The proposed unsupervised framework for road scene parsing can be extended to other remote sensing applications by adapting the methodology to suit the specific requirements of the new application. For building extraction, the framework can be modified to focus on detecting building structures and differentiating them from the surrounding environment. This may involve training the model to identify specific features of buildings, such as edges, corners, and textures, and clustering them to generate pseudo-labels for segmentation. Additionally, incorporating additional pre-processing steps to enhance the detection of building structures, such as edge detection algorithms or texture analysis, can improve the accuracy of the segmentation.
For land cover classification, the framework can be adjusted to classify different types of land cover, such as vegetation, water bodies, and urban areas. By training the model to recognize the unique characteristics of each land cover type, such as color, texture, and spatial patterns, the framework can generate pseudo-labels for semantic segmentation. Utilizing satellite imagery or aerial photographs with high spatial resolution can provide detailed information about land cover types, enabling the model to accurately classify and segment different areas.
Overall, the key to extending the unsupervised framework to other remote sensing applications lies in customizing the methodology to suit the specific features and requirements of the new application, training the model on relevant data, and fine-tuning the parameters to optimize performance for the desired task.