Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz
Introduction Urbanization as a revolution in human culture has transformed human interactions with one another. As the urbanization population grows, the use of the environment is intensified. Studies have shown that increasing population and expanding urbanization are turning urban green spaces int...
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| Format: | Article |
| Language: | fas |
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University of Tabriz
2021-01-01
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| Series: | نشریه جغرافیا و برنامهریزی |
| Subjects: | |
| Online Access: | https://geoplanning.tabrizu.ac.ir/article_10780_d6debb31f60fe90ef8ab0243f90e9f26.pdf |
| _version_ | 1797256882612600832 |
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| author | Hassan Mahmoudzadeh Mostafa Mahdavifard Majid Azizmoradi zanjani zanjani sani |
| author_facet | Hassan Mahmoudzadeh Mostafa Mahdavifard Majid Azizmoradi zanjani zanjani sani |
| author_sort | Hassan Mahmoudzadeh |
| collection | DOAJ |
| description | Introduction
Urbanization as a revolution in human culture has transformed human interactions with one another. As the urbanization population grows, the use of the environment is intensified. Studies have shown that increasing population and expanding urbanization are turning urban green spaces into rough and impermeable concrete surfaces, and this trend is especially serious in developing countries and the Third World. Since urban growth is a complex phenomenon in which a number of variables interact nonlinearly, the use of ANNs to model urban development and growth is perfectly reasonable. Artificial neural networks with nonlinear mapping structure have been developed for modeling interconnected systems such as the brain consisting of neurons. The artificial neural network is independent of the statistical distribution of data and does not require any specific statistical variables, so this feature facilitates the combination of remote sensing data and GIS. Currently, remote sensing science is changing a fundamental paradigm in which one- or two-image interpretation approaches pave the way for a wide array of data-rich applications. These improvements are facilitated by the GEE Satellite Image Processing System. The purpose of this research is to introduce a new system (GEE), to investigate and analyze this web portal, its application in monitoring and evaluation of human habitat changes (GHSL) and to map the relationship created using MLP model to predict physical development changes in Tabriz.
Materials and Methods
In this study, the Google Earth Engine (GEE) satellite image processing online system was used to process and extract the global GHSL product, and then the MLP model of Terset was used to predict changes.
Results and Discussion
In this study, it was attempted to analyze and analyze Landsat satellite images in a few minutes in order to prepare physical development map of Tabriz city without using hard data and to predict future development changes using the data available in Google Inheritance Satellite Image Processing System. Physically measure the city using the MLP model. GEE online processor has been able to map the growth of urbanization in the Tabriz city over the past six years. With the increase in urbanization over the past 40 years in the city of Tabriz, we have seen the destruction of about 38% of gardens and agriculture in the city, and even this system of rapid population growth in recent years (2014) on the outskirts of Tabriz as the main center of recent earthquakes.
Conclusion
It has shown the city of Tabriz and is also witnessing a growing trend towards physical development of the city in this part of Tabriz. The results of the MLP model show that the physical development of Tabriz in the future is northeastward and on the outskirts of Mount Aoun bin Ali. |
| first_indexed | 2024-04-24T22:28:48Z |
| format | Article |
| id | doaj.art-e1e1a09000934997b5d0899baa4e59cb |
| institution | Directory Open Access Journal |
| issn | 2008-8078 2717-3534 |
| language | fas |
| last_indexed | 2024-04-24T22:28:48Z |
| publishDate | 2021-01-01 |
| publisher | University of Tabriz |
| record_format | Article |
| series | نشریه جغرافیا و برنامهریزی |
| spelling | doaj.art-e1e1a09000934997b5d0899baa4e59cb2024-03-19T22:10:31ZfasUniversity of Tabrizنشریه جغرافیا و برنامهریزی2008-80782717-35342021-01-01247421523210.22034/gp.2021.1078010780Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: TabrizHassan Mahmoudzadeh0Mostafa Mahdavifard1Majid Azizmoradi2zanjani zanjani sani3Associate Professor of Geography and Urban Planning, Department of Geography and Urban Planning, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, IranMs student of Remote sensing and GIS, Faculty of Planning and Environmental Sciences, University of TabrizMs in Remote sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tab rizMs student of Remote sensing and GIS, Faculty of Planning and Environmental Sciences, University of TabrizIntroduction Urbanization as a revolution in human culture has transformed human interactions with one another. As the urbanization population grows, the use of the environment is intensified. Studies have shown that increasing population and expanding urbanization are turning urban green spaces into rough and impermeable concrete surfaces, and this trend is especially serious in developing countries and the Third World. Since urban growth is a complex phenomenon in which a number of variables interact nonlinearly, the use of ANNs to model urban development and growth is perfectly reasonable. Artificial neural networks with nonlinear mapping structure have been developed for modeling interconnected systems such as the brain consisting of neurons. The artificial neural network is independent of the statistical distribution of data and does not require any specific statistical variables, so this feature facilitates the combination of remote sensing data and GIS. Currently, remote sensing science is changing a fundamental paradigm in which one- or two-image interpretation approaches pave the way for a wide array of data-rich applications. These improvements are facilitated by the GEE Satellite Image Processing System. The purpose of this research is to introduce a new system (GEE), to investigate and analyze this web portal, its application in monitoring and evaluation of human habitat changes (GHSL) and to map the relationship created using MLP model to predict physical development changes in Tabriz. Materials and Methods In this study, the Google Earth Engine (GEE) satellite image processing online system was used to process and extract the global GHSL product, and then the MLP model of Terset was used to predict changes. Results and Discussion In this study, it was attempted to analyze and analyze Landsat satellite images in a few minutes in order to prepare physical development map of Tabriz city without using hard data and to predict future development changes using the data available in Google Inheritance Satellite Image Processing System. Physically measure the city using the MLP model. GEE online processor has been able to map the growth of urbanization in the Tabriz city over the past six years. With the increase in urbanization over the past 40 years in the city of Tabriz, we have seen the destruction of about 38% of gardens and agriculture in the city, and even this system of rapid population growth in recent years (2014) on the outskirts of Tabriz as the main center of recent earthquakes. Conclusion It has shown the city of Tabriz and is also witnessing a growing trend towards physical development of the city in this part of Tabriz. The results of the MLP model show that the physical development of Tabriz in the future is northeastward and on the outskirts of Mount Aoun bin Ali.https://geoplanning.tabrizu.ac.ir/article_10780_d6debb31f60fe90ef8ab0243f90e9f26.pdfgoogle earth enginephysical development of the citymulti layer perceptronremote sensing |
| spellingShingle | Hassan Mahmoudzadeh Mostafa Mahdavifard Majid Azizmoradi zanjani zanjani sani Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz نشریه جغرافیا و برنامهریزی google earth engine physical development of the city multi layer perceptron remote sensing |
| title | Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz |
| title_full | Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz |
| title_fullStr | Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz |
| title_full_unstemmed | Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz |
| title_short | Modeling physical development by combining the capabilities of Google Earth Engine (GEE) and Artificial Neural Network (MLP) the Case Study: Tabriz |
| title_sort | modeling physical development by combining the capabilities of google earth engine gee and artificial neural network mlp the case study tabriz |
| topic | google earth engine physical development of the city multi layer perceptron remote sensing |
| url | https://geoplanning.tabrizu.ac.ir/article_10780_d6debb31f60fe90ef8ab0243f90e9f26.pdf |
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