Batch Segmentation for Remote Sensing Imagery with SAM 3¶

This notebook demonstrates how to do batch segmentation for remote sensing imagery with SAM 3.

Installation¶

First, make sure you have the required dependencies installed:

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# %pip install "segment-geospatial[samgeo3]"

# %pip install "segment-geospatial[samgeo3]"

Import Libraries¶

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import leafmap
from samgeo import SamGeo3, download_file

import leafmap from samgeo import SamGeo3, download_file

Download Sample Data¶

Let's download sample satellite images covering the University of California, Berkeley, for testing:

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image_paths = []
for i in range(1, 5):
    url = f"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/uc_berkeley_{i}.tif"
    image_path = download_file(url)
    image_paths.append(image_path)

image_paths = [] for i in range(1, 5): url = f"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/uc_berkeley_{i}.tif" image_path = download_file(url) image_paths.append(image_path)

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m = leafmap.Map()
for i, image_path in enumerate(image_paths):
    m.add_raster(image_path, layer_name=f"image_{i + 1}")
m

m = leafmap.Map() for i, image_path in enumerate(image_paths): m.add_raster(image_path, layer_name=f"image_{i + 1}") m

Request access to SAM3¶

To use SAM3, you need to request access by filling out this form on Hugging Face: https://huggingface.co/facebook/sam3

Once you have access, uncomment the following code block and run it.

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# from huggingface_hub import login
# login()

# from huggingface_hub import login # login()

Initialize SAM3¶

When initializing SAM3, you can choose the backend from "meta", or "transformers".

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sam3 = SamGeo3(backend="meta", device=None, checkpoint_path=None, load_from_HF=True)

sam3 = SamGeo3(backend="meta", device=None, checkpoint_path=None, load_from_HF=True)

Set the image batch¶

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sam3.set_image_batch(image_paths)

sam3.set_image_batch(image_paths)

Generate masks with text prompt¶

Generate masks for all images with a text prompt

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sam3.generate_masks_batch("building", min_size=100)

sam3.generate_masks_batch("building", min_size=100)

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# Access results for each image
for i, result in enumerate(sam3.batch_results):
    print(f"Image {i + 1}: Found {len(result['masks'])} objects")
    # result contains: masks, boxes, scores, image, source

# Access results for each image for i, result in enumerate(sam3.batch_results): print(f"Image {i + 1}: Found {len(result['masks'])} objects") # result contains: masks, boxes, scores, image, source

Show results¶

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# Visualize all annotations in a grid
sam3.show_anns_batch(ncols=2, show_bbox=True, show_score=True)

# Visualize all annotations in a grid sam3.show_anns_batch(ncols=2, show_bbox=True, show_score=True)

Save results¶

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sam3.show_anns_batch(output_dir="output/annotations/", prefix="ann", dpi=300)

sam3.show_anns_batch(output_dir="output/annotations/", prefix="ann", dpi=300)

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# Save all masks to disk
saved_files = sam3.save_masks_batch(
    output_dir="output/", prefix="building_mask", unique=True
)

# Save all masks to disk saved_files = sam3.save_masks_batch( output_dir="output/", prefix="building_mask", unique=True )