Life Expectancy: Insights from Data Analysis

This research examines the application of transfer learning techniques to improve the accuracy of land-use classification in metropolitan areas. Traditional remote sensing methods usually fail to accurately classify complex urban land cover patterns. The current study exploits pre-trained models and fine-tuning strategies to improve the performance of LSTM and DNNs for LAC. Utilizing transfer learning, the models can learn effectively from large datasets and adapt to the complexities of urban environments even with limited labeled data. The study demonstrates the effectiveness of this approach using the UC-Merced_LandUse dataset, with heatmap analysis providing insights into the models' classification decisions. This research contributes to the advancement of land-use classification techniques, enabling more accurate and informed urban planning and environmental management.