Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation
Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines, such as urban planning, environmental sciences, and health care. Thus, remote-sensing experts must handle various and complex image...
| Published in: | Journal of Spatial Information Science |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
University of Maine
2013-12-01
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| Subjects: | |
| Online Access: | http://josis.org/index.php/josis/article/view/142 |
| _version_ | 1857030416438919168 |
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| author | Christelle Pierkot Samuel Andrés Jean François Faure Frédérique Seyler |
| author_facet | Christelle Pierkot Samuel Andrés Jean François Faure Frédérique Seyler |
| author_sort | Christelle Pierkot |
| collection | DOAJ |
| container_title | Journal of Spatial Information Science |
| description | Technological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines, such as urban planning, environmental sciences, and health care. Thus, remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features, and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result, we propose a high level conceptual framework, based on existing and standardized approaches, offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge. |
| format | Article |
| id | doaj-art-796913a9fdcc4ebc86c19e37ecaf3e10 |
| institution | Directory of Open Access Journals |
| issn | 1948-660X |
| language | English |
| publishDate | 2013-12-01 |
| publisher | University of Maine |
| record_format | Article |
| spelling | doaj-art-796913a9fdcc4ebc86c19e37ecaf3e102025-08-19T19:40:25ZengUniversity of MaineJournal of Spatial Information Science1948-660X2013-12-0120137779810.5311/JOSIS.2013.7.14291Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretationChristelle Pierkot0Samuel Andrés1Jean François Faure2Frédérique Seyler3IRD UMR Espace-DevIRD UMR Espace-DevIRD UMR Espace-DevIRD UMR Espace-DevTechnological tools allow the generation of large volumes of data. For example satellite images aid in the study of spatiotemporal phenomena in a range of disciplines, such as urban planning, environmental sciences, and health care. Thus, remote-sensing experts must handle various and complex image sets for their interpretations. The GIS community has undertaken significant work in describing spatiotemporal features, and standard specifications nowadays provide design foundations for GIS software and spatial databases. We argue that this spatiotemporal knowledge and expertise would provide invaluable support for the field of image interpretation. As a result, we propose a high level conceptual framework, based on existing and standardized approaches, offering enough modularity and adaptability to represent the various dimensions of spatiotemporal knowledge.http://josis.org/index.php/josis/article/view/142Spatio-temporalitygeographic standardsremote-sensing interpretation |
| spellingShingle | Christelle Pierkot Samuel Andrés Jean François Faure Frédérique Seyler Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation Spatio-temporality geographic standards remote-sensing interpretation |
| title | Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| title_full | Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| title_fullStr | Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| title_full_unstemmed | Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| title_short | Formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| title_sort | formalizing spatiotemporal knowledge in remote sensing applications to improve image interpretation |
| topic | Spatio-temporality geographic standards remote-sensing interpretation |
| url | http://josis.org/index.php/josis/article/view/142 |
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