While the IDAL-FIM framework shows promise in improving the interpretability and efficiency of deep active learning for flood inundation mapping, there are potential limitations to its applicability to other remote sensing tasks beyond flood mapping. Some of these limitations include:
Task-specific features: The class ambiguity indices and acquisition functions in the IDAL-FIM framework are tailored for flood mapping tasks and may not directly translate to other remote sensing applications with different characteristics and requirements. Adapting these components to suit the specific needs of other tasks may require significant modifications and validation.
Data diversity: The effectiveness of the framework relies on the availability of diverse and representative training data. For tasks with limited or biased training data, the performance of the framework may be compromised, leading to challenges in generalization and model robustness.
Model architecture: The choice of deep learning model and uncertainty estimation method in the framework may not be optimal for all remote sensing tasks. Different tasks may require specific model architectures and uncertainty measures, necessitating customization and experimentation to achieve optimal results.
Computational complexity: The iterative nature of deep active learning in the framework can be computationally intensive, especially for tasks with large datasets or complex data structures. This may pose challenges in scalability and efficiency for certain remote sensing applications.