HARNESSING AUTOMATED SATELLITE IMAGE CLASSIFICATION FOR COMPREHENSIVE ENVIRONMENTAL ASSESSMENT AND EARTH MONITORING
Keywords:
Random Forest, Image Processing, Landsat, Ensemble Learning, Urban planningAbstract
Monitoring and assessing land cover changes has relied on satellite photography. Since their launch in the early 1970s, Landsat satellites have given environmental monitoring and land use planning data. Urban planning, forestry, and environmental conservation require land cover change analysis. Traditional land cover change analysis used manual satellite images interpretation or rudimentary change detection techniques. These methods may work, but they are time-consuming, subjective, and may miss subtle or complex changes. The biggest challenge is creating a Landsat-based land cover change analysis system. This requires processing and interpreting vast amounts of information over time to identify land use and land cover changes and classify them. Sustainable land management and environmental conservation need land cover monitoring. This data is essential for urban development, natural resource management, and habitat preservation decisions. Advanced methods like ensemble learning improve land cover change analysis accuracy and dependability. The research, "Analyzing Land Cover Changes with Landsat Satellite Data: An Application to Ensemble Learning," uses advanced ensemble learning to revolutionize land cover change analysis. The collective intelligence of numerous models is used to construct a system that can autonomously and reliably identify and classify land cover changes. Ensemble learning methods handle complex data and improve prediction accuracy, making them ideal for this purpose. This breakthrough could improve environmental monitoring and land management by making Landsat satellite data land cover change analysis more dependable and accurate.