An AI framework for Change Analysis and Forecast Modelling of Temporal Series of Satellite Images

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Deep Neural Networks model used.
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Published: May 17, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i5s.6648
Keywords:
Cellular Automata, Artificial Neural Networks, Deep Neural Networks, LULC

Main Article Content

Pallavi M.
Assistant Professor, dept. of CSE Presidency University Bangalore, India
Thivakaran T. K.
Professor, dept. of CSE Presidency University Bangalore, India
Chandankeri Ganapathi
Professor, dept. of CIVIL Presidency University Bangalore, India

Abstract

The study focuses on change analysis and predicting future LULC map of capital city of Karnataka state, India. The chosen study area is more prone to urbanisation and greatly affected by population in recent years. Spatial-temporal data from 1989-2019 are considered. LULC classes comprise of Water bodies, Urban, Forest, Vegetation and Openland. An optimal LULC maps from 1989 to 2019 obtained by deep neural network technique are used to perform change analysis which would mainly give the change LULC map with number and percentage of change pixels. According to the analysis performed major change as environmental affecting factor was noticed between 2009 and 2019 where in urban with the area of 189.3861 sq. km remain unchanged and noticeable transitions from other LULC classes to urban. Later, time series classification was performed using Cellular Automata, Cellular Automata-Neural Networks, techniques to predict the LULC map of 2024. Among these CA-NN outperformed with an average kappa coefficient of 0.83. Also, this was validated with projected LULC map of 2024 provided by USGS.

Article Details

How to Cite
M., P. ., T. K., T. ., & Ganapathi, C. . (2023). An AI framework for Change Analysis and Forecast Modelling of Temporal Series of Satellite Images. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 223–229. https://doi.org/10.17762/ijritcc.v11i5s.6648
Issue
Vol. 11 No. 5s (2023): Special Issue on Advances in Computing Systems for Web-Based Machine Learning Applications
Section
Articles

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