-4d90550d17f4602e08910f6 20220624033857132 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 07 30 2022 11 8 Research Scholar, Department of Computer Engineering, Vidyalankar Institute of Technology (VIT), Mumbai (Maharashtra), India. Varsha Bhosale Dr. Archana Patankar Professor, Department of Computer Engineering at Thadomal Shahani College of Engineering, Mumbai (Maharashtra), India. Several geospatial applications use Classification. Classification is useful in identifying change detection. The Changes amongst various classes are identified at different time periods thus helping to analyze the changes happening in Land Use Land cover (LULC) of the area under consideration over a period. Neural networks have shown its command in majority of fields in solving complex problems, and geospatial field is also benefited by the Neural Networks. Several effective and efficient mechanisms are suggested for supervised satellite image classification. The Neural network’s Machine Learning algorithms are gaining popularity for supervised satellite image classification. The objective of this paper is to show how the Convolution Neural Network (CNN) as a machine learning algorithm is implemented for classification of Satellite image. The study area considered is Mumbai Metropolitan region (MMR), the Financial Capital of India. The work was executed considering the Landsat 8 images. The images were obtained and processed in QGIS Open-Source Software; Machine Learning algorithm was developed using Python Scripting. The NN algorithm was effectively implemented, and results showed the competence of Neural Network in generating classification of Landsat 8 satellite Image using CNN. 07 30 2022 23 28 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.H9133.0711822 https://www.ijitee.org/portfolio-item/h91330711822/