A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
In this tutorial, we design a complete GeoAI workflow for extracting building footprints from high-resolution NAIP aerial imagery. We begin by configuring the geospatial deep learning environment, downloading raster imagery and vector labels, and inspecting their spatial properties before generating georeferenced image chips and segmentation masks. We then train a U-Net model with a ResNet-34 encoder, evaluate its learning behavior, and apply sliding-window inference to an unseen scene. Beyond
Get smart on it
This tutorial demonstrates how to build a complete workflow for extracting building footprints from aerial imagery using multiple artificial intelligence models. The process involves configuring a geospatial deep learning environment, downloading aerial imagery and building location data, training a U-Net model with a specific encoder architecture, and comparing results from different segmentation approaches including zero-shot models and instance segmentation. The tutorial extends to real-world applications by incorporating imagery from Microsoft Planetary Computer and building labels from Overture Maps, showing how the same pipeline can be adapted for different geographic areas.
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