A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images
Cities are facing many challenges related to urban growth. This phenomenon has prompted decision-makers to adopt innovative approaches for planning based on accurate forecasting of urban growth. Among the most widely used forecasting methods, there are Cellular Automata (CA) based methods and Recurr...
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Land Use Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/1747423X.2024.2403789 |
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| author | Reda Yaagoubi Charaf-Eddine Lakber Yehia Miky |
| author_facet | Reda Yaagoubi Charaf-Eddine Lakber Yehia Miky |
| author_sort | Reda Yaagoubi |
| collection | DOAJ |
| description | Cities are facing many challenges related to urban growth. This phenomenon has prompted decision-makers to adopt innovative approaches for planning based on accurate forecasting of urban growth. Among the most widely used forecasting methods, there are Cellular Automata (CA) based methods and Recurrent Neural Networks (RNN) based methods. The accuracy of these forecasting models is strongly related to data quality, data availability, Model calibration and Model validation. In this paper, a comparative analysis between three forecasting methods is presented based on a temporal sequence of Sentinel 2A images. The main goal of this study is to assess the performance of these models which are of CA-Markov Chain, MLP-Markov and ConvLSTM in terms of accuracy, complexity, and feasibility. The case study is carried out on the city of Casablanca in Morocco. After implementing these three forecasting methods, the obtained results show that the Kappa coefficient of MLP-Markov, CA-Markov and ConvLSTM is, respectively, 89,40%; 97,20%; and 94,50%. In terms of complexity, the ConvLSTM method is more complex due to the number of elementary operations. In terms of feasibility, the ConvLSTM method is more demanding in terms of data volume since it is a Deep Learning model. Accordingly, CA-Markov based methods, in particular MLP-Markov, show a great potential for forecasting urban growth, especially for short term forecasting when there are not enough satellite images available to adopt a Deep Learning approach such as ConvLSTM. |
| format | Article |
| id | doaj-art-3a5d292a0b5d4be08b226315cd7a0e21 |
| institution | OA Journals |
| issn | 1747-423X 1747-4248 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | Journal of Land Use Science |
| spelling | doaj-art-3a5d292a0b5d4be08b226315cd7a0e212025-08-20T02:33:44ZengTaylor & Francis GroupJournal of Land Use Science1747-423X1747-42482024-12-0119125827710.1080/1747423X.2024.2403789A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A imagesReda Yaagoubi0Charaf-Eddine Lakber1Yehia Miky2School of Geomatics and Surveying Engineering, Hassan II Institute of Agriculture and Veterinary Medicine, Rabat, MoroccoSchool of Geomatics and Surveying Engineering, Hassan II Institute of Agriculture and Veterinary Medicine, Rabat, MoroccoGeomatics Department, Faculty of Architecture and Planning, King Abdulaziz University, Jeddah, Saudi ArabiaCities are facing many challenges related to urban growth. This phenomenon has prompted decision-makers to adopt innovative approaches for planning based on accurate forecasting of urban growth. Among the most widely used forecasting methods, there are Cellular Automata (CA) based methods and Recurrent Neural Networks (RNN) based methods. The accuracy of these forecasting models is strongly related to data quality, data availability, Model calibration and Model validation. In this paper, a comparative analysis between three forecasting methods is presented based on a temporal sequence of Sentinel 2A images. The main goal of this study is to assess the performance of these models which are of CA-Markov Chain, MLP-Markov and ConvLSTM in terms of accuracy, complexity, and feasibility. The case study is carried out on the city of Casablanca in Morocco. After implementing these three forecasting methods, the obtained results show that the Kappa coefficient of MLP-Markov, CA-Markov and ConvLSTM is, respectively, 89,40%; 97,20%; and 94,50%. In terms of complexity, the ConvLSTM method is more complex due to the number of elementary operations. In terms of feasibility, the ConvLSTM method is more demanding in terms of data volume since it is a Deep Learning model. Accordingly, CA-Markov based methods, in particular MLP-Markov, show a great potential for forecasting urban growth, especially for short term forecasting when there are not enough satellite images available to adopt a Deep Learning approach such as ConvLSTM.https://www.tandfonline.com/doi/10.1080/1747423X.2024.2403789Forecasting urban growthCA-MarkovMLP-MarkovConvLSTM |
| spellingShingle | Reda Yaagoubi Charaf-Eddine Lakber Yehia Miky A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images Journal of Land Use Science Forecasting urban growth CA-Markov MLP-Markov ConvLSTM |
| title | A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images |
| title_full | A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images |
| title_fullStr | A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images |
| title_full_unstemmed | A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images |
| title_short | A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images |
| title_sort | comparative analysis on the use of a cellular automata markov chain versus a convolutional lstm model in forecasting urban growth using sentinel 2a images |
| topic | Forecasting urban growth CA-Markov MLP-Markov ConvLSTM |
| url | https://www.tandfonline.com/doi/10.1080/1747423X.2024.2403789 |
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