Satellite images provide valuable information about the Earth’s surface aspects, such as land use/cover, environmental conditions, and changes over time. However, accurately interpreting and extracting meaningful information from these images can be challenging due to their complex and dynamic nature. Machine learning techniques have shown greaJt potential in analyzing and modeling spatially and temporally variable data, including satellite imagery. These algorithms can capture spatial, temporal, and spatio-temporal dependencies through automatic feature representation learning. By leveraging machine learning algorithms, researchers can develop models that can effectively classify and predict various features and phenomena in satellite images. In this study, we focus on classifying geospatial data from satellite imagery using a machine learning approach, such as Support Vector Machine, Naive Bayes, and Decision Tree, on the vegetation objects around UII.
“The full article can be seen at the following link Harnessing Machine Learning“
