Semantic segmentation of optical satellite images for the illegal construction detection using transfer learning

As urban populations grow, the demand for new infrastructure intensifies. However, some builders often neglect to obtain necessary permits, resulting in unstable structures that endanger public safety and disrupt city planning. Illegal construction poses significant challenges to economic development and social harmony. To tackle this issue, there is an urgent need for an automated illegal building monitoring system that can accurately identify and notify authorities about unlawful constructions. Current methods are often inefficient due to limited data access, inadequate analysis, and lack of automation. Recent technological advancements suggest that using satellite imagery alongside semantic segmentation is a highly effective approach for detecting illegal buildings. This process not only ensures the safety and compliance of structures but also enhances overall regulatory enforcement. This research distinguishes itself from previous studies by primarily focusing on illegal construction. It employs a diverse range of machine learning models, including the U-Shaped Network and Visual Geometry Group, and incorporates customized evaluation metrics that are not typically found in earlier research. Additionally, it utilizes real-world data, enhancing its practical relevance, and features IoT integration for real-time monitoring capabilities. We chose U-Net as our architecture due to its symmetrical design, which facilitates effective feature extraction. Its skip connections play a crucial role in preserving important features during the segmentation process. U-Net is known for its high accuracy and adaptability across various domains, making it versatile for our needs. Additionally, it performs well even with limited data, demonstrating robustness in diverse conditions. Furthermore, its compatibility with transfer learning enhances its applicability. For all these reasons, U-Net was selected as our model. Our experiments revealed that the Unet_mini model outperformed all others, demonstrating its effectiveness in addressing illegal construction challenges in urban areas. Qualitative results further illustrate the model's robustness, showing a significant reduction in false positives and improved accuracy in identifying illegal structures. This comprehensive approach not only improves detection rates but also contributes to more efficient regulatory enforcement, thereby promoting safer urban environments. This research distinguishes itself from previous studies by primarily focusing on illegal construction detection rather than general segmentation tasks. It employs a diverse range of machine learning models, including the UShaped Network and Visual Geometry Group, and integrates customized evaluation metrics that are not typically found in earlier research. Furthermore, it utilizes real-world data, enhancing its practical relevance, and incorporates IoT integration for real-time monitoring capabilities, setting it apart from existing studies.