To extend the proposed scene change detection method to incorporate semantic understanding, one could integrate a semantic segmentation module alongside the existing change detection framework. This could involve the following steps:
Semantic Segmentation Backbone: Utilize a pre-trained semantic segmentation model, such as DeepLab or U-Net, to extract semantic features from the input images. This model would classify each pixel into predefined categories, providing a richer context for the detected changes.
Feature Fusion: After obtaining semantic features, these can be fused with the dense features extracted from the DINOv2 backbone. This fusion can be achieved through concatenation or attention mechanisms, allowing the model to leverage both the robust visual features and the semantic context.
Change Classification: Instead of merely detecting changes, the model could classify the type of change (e.g., appearance, disappearance, or modification of specific objects) by training a classifier on the combined features. This would enable the system to provide detailed insights into the nature of the changes, enhancing its utility in applications like urban planning and environmental monitoring.
Multi-task Learning: Implement a multi-task learning framework where the model simultaneously learns to detect changes and perform semantic segmentation. This approach can improve the model's performance on both tasks by sharing representations and gradients during training.
Contextual Information: Incorporate additional contextual information, such as temporal data or spatial relationships between objects, to further enhance the understanding of changes. This could involve using recurrent neural networks (RNNs) or graph-based methods to model relationships over time.
By integrating these components, the proposed method can evolve from a simple change detection system to a comprehensive tool capable of providing semantic insights into the changes detected, thereby improving its applicability in various domains.