Background
The classification of land use is crucial in terms of urban planning, management as well as environmental monitoring. There are many differences in the class characteristics of land-use types, and it is demanding to accurately recognize each. WPe can use spectral and spatial features which are inherent to the Sentinel-2 images for this purpose.
Objective
The system enables researchers to suggest and compare a novel method of the time-series land use classification from Sentinel-2 images that focuses on temporal adjustments in Katpadi evolving area under Vellore District for 2017-2024.
Problem Statement
However, traditional land use classification methods have certain limitations in recognizing various forms of land cover types aptly through time. However these methods frequently are plagued by problems of accuracy and efficiency, making them less suitable for dynamic environments.
Methodologies
Over time, we apply a deep learning ensemble network for land cover type classification. This method is the one that adds many of spectral and spatial features of Sentinel-2 images to their model. The ensemble derivative network is specialized to capture intricate relationships and patterns among different land cover classes by integrating multiple continuous information in the nested time-series data.
Results/Findings
Using a benchmark, we assessed our approach using actual Sentinel-2 timeseries data. The outcomes of the experiments suggest that the ensemble network that has been suggested can perform better than previous include/exclude models and conventional classification methods. The suggested approach outperforms the most advanced land use classification system currently in use, demonstrating that UDA has great potential as a remote sensing instrument.