Identification of vacant land in Tegal Regency using cnn algorithm based on goolge earth imagery
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Published:
Jun 30, 2024
Keywords:
Convolutional neural network, Google earth imagery, Image preprocessing, Image sharing, Vacant land identification
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Abstract
This research developed a Convolutional Neural Network (CNN) algorithm to identify vacant land in Tegal Regency using imagery from Google Earth. By utilizing labeled imagery datasets, CNN models are optimized to recognize texture characteristics, colors, and distribution patterns of vacant land. Preprocessing and image sharing techniques are applied to improve model quality. The results of this study offer a new methodology in visual data processing for accurate and efficient identification of vacant land, providing a solid basis for more sustainable and efficient land use policies. This research contributes significantly to the scientific literature and field practice, particularly in natural resource management and regional planning
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Andriani, W. ., Fatkhurrohman, F. ., & Gunawan, G. . (2024). Identification of vacant land in Tegal Regency using cnn algorithm based on goolge earth imagery. Journal of Intelligent Decision Support System (IDSS), 7(2), 146-154. https://doi.org/10.35335/idss.v7i2.243
References
Abbas, A., Alroobaea, R., Krichen, M., Rubaiee, S., Vimal, S., & Almansour, F. M. (2024). Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Personal and Ubiquitous Computing, 28(1), 59–72. https://doi.org/10.1007/s00779-021-01626-0
Addesso, P., Restaino, R., & Vivone, G. (2021). An improved version of the generalized Laplacian pyramid algorithm for pansharpening. Remote Sensing, 13(17), 1–20. https://doi.org/10.3390/rs13173386
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, 112128. https://doi.org/10.1016/j.rser.2022.112128
Ahsan, M., Based, M. A., Haider, J., & Kowalski, M. (2021). COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors, 21(4), 1480. https://doi.org/doi.org/10.3390/s21041480
Ayhan, Ç. K., Taşlı, T. C., Özkök, F., & Tatlı, H. (2020). Land use suitability analysis of rural tourism activities: Yenice, Turkey. Tourism Management, 76, 103949. https://doi.org/10.1016/j.tourman.2019.07.003
Bibri, S. E. (2020). Compact urbanism and the synergic potential of its integration with data-driven smart urbanism: An extensive interdisciplinary literature review. Land Use Policy, 97, 104703. https://doi.org/10.1016/j.landusepol.2020.104703
Buschbacher, K., Ahrens, D., Espeland, M., & Steinhage, V. (2020). Image-based species identification of wild bees using convolutional neural networks. Ecological Informatics, 55, 101017. https://doi.org/10.1016/j.ecoinf.2019.101017
Cai, L. Y., Yang, Q., Hansen, C. B., Nath, V., Ramadass, K., Johnson, G. W., Conrad, B. N., Boyd, B. D., Begnoche, J. P., & Beason‐Held, L. L. (2021). PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magnetic Resonance in Medicine, 86(1), 456–470. https://doi.org/10.1002/mrm.28678
Giuffrida, G., Fanucci, L., Meoni, G., Batič, M., Buckley, L., Dunne, A., Van Dijk, C., Esposito, M., Hefele, J., & Vercruyssen, N. (2021). The Φ-Sat-1 mission: The first on-board deep neural network demonstrator for satellite earth observation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. https://doi.org/10.1109/TGRS.2021.3125567
Jun, J., Kim, J., Seol, J., Kim, J., & Son, H. Il. (2021). Towards an efficient tomato harvesting robot: 3d perception, manipulation, and end-effector. IEEE Access, 9, 17631–17640. https://doi.org/10.1109/ACCESS.2021.3052240
Kafy, A. Al, Faisal, A. Al, Shuvo, R. M., Naim, M. N. H., Sikdar, M. S., Chowdhury, R. R., Islam, M. A., Sarker, M. H. S., Khan, M. H. H., & Kona, M. A. (2021). Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21(August 2020), 100463. https://doi.org/10.1016/j.rsase.2020.100463
Kamir, E., Waldner, F., & Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 124–135. https://doi.org/10.1016/j.isprsjprs.2019.11.008
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53, 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
LeBien, J., Zhong, M., Campos-Cerqueira, M., Velev, J. P., Dodhia, R., Ferres, J. L., & Aide, T. M. (2020). A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network. Ecological Informatics, 59, 101113. https://doi.org/10.1016/j.ecoinf.2020.101113
Li, W., & Hsu, C.-Y. (2020). Automated terrain feature identification from remote sensing imagery: a deep learning approach. International Journal of Geographical Information Science, 34(4), 637–660. https://doi.org/10.1080/13658816.2018.1542697
Li, Z., Shen, H., Weng, Q., Zhang, Y., Dou, P., & Zhang, L. (2022). Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 89–108. https://doi.org/10.1016/j.isprsjprs.2022.03.020
Liu, J., Jin, X., Xu, W., Gu, Z., Yang, X., Ren, J., Fan, Y., & Zhou, Y. (2020). A new framework of land use efficiency for the coordination among food, economy and ecology in regional development. Science of the Total Environment, 710, 135670. https://doi.org/10.1016/j.scitotenv.2019.135670
Masood, S., Rai, A., Aggarwal, A., Doja, M. N., & Ahmad, M. (2020). Detecting distraction of drivers using convolutional neural network. Pattern Recognition Letters, 139, 79–85. https://doi.org/10.1016/j.patrec.2017.12.023
Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 1: Literature review. Remote Sensing, 13(13), 2450. https://doi.org/10.3390/rs13132450
Song, X., Wen, M., Shen, Y., Feng, Q., Xiang, J., Zhang, W., Zhao, G., & Wu, Z. (2020). Urban vacant land in growing urbanization: An international review. Journal of Geographical Sciences, 30, 669–687. https://doi.org/10.1007/s11442-020-1749-0
Vargas-Munoz, J. E., Srivastava, S., Tuia, D., & Falcao, A. X. (2020). OpenStreetMap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1), 184–199. https://doi.org/10.1109/MGRS.2020.2994107
Zhang, B., Huang, S., Zhang, L., Liu, X., Song, G., Qin, J., & Li, M. (2022). Method of railway shunting operation sheet information extraction guided by table header. IET Intelligent Transport Systems, 16(10), 1391–1403. https://doi.org/10.1049/itr2.12213
Zhang, B., Wu, Y., Zhao, B., Chanussot, J., Hong, D., Yao, J., & Gao, L. (2022). Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1814–1822. https://doi.org/https://doi.org/10.1109/JSTARS.2022.3148139
Addesso, P., Restaino, R., & Vivone, G. (2021). An improved version of the generalized Laplacian pyramid algorithm for pansharpening. Remote Sensing, 13(17), 1–20. https://doi.org/10.3390/rs13173386
Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, 112128. https://doi.org/10.1016/j.rser.2022.112128
Ahsan, M., Based, M. A., Haider, J., & Kowalski, M. (2021). COVID-19 detection from chest X-ray images using feature fusion and deep learning. Sensors, 21(4), 1480. https://doi.org/doi.org/10.3390/s21041480
Ayhan, Ç. K., Taşlı, T. C., Özkök, F., & Tatlı, H. (2020). Land use suitability analysis of rural tourism activities: Yenice, Turkey. Tourism Management, 76, 103949. https://doi.org/10.1016/j.tourman.2019.07.003
Bibri, S. E. (2020). Compact urbanism and the synergic potential of its integration with data-driven smart urbanism: An extensive interdisciplinary literature review. Land Use Policy, 97, 104703. https://doi.org/10.1016/j.landusepol.2020.104703
Buschbacher, K., Ahrens, D., Espeland, M., & Steinhage, V. (2020). Image-based species identification of wild bees using convolutional neural networks. Ecological Informatics, 55, 101017. https://doi.org/10.1016/j.ecoinf.2019.101017
Cai, L. Y., Yang, Q., Hansen, C. B., Nath, V., Ramadass, K., Johnson, G. W., Conrad, B. N., Boyd, B. D., Begnoche, J. P., & Beason‐Held, L. L. (2021). PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images. Magnetic Resonance in Medicine, 86(1), 456–470. https://doi.org/10.1002/mrm.28678
Giuffrida, G., Fanucci, L., Meoni, G., Batič, M., Buckley, L., Dunne, A., Van Dijk, C., Esposito, M., Hefele, J., & Vercruyssen, N. (2021). The Φ-Sat-1 mission: The first on-board deep neural network demonstrator for satellite earth observation. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. https://doi.org/10.1109/TGRS.2021.3125567
Jun, J., Kim, J., Seol, J., Kim, J., & Son, H. Il. (2021). Towards an efficient tomato harvesting robot: 3d perception, manipulation, and end-effector. IEEE Access, 9, 17631–17640. https://doi.org/10.1109/ACCESS.2021.3052240
Kafy, A. Al, Faisal, A. Al, Shuvo, R. M., Naim, M. N. H., Sikdar, M. S., Chowdhury, R. R., Islam, M. A., Sarker, M. H. S., Khan, M. H. H., & Kona, M. A. (2021). Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sensing Applications: Society and Environment, 21(August 2020), 100463. https://doi.org/10.1016/j.rsase.2020.100463
Kamir, E., Waldner, F., & Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 160, 124–135. https://doi.org/10.1016/j.isprsjprs.2019.11.008
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53, 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
LeBien, J., Zhong, M., Campos-Cerqueira, M., Velev, J. P., Dodhia, R., Ferres, J. L., & Aide, T. M. (2020). A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network. Ecological Informatics, 59, 101113. https://doi.org/10.1016/j.ecoinf.2020.101113
Li, W., & Hsu, C.-Y. (2020). Automated terrain feature identification from remote sensing imagery: a deep learning approach. International Journal of Geographical Information Science, 34(4), 637–660. https://doi.org/10.1080/13658816.2018.1542697
Li, Z., Shen, H., Weng, Q., Zhang, Y., Dou, P., & Zhang, L. (2022). Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 188, 89–108. https://doi.org/10.1016/j.isprsjprs.2022.03.020
Liu, J., Jin, X., Xu, W., Gu, Z., Yang, X., Ren, J., Fan, Y., & Zhou, Y. (2020). A new framework of land use efficiency for the coordination among food, economy and ecology in regional development. Science of the Total Environment, 710, 135670. https://doi.org/10.1016/j.scitotenv.2019.135670
Masood, S., Rai, A., Aggarwal, A., Doja, M. N., & Ahmad, M. (2020). Detecting distraction of drivers using convolutional neural network. Pattern Recognition Letters, 139, 79–85. https://doi.org/10.1016/j.patrec.2017.12.023
Maxwell, A. E., Warner, T. A., & Guillén, L. A. (2021). Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 1: Literature review. Remote Sensing, 13(13), 2450. https://doi.org/10.3390/rs13132450
Song, X., Wen, M., Shen, Y., Feng, Q., Xiang, J., Zhang, W., Zhao, G., & Wu, Z. (2020). Urban vacant land in growing urbanization: An international review. Journal of Geographical Sciences, 30, 669–687. https://doi.org/10.1007/s11442-020-1749-0
Vargas-Munoz, J. E., Srivastava, S., Tuia, D., & Falcao, A. X. (2020). OpenStreetMap: Challenges and opportunities in machine learning and remote sensing. IEEE Geoscience and Remote Sensing Magazine, 9(1), 184–199. https://doi.org/10.1109/MGRS.2020.2994107
Zhang, B., Huang, S., Zhang, L., Liu, X., Song, G., Qin, J., & Li, M. (2022). Method of railway shunting operation sheet information extraction guided by table header. IET Intelligent Transport Systems, 16(10), 1391–1403. https://doi.org/10.1049/itr2.12213
Zhang, B., Wu, Y., Zhao, B., Chanussot, J., Hong, D., Yao, J., & Gao, L. (2022). Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1814–1822. https://doi.org/https://doi.org/10.1109/JSTARS.2022.3148139
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