Optimizing Land Use and Land Cover Mapping Through Dynamic Time Warping with Time-Weighted Analysis

This research study focuses on the precise classification of land objects in the region of Kanigiri, Andhra Pradesh, utilizing Sentinel-2 satellite data. The primary objective is to provide accurate information for large geographical areas, specifically targeting assessment of land cover types and land usage. Employing advanced techniques, including Time-Weighted Dynamic Time Warping (TWDTW), this study aims to detect and classify various land objects such as water bodies, vegetation areas, forests, urban areas, and bare land. By analyzing temporal patterns and dynamic changes in these land objects, this study enhances understanding of land use dynamics and facilitates informed decision-making in resource management, urban planning, and environmental conservation efforts. Leveraging the TWDTW method, the project ensures robust classification performance, particularly in capturing seasonal variations and subtle changes in land cover over time. The findings of the project contribute significantly to land use and land cover assessment, supporting sustainable development initiatives and promoting resilience in the Kanigiri region. Overall, the project underscores the importance of accurate land object classification for effective land use planning and management, aligning with its core aim of focusing solely on land cover types and land usage.