Online Incremental Machine Learning Model For Multi-Sensor and Multi-Temporal Satellite Data: A Case Study of Bangalore

Once a machine learning model has been trained and deployed, training it again on new and different data is time and compute expensive. In this research, we propose a novel approach that enables machine learning models to continue learning on multi-sensor and multi-temporal data. A comparative analysis between traditional learning and incremental learning approaches, focusing on their performances and effectiveness in handling large and evolving datasets is presented in the context of land use classification. The online incremental approach effectively integrated information from multiple sources and time periods, enhancing the overall predictive power and accuracy of the models. Over single and multiple years, incremental learning performed better as compared to traditional learning.