Super-Resolution and Context Improve CNNs for Burn Detection

The annual impact of wildfires is enormous and is likely to worsen due to climate change.nbsp; Mapping the destruction wrought by these fires offers insight that can aid fire and forest management, climate and carbon cycle research, and even aid private and corporate risk-tolerance analysis.nbsp; Satellite imagery of the Earth is captured regularly, providing a vast wealth of detailed information that greatly benefits from automated analysis and has particular utility for burned area detection and mapping.nbsp; Existing state-of-the-art burned area algorithms are accurate but slow, involving region-specific models and human-in-the-loop quality assessments.Meanwhile, a powerful array of tools and techniques have been developed in the deep-learning community for semantic segmentation and convolutional neural networks (CNNs) have become increasingly capable.nbsp; However, benchmark datasets are not generally composed of remote sensing imagery and there are important considerations to adapting state-of-the-art techniques to this data.To address this need and fully leverage the available data, Toyon will implement a core Feature Extraction Module (FEM) that incorporates a modern suite of best practices in CNN architecture and design, transfer learning, and remote sensing imagery analysis.nbsp; Additionally, Toyon will implement 2 enhancement modules: A pre-processing Super-Resolution Module (SRM) and a post-processing Contextual Representation Module (CRM).nbsp; The SRM is composed of a CNN that produces high-resolution auxiliary outputs for human viewing but is trained with the core segmentation model to enhance the latterrsquo;s performance.nbsp; The CRM uses candidate semantic segmentations to refine and augment the FEM-derived features.nbsp; Both enhancement modules are entirely general and can be used toward a variety of other remote sensing tasks. Toyon will combine the 3 modules to produce a sophisticated burned-area detector that can automatically process satellite imagery on an ongoing basis.