A Decision Support System Based on Machine Learning for Land Investment.
This research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes. The proposed methodology involves data preproces...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Education for Pure Sciences
2023-12-01
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| Series: | مجلة التربية والعلم |
| Subjects: | |
| Online Access: | https://edusj.mosuljournals.com/article_179955_2cd684b6e5214a5a7ac6e6e91d9cbf06.pdf |
| _version_ | 1797447457158725632 |
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| author | Dhufr Hussein Alali Timur Inan |
| author_facet | Dhufr Hussein Alali Timur Inan |
| author_sort | Dhufr Hussein Alali |
| collection | DOAJ |
| description | This research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes. The proposed methodology involves data preprocessing, dataset splitting, training images, model selection, model training, and evaluation using performance measures. Three neural network models (ResNet50, VGG19, and EfficientNetB3) are compared, and the best model is selected based on performance metrics such as precision, recall, F1-score, and the confusion matrix. The results show the effectiveness of the proposed methodology in accurately classifying aerial photographs. This indicates that EfficientNetB3 has a higher ability to classify aerial photographs and lands compared to ResNet50 and VGG19. ResNet50 achieved moderate performance with relatively lower precision, recall, and F1-score compared to EfficientNetB3. VGG19, on the other hand, demonstrated the lowest performance across all metrics, showing low precision, recall, and F1-score values. These results can contribute to various applications such as urban planning, real estate development, and land management. |
| first_indexed | 2024-03-09T13:56:13Z |
| format | Article |
| id | doaj.art-880607fe31eb4a9da2c7a006658a2022 |
| institution | Directory Open Access Journal |
| issn | 1812-125X 2664-2530 |
| language | Arabic |
| last_indexed | 2024-03-09T13:56:13Z |
| publishDate | 2023-12-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | مجلة التربية والعلم |
| spelling | doaj.art-880607fe31eb4a9da2c7a006658a20222023-11-30T18:30:02ZaraCollege of Education for Pure Sciencesمجلة التربية والعلم1812-125X2664-25302023-12-01324344710.33899/edusj.2023.141005.1375179955A Decision Support System Based on Machine Learning for Land Investment.Dhufr Hussein Alali0Timur Inan1Ninavah Investment CommissionAltinbas University, Information Technologies, Istanbul, TurkeyThis research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes. The proposed methodology involves data preprocessing, dataset splitting, training images, model selection, model training, and evaluation using performance measures. Three neural network models (ResNet50, VGG19, and EfficientNetB3) are compared, and the best model is selected based on performance metrics such as precision, recall, F1-score, and the confusion matrix. The results show the effectiveness of the proposed methodology in accurately classifying aerial photographs. This indicates that EfficientNetB3 has a higher ability to classify aerial photographs and lands compared to ResNet50 and VGG19. ResNet50 achieved moderate performance with relatively lower precision, recall, and F1-score compared to EfficientNetB3. VGG19, on the other hand, demonstrated the lowest performance across all metrics, showing low precision, recall, and F1-score values. These results can contribute to various applications such as urban planning, real estate development, and land management.https://edusj.mosuljournals.com/article_179955_2cd684b6e5214a5a7ac6e6e91d9cbf06.pdfdeep learning,,,،transfer learning,,,،aerial photograph classification |
| spellingShingle | Dhufr Hussein Alali Timur Inan A Decision Support System Based on Machine Learning for Land Investment. مجلة التربية والعلم deep learning,, ,،transfer learning,, ,،aerial photograph classification |
| title | A Decision Support System Based on Machine Learning for Land Investment. |
| title_full | A Decision Support System Based on Machine Learning for Land Investment. |
| title_fullStr | A Decision Support System Based on Machine Learning for Land Investment. |
| title_full_unstemmed | A Decision Support System Based on Machine Learning for Land Investment. |
| title_short | A Decision Support System Based on Machine Learning for Land Investment. |
| title_sort | decision support system based on machine learning for land investment |
| topic | deep learning,, ,،transfer learning,, ,،aerial photograph classification |
| url | https://edusj.mosuljournals.com/article_179955_2cd684b6e5214a5a7ac6e6e91d9cbf06.pdf |
| work_keys_str_mv | AT dhufrhusseinalali adecisionsupportsystembasedonmachinelearningforlandinvestment AT timurinan adecisionsupportsystembasedonmachinelearningforlandinvestment AT dhufrhusseinalali decisionsupportsystembasedonmachinelearningforlandinvestment AT timurinan decisionsupportsystembasedonmachinelearningforlandinvestment |