Semantic Integration of Raster Data for Earth Observation on Territorial Units
Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/2/149 |
| _version_ | 1797479432083996672 |
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| author | Ba-Huy Tran Nathalie Aussenac-Gilles Catherine Comparot Cassia Trojahn |
| author_facet | Ba-Huy Tran Nathalie Aussenac-Gilles Catherine Comparot Cassia Trojahn |
| author_sort | Ba-Huy Tran |
| collection | DOAJ |
| description | Semantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to <i>territorial units</i> (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a <i>semantic extraction, transformation and load</i> (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach. |
| first_indexed | 2024-03-09T21:45:48Z |
| format | Article |
| id | doaj.art-31e6f55f1a6a46d4a5a3261c0ac4abfa |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T21:45:48Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-31e6f55f1a6a46d4a5a3261c0ac4abfa2023-11-23T20:16:38ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-02-0111214910.3390/ijgi11020149Semantic Integration of Raster Data for Earth Observation on Territorial UnitsBa-Huy Tran0Nathalie Aussenac-Gilles1Catherine Comparot2Cassia Trojahn3Institut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse INP, UT3, UT1, UT2, F-31062 Toulouse, FranceInstitut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse INP, UT3, UT1, UT2, F-31062 Toulouse, FranceInstitut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse INP, UT3, UT1, UT2, F-31062 Toulouse, FranceInstitut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse, CNRS, Toulouse INP, UT3, UT1, UT2, F-31062 Toulouse, FranceSemantic technologies have proven their relevance in facilitating the interpretation of Earth Observation (EO) data through formats such as RDF and reusable models, especially for the representation of space and time. While rasters are the usual data format for the results of image processing algorithms, a recurrent problem is transferring the pixel values of these rasters into features that make sense of the areas of interest on the Earth, thus facilitating the interpretation of their content. This paper addresses this issue through a semantic data integration process based on spatial and temporal properties. We propose (i) a modular and generic semantic model for the homogeneous representation of data qualifying a geographical area of interest thanks to <i>territorial units</i> (land parcels, administrative units, forest areas, etc.) that we define as divisions of a larger territory according to a criteria in relation with human activities; and (ii) a <i>semantic extraction, transformation and load</i> (ETL) process that builds on the model and the data extracted from rasters and that maps aggregated data to the corresponding unit areas. We evaluate our approach in terms of the (i) adaptability of the proposed model and pipeline to accommodate different use cases (vineyard and urban expansion monitoring), (ii) added value of the generated datasets to assist in decision making, and (iii) scalability of the approach.https://www.mdpi.com/2220-9964/11/2/149earth observationsemantic data integrationspatial and temporal datachange detectionland coverNDVI |
| spellingShingle | Ba-Huy Tran Nathalie Aussenac-Gilles Catherine Comparot Cassia Trojahn Semantic Integration of Raster Data for Earth Observation on Territorial Units ISPRS International Journal of Geo-Information earth observation semantic data integration spatial and temporal data change detection land cover NDVI |
| title | Semantic Integration of Raster Data for Earth Observation on Territorial Units |
| title_full | Semantic Integration of Raster Data for Earth Observation on Territorial Units |
| title_fullStr | Semantic Integration of Raster Data for Earth Observation on Territorial Units |
| title_full_unstemmed | Semantic Integration of Raster Data for Earth Observation on Territorial Units |
| title_short | Semantic Integration of Raster Data for Earth Observation on Territorial Units |
| title_sort | semantic integration of raster data for earth observation on territorial units |
| topic | earth observation semantic data integration spatial and temporal data change detection land cover NDVI |
| url | https://www.mdpi.com/2220-9964/11/2/149 |
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