Deep Learning Based Land Use and Land Cover Classification Using Remote Sensing Imagery

In today’s world of urban development, it becomes excessively difficult to observe and monitor land use and land cover changes. Since the growth is relatively more rapid in the current age, this analysis is more crucial than ever. The current approach in place uses many techniques, such as satellite imagery, which are hard to practically apply and require strong technical skills. Common users find it difficult to apply the deep learning model outputs. This work proposes a ResNet-50-based system to acquire satellite imagery, followed by land use and land cover classification with a visualization dashboard, which also features a spatial and temporal comparison of the selected area. This makes it especially useful for common users to interpret the current usage of land and the change in land usage over time. The satellite imagery is obtained from the Sentinel-2 dataset that is acquired through the Google Earth Engine. These acquired images are passed through a custom convolutional neural network based on ResNet-50. The model is initially trained for ten classes of land usage based on the EuroSat dataset. This system also allows comparison of specific land areas over a range of time to understand the change in land usage patterns across different times. The user will be able to view a statistical summary of the selected region, and this report can be used to query an AI chatbot to understand and interpret the statistical results. This increases the usability of a niche system to a regular user who does not have an idea about the land usage patterns. This model achieves an accuracy of 95.11 percent, which makes it a reliable and consistent system that can be utilized in real-world decision making process. This can be used to decide whether a new project in one of these 10 classes can be accommodated or not based on the current land usage pattern in the given area.