Namespace:    mas-fuelmap

PI: Mai Nguyen
Institution: University of California, San Diego
Project description:

Land data products such as fuel maps and land cover maps are critical for many applications including land use analysis, bio-diversity conservation, and wildfire management. Current products provide essential land data and are widely used by various agencies across the nation. However, these products are generated very infrequently (e.g., every few years) and based on medium-resolution imagery that do not provide the granularity possible with high-resolution imagery.

Our research proposes an approach to generate products as needed, based on up-to-date imagery, and at scale. Specifically, our research goals are to generate more frequent products by creating maps as needed from satellite imagery, more accurate products by using up-to-date, high-resolution satellite imagery, and scalable products by utilizing machine learning to automate the process. The approach we use extracts features from satellite images using a deep learning model. The resulting feature vectors are then used for classifying or segmenting the images in order to generate land cover maps. We plan to extend our work to multi-spectral imagery, larger and more varied regions, and other types of land data products.

Software: Keras, Scikit-learn, Spark, PIL, GDAL

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