In recent years, Canada has been experiencing an increased frequency of devastating wildfires. Especially in Western Canada, wildfires continue to occur with increased burn intensity and duration and have started to threaten urban areas. Thus, there is an urgent need to develop new methods for monitoring and assessing wildfire-related risks and hazards across broad geographic areas. These data are needed as inputs to modeling and forecasting systems that aim to protect the natural and built environments as well as public health and safety. In this context, the overarching long-term objective of this initiative is to develop a novel technology for assessment of wildfire-related risks and hazards through crowdsensing, which is a useful tool for obtaining information about various phenomena from large areas in nearly real time. We will develop a unique crowdsensing-based technology by employing crowdsourced image data from in-vehicle cameras analyzed with AI, computer vision, and image analysis with deep learning. We will study various phenomena including i) the exposure of critical infrastructure to wildfire by assessing the proximity of hazardous fuels to infrastructures and ii) important seasonal indicators that mark the beginning and end of the spring fire season known for human-caused fires: the timing of spring snowmelt and leafing-out of trees. It is expected that the framework will serve as a valuable tool for urban planning and fire management and will aid in the development of fire management plans by validating and enhancing observational data currently available from fixed sources such as fire lookout towers or fire weather stations.