ToolsAugust 2, 2026via MarkTechPost
A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
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.
Key signals
- Combines 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)
The hook
Building footprint extraction just got a workflow: U-Net + SAM + Grounding DINO in production GeoAI.
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…