To extend the California Wildfire GeoImaging Dataset (CWGID) to encompass other types of forest disturbances, such as insect infestations or deforestation, several strategic steps can be undertaken.
Data Acquisition: Similar to the methodology used for wildfires, researchers can gather authoritative datasets that document instances of insect infestations and deforestation. This could involve sourcing data from forestry agencies, ecological studies, and remote sensing databases that provide information on affected areas.
Defining Disturbance Polygons: For each type of disturbance, polygons representing the affected areas can be created. For insect infestations, this may involve identifying areas with significant tree mortality or health decline, while for deforestation, polygons would represent areas where tree cover has been lost.
Bi-Temporal Imagery: The dataset can be expanded to include bi-temporal satellite imagery for these disturbances. For instance, images before and after an insect infestation or deforestation event can be collected to analyze changes in forest cover and health.
Labeling and Ground Truth Creation: Ground truth masks would need to be generated for these new disturbances, similar to the wildfire masks. This involves creating binary masks that indicate the presence or absence of the disturbance, allowing for effective training of deep learning models.
Integration with Existing Data: The new datasets can be integrated with the existing CWGID framework, allowing for a comprehensive analysis of multiple forest disturbances. This integration would facilitate comparative studies and enhance the dataset's utility for various research applications.
Utilizing Advanced Deep Learning Techniques: By employing advanced deep learning architectures, such as Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs), the extended dataset can be used to train models that detect and classify different types of forest disturbances, improving monitoring and management strategies.