Machine Learning Models for the Analysis of Multi Temporal Satellite Images for Change Identification and Predictions

Satellite data are used to monitor certain fields such as
deforestation, polar ice loss, and climate change. The rate and
extent of forest loss and ice melt, as well as global climate change
trends, may all be tracked. Multi-temporal images are satellite
photos or aerial photographs acquired by a sensor at different
times but corresponding to the same location or area covered by
that sensor. Change detection and prediction is essential for
detecting changes in aerial images, environmental changes, and
determining land utilization and coverage. Neural networks are
robust Machine Learning(ML) models that can learn from data
and perform many tasks, including image identification, natural
language processing, and speech synthesis. Most neural networks
lack the ability to adapt to new data inputs that change over time.
The proposed study employs an Artificial Neural
Networks(ANN) that adapts to the dynamic and unpredictable
satellite image detection and prediction.