MAPPING SURFICIAL MATERIALS IN THE DELAWARE RIVER BASIN USING A DEEP LEARNING MODEL APPLIED TO HIGH-RESOLUTION LIDAR DATA
The advent of high-resolution (1-3 m) lidar and user-friendly deep learning tools has recently enabled more rapid, precise classification of surficial materials and landforms than previously possible. We applied these tools to the DRB to generate high-resolution preliminary surficial maps. We trained a resnet-18 deep learning model using a topographic position index-slope composite image and existing geologic/land use datasets from New Jersey, New York, and Pennsylvania (total shapefile area = 11,921 km2). The model was trained to identify 15 distinctive textures, ranging from alluvium and bedrock to urban zones and water bodies.
Preliminary results indicate that overall model performance is particularly effective for mapping areas of exposed bedrock (>90% accuracy) and urban development (>70% accuracy). More nuanced surficial classes, including boulder-dominated colluvium and Quaternary alluvium, were identified with >60% accuracy. Our model may be improved by additional, targeted training data and larger training image tiles. While no deep learning model – regardless of data resolution – can replace field investigations, this approach shows promise for rapidly generating useful preliminary surficial maps over large areas.