data accuracy in environmental monitoring is crucial for practical large-scale data analytics and the development of AI models. In this context, spatial data is metadata and faces the same challenges as any other metadata, like missing values, false or contradicting information, formatting problems of textual data and numbers, the usage of different languages, and more. These issues severely limit the usability of the data.With this study, we provide an automatic approach, CleanGeoStreamR, to resolve as many of these issues as possible for the spatially annotated environmental monitoring database. We substantially increased the quality of the spatial metadata and, therefore, the quantity of data points that can be used in large-scale data analytics and AI applications.Further, our goal is to raise awareness about the issues related to spatial metadata and promote the implementation of our concepts in other environmental monitoring data sources. Advanced understanding and the availability of automatic approaches like the presented method will substantially contribute to making environmental monitoring data FAIR and enhance its usability in the era of Big Data and AI.http://www.sciencedirect.com/science/article/pii/S1574954125000470Environmental monitoringSpatial data accuracyAutomated data curationBig data analyticsAI applications in hydrology |
| spellingShingle | İlhan Mutlu Jörg Hackermüller Jana Schor Automated curation of spatial metadata in environmental monitoring data Ecological Informatics Environmental monitoring Spatial data accuracy Automated data curation Big data analytics AI applications in hydrology |
| title | Automated curation of spatial metadata in environmental monitoring data |
| title_full | Automated curation of spatial metadata in environmental monitoring data |
| title_fullStr | Automated curation of spatial metadata in environmental monitoring data |
| title_full_unstemmed | Automated curation of spatial metadata in environmental monitoring data |
| title_short | Automated curation of spatial metadata in environmental monitoring data |
| title_sort | automated curation of spatial metadata in environmental monitoring data |
| topic | Environmental monitoring Spatial data accuracy Automated data curation Big data analytics AI applications in hydrology |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000470 |
| work_keys_str_mv | AT ilhanmutlu automatedcurationofspatialmetadatainenvironmentalmonitoringdata AT jorghackermuller automatedcurationofspatialmetadatainenvironmentalmonitoringdata AT janaschor automatedcurationofspatialmetadatainenvironmentalmonitoringdata |