Research on the application of neural network based external location element settlement method in object location of geographic information
Background: Geographical information about human settlements is necessary to facilitate humanitarian aid, recognize the location of real-world objects, support local development and improve disaster resilience in settlements. Although deep learning is employed in object location in settlements, thes...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Journal of King Saud University: Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1018364722006449 |
| _version_ | 1797952823634165760 |
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| author | Yang Nana Cuijian |
| author_facet | Yang Nana Cuijian |
| author_sort | Yang Nana |
| collection | DOAJ |
| description | Background: Geographical information about human settlements is necessary to facilitate humanitarian aid, recognize the location of real-world objects, support local development and improve disaster resilience in settlements. Although deep learning is employed in object location in settlements, these models have computational complexity and less detection accuracy. Hence they require further development with regards to precision in object location from geographic data. Objective: To efficiently identify the object location in Chinese settlements from geographic information, we proposed Artificial Neural Network (ANN) optimized by Red Fox (RF) optimization (ANN-RF) model in this research. Methods: The multi-resolution Geographic information data was collected from Tongzhou District, Beijing, China. ANN applies the measures of length, area, distance and direction to recognize object location from this data. An RF algorithm is used to optimize the weight vector of the ANN architecture to improve the efficiency of the ANN architecture. The parameters such as precision, recall, F1-score, number of correct detections, overall actual matches and the total number of detections were computed for the proposed model. Results: The precision rate and F1-score for the proposed ANN based RF optimization are about 98.9% and 97.5% which are higher than that of conventional models. Conclusion: The proposed ANN-RF model shows promising results in detecting object location in settlements from geographic information. |
| first_indexed | 2024-04-10T22:52:11Z |
| format | Article |
| id | doaj.art-0bc9d7fcad414451844c8ee7f1907a22 |
| institution | Directory Open Access Journal |
| issn | 1018-3647 |
| language | English |
| last_indexed | 2024-04-10T22:52:11Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Science |
| spelling | doaj.art-0bc9d7fcad414451844c8ee7f1907a222023-01-15T04:21:14ZengElsevierJournal of King Saud University: Science1018-36472023-02-01352102463Research on the application of neural network based external location element settlement method in object location of geographic informationYang Nana0 Cuijian1Qilu Research Institute, Aerospace Information Innovation Research Institute, Chinese Academy of Sciences, ChinaShandong Jian Zhu University, School of Surveying, Mapping and Geographic Information, China; Corresponding author.Background: Geographical information about human settlements is necessary to facilitate humanitarian aid, recognize the location of real-world objects, support local development and improve disaster resilience in settlements. Although deep learning is employed in object location in settlements, these models have computational complexity and less detection accuracy. Hence they require further development with regards to precision in object location from geographic data. Objective: To efficiently identify the object location in Chinese settlements from geographic information, we proposed Artificial Neural Network (ANN) optimized by Red Fox (RF) optimization (ANN-RF) model in this research. Methods: The multi-resolution Geographic information data was collected from Tongzhou District, Beijing, China. ANN applies the measures of length, area, distance and direction to recognize object location from this data. An RF algorithm is used to optimize the weight vector of the ANN architecture to improve the efficiency of the ANN architecture. The parameters such as precision, recall, F1-score, number of correct detections, overall actual matches and the total number of detections were computed for the proposed model. Results: The precision rate and F1-score for the proposed ANN based RF optimization are about 98.9% and 97.5% which are higher than that of conventional models. Conclusion: The proposed ANN-RF model shows promising results in detecting object location in settlements from geographic information.http://www.sciencedirect.com/science/article/pii/S1018364722006449Geographic informationMulti-resolution representation datasetArtificial neural networkRed fox optimization |
| spellingShingle | Yang Nana Cuijian Research on the application of neural network based external location element settlement method in object location of geographic information Journal of King Saud University: Science Geographic information Multi-resolution representation dataset Artificial neural network Red fox optimization |
| title | Research on the application of neural network based external location element settlement method in object location of geographic information |
| title_full | Research on the application of neural network based external location element settlement method in object location of geographic information |
| title_fullStr | Research on the application of neural network based external location element settlement method in object location of geographic information |
| title_full_unstemmed | Research on the application of neural network based external location element settlement method in object location of geographic information |
| title_short | Research on the application of neural network based external location element settlement method in object location of geographic information |
| title_sort | research on the application of neural network based external location element settlement method in object location of geographic information |
| topic | Geographic information Multi-resolution representation dataset Artificial neural network Red fox optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1018364722006449 |
| work_keys_str_mv | AT yangnana researchontheapplicationofneuralnetworkbasedexternallocationelementsettlementmethodinobjectlocationofgeographicinformation AT cuijian researchontheapplicationofneuralnetworkbasedexternallocationelementsettlementmethodinobjectlocationofgeographicinformation |