A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
Building footprint extraction just got a workflow: U-Net + SAM + Grounding DINO in production GeoAI.
Why it matters
A practical tutorial showing how to combine multiple open vision models (U-Net, SAM, Mask R-CNN, Grounding DINO) into a complete geospatial ML pipeline — actionable for practitioners building computer vision systems on real satellite data.
The key facts
9 to knowCombines U-Net (segmentation), Grounding DINO (object detection), SAM (segment-anything), and Mask R-CNN
Workflow includes geospatial environment setup, NAIP aerial imagery ingestion, georeferenced chip generation, and mask training
ResNet-34 backbone used for U-Net model
End-to-end tutorial covering data prep through model training on satellite imagery
Published by MarkTechPost (educational/tutorial source)
Models covered: U-Net (ResNet-34 backbone), Grounding DINO, SAM, Mask R-CNN
Use case: Building footprint extraction from NAIP aerial imagery
Workflow includes: environment setup, data download, georeferencing, chip generation, model training
Tutorial format on MarkTechPost (vendor/educational content, not primary research or product launch)
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: 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…