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A platform for research: civil engineering, architecture and urbanism
Urban Expansion Monitoring Using Machine Learning Algorithms on Google Earth Engine Platform and Cellular Automata Model: A Case Study of Raiganj Municipality, West Bengal, India
Since producing a reliable land use land cover map is complex and time-consuming, the introduction of Google Earth Engine (GEE) and the availability of enormous volumes of Geosciences and Remote Sensing information provide a possibility for spatiotemporal monitoring of changing earth surface. The aim of this study is to utilise machine learning (random forest) on the Google Earth Engine framework with earth observation data to analyse land use land cover change in the Raiganj municipality. The research also uses a logistic regression-cellular automata model to evaluate the potential land use land cover changes by 2025. The findings of the study demonstrate that between 1990 and 2000, the study area experienced 1.87 km2 of urban expansion at an annual rate of 8.68%. The five-year land use land cover change study revealed that urban expansion was recorded at 59.88% from 1990 to 1995, followed by 2010–2015 (28.26%). With an average annual growth rate of 1.8% (0.41 sq. km), the lowest urban expansion was seen between 2005 and 2010. In Raiganj municipality, the majority of urban expansion and growth occurs in the southwest direction. According to the predicted land use land cover map for 2025, about 5.06% of the study area will be urbanised in the upcoming five years and urbanisation will spread in the northeastern part of the study region. The results highlight the requirement of monitoring land use land cover change and assisting policymakers in implementing policies to limit haphazard urbanisation and avoid human–environment conflict in the study region.
Urban Expansion Monitoring Using Machine Learning Algorithms on Google Earth Engine Platform and Cellular Automata Model: A Case Study of Raiganj Municipality, West Bengal, India
Since producing a reliable land use land cover map is complex and time-consuming, the introduction of Google Earth Engine (GEE) and the availability of enormous volumes of Geosciences and Remote Sensing information provide a possibility for spatiotemporal monitoring of changing earth surface. The aim of this study is to utilise machine learning (random forest) on the Google Earth Engine framework with earth observation data to analyse land use land cover change in the Raiganj municipality. The research also uses a logistic regression-cellular automata model to evaluate the potential land use land cover changes by 2025. The findings of the study demonstrate that between 1990 and 2000, the study area experienced 1.87 km2 of urban expansion at an annual rate of 8.68%. The five-year land use land cover change study revealed that urban expansion was recorded at 59.88% from 1990 to 1995, followed by 2010–2015 (28.26%). With an average annual growth rate of 1.8% (0.41 sq. km), the lowest urban expansion was seen between 2005 and 2010. In Raiganj municipality, the majority of urban expansion and growth occurs in the southwest direction. According to the predicted land use land cover map for 2025, about 5.06% of the study area will be urbanised in the upcoming five years and urbanisation will spread in the northeastern part of the study region. The results highlight the requirement of monitoring land use land cover change and assisting policymakers in implementing policies to limit haphazard urbanisation and avoid human–environment conflict in the study region.
Urban Expansion Monitoring Using Machine Learning Algorithms on Google Earth Engine Platform and Cellular Automata Model: A Case Study of Raiganj Municipality, West Bengal, India
GIScience & Geo-environmental Modelling
Rahman, Atiqur (editor) / Sen Roy, Shouraseni (editor) / Talukdar, Swapan (editor) / Shahfahad (editor) / Saha, Sunil (author) / Sarkar, Debabrata (author) / Mondal, Prolay (author)
2023-03-04
13 pages
Article/Chapter (Book)
Electronic Resource
English
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