Object Detection in Satellite Imagery for Land Change Monitoring

Abstract

This study aims to develop an object detection method using satellite imagery to monitor land use changes from 2010 to 2025. In this research, we applied Convolutional Neural Networks (CNN), a deep learning technique, to analyze land use changes, including urban expansion, agricultural land conversion, and deforestation. Satellite images from the Sentinel-2 and Landsat programs were used to detect these changes. The image processing steps involved geometric and radiometric correction, cloud removal, and image normalization to improve data quality. The results of the study showed that the developed CNN model achieved an overall accuracy of 92%, with high precision and recall rates for urban and agricultural land categories. The model also successfully detected land use changes with an accuracy of 90%, especially urban expansion with a recall rate of 95%. A comparison with traditional methods, such as pixel-based classification and thresholding, revealed that the CNN model outperformed these methods in terms of accuracy and precision. This research demonstrates that deep learning techniques, particularly CNNs, can be effectively used for automated land use monitoring using satellite imagery, providing valuable insights for urban planning, environmental monitoring, and natural resource management. However, challenges such as image resolution and cloud interference remain and should be addressed in future studies to enhance the accuracy of land use change detection.