Land Use Patch Generalization Based on Semantic Priority
Land use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationsh...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/151520 |
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| _version_ | 1849411590520045568 |
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| author | Jun Yang Fanqiang Kong Jianchao Xi Quansheng Ge Xueming Li Peng Xie |
| author_facet | Jun Yang Fanqiang Kong Jianchao Xi Quansheng Ge Xueming Li Peng Xie |
| author_sort | Jun Yang |
| collection | DOAJ |
| description | Land use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationships, retrieve the most neighboring patches of a feature using the model for data combination, and thus guarantee the area of various land types in patch combination. (2) We establish patch features using nodes at the intersection of separate feature buffers to fill the bridge area to achieve feature aggregation and effectively control nonbridge area deformation during feature aggregation. (3) We simplify the narrow zones by dividing them from the adjacent feature buffer area and then amalgamating them into the surrounding features. This effectively deletes narrow features and meets the area requirements, better generalizes land use features, and guarantees simple and attractive maps with appropriate loads. (4) We simplify the feature sidelines using the Douglas-Peucker algorithm to effectively eliminate nodes having little impact on overall shapes and characteristics. Here, we discuss the model and algorithm process in detail and provide experimental results of the actual data. |
| format | Article |
| id | doaj-art-5d4323db5bbb4bf0b17a8d3feab367a6 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-5d4323db5bbb4bf0b17a8d3feab367a62025-08-20T03:34:44ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/151520151520Land Use Patch Generalization Based on Semantic PriorityJun Yang0Fanqiang Kong1Jianchao Xi2Quansheng Ge3Xueming Li4Peng Xie5Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaLiaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaInstitute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaInstitute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaLiaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaLiaoning Key Laboratory of Physical Geography and Geomatics, Dalian 116029, ChinaLand use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationships, retrieve the most neighboring patches of a feature using the model for data combination, and thus guarantee the area of various land types in patch combination. (2) We establish patch features using nodes at the intersection of separate feature buffers to fill the bridge area to achieve feature aggregation and effectively control nonbridge area deformation during feature aggregation. (3) We simplify the narrow zones by dividing them from the adjacent feature buffer area and then amalgamating them into the surrounding features. This effectively deletes narrow features and meets the area requirements, better generalizes land use features, and guarantees simple and attractive maps with appropriate loads. (4) We simplify the feature sidelines using the Douglas-Peucker algorithm to effectively eliminate nodes having little impact on overall shapes and characteristics. Here, we discuss the model and algorithm process in detail and provide experimental results of the actual data.http://dx.doi.org/10.1155/2013/151520 |
| spellingShingle | Jun Yang Fanqiang Kong Jianchao Xi Quansheng Ge Xueming Li Peng Xie Land Use Patch Generalization Based on Semantic Priority Abstract and Applied Analysis |
| title | Land Use Patch Generalization Based on Semantic Priority |
| title_full | Land Use Patch Generalization Based on Semantic Priority |
| title_fullStr | Land Use Patch Generalization Based on Semantic Priority |
| title_full_unstemmed | Land Use Patch Generalization Based on Semantic Priority |
| title_short | Land Use Patch Generalization Based on Semantic Priority |
| title_sort | land use patch generalization based on semantic priority |
| url | http://dx.doi.org/10.1155/2013/151520 |
| work_keys_str_mv | AT junyang landusepatchgeneralizationbasedonsemanticpriority AT fanqiangkong landusepatchgeneralizationbasedonsemanticpriority AT jianchaoxi landusepatchgeneralizationbasedonsemanticpriority AT quanshengge landusepatchgeneralizationbasedonsemanticpriority AT xuemingli landusepatchgeneralizationbasedonsemanticpriority AT pengxie landusepatchgeneralizationbasedonsemanticpriority |