To extend the proposed framework for handling more complex underground environments, several strategies can be implemented. First, the integration of advanced machine learning techniques, such as multi-task learning, can be employed to simultaneously process data from different layers and soil types. This would involve training the ParNet and GPRNet models on diverse datasets that include various underground structures and soil compositions, allowing the models to learn the unique characteristics of each layer and material.
Additionally, incorporating a hierarchical approach to data processing can enhance the framework's ability to manage multiple layers. By segmenting the GPR data into distinct layers based on their electromagnetic properties, the system can apply specialized algorithms tailored to each layer's specific challenges. For instance, different parabola fitting techniques could be utilized for layers with varying soil compositions, improving the accuracy of object detection and localization.
Furthermore, the framework could benefit from the use of synthetic data generation techniques, such as those provided by gprMax, to create realistic simulations of complex underground environments. This would allow for extensive training and validation of the models under various conditions, ensuring robustness against real-world variability.
Lastly, integrating additional contextual information, such as geological surveys or historical data about the area, can provide valuable insights into the expected underground structures, further enhancing the framework's predictive capabilities.