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This tutorial outlines a practical GeoAI workflow for extracting building footprints from aerial imagery using specific deep learning architectures like U-Net and SAM. It details the end-to-end process of configuring geospatial environments, training segmentation models with sliding-window inference, and evaluating performance metrics such as IoU and F1 scores. The approach demonstrates how standard computer vision tools can be adapted for specialized remote sensing tasks to generate vector data from raster inputs.

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A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN

MarkTechPostAugust 2, 20264 min read

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 […] The post A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN appeared first on MarkTechPost.

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…

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