To use SamGeo 3, install it as:
	pip install segment-geospatial[samgeo3]
=== versions ===
python 3.12.0 | segment-geospatial 1.4.2 | segment-anything 1.0 | torch 2.14.1+cpu | rasterio 1.5.2 | geopandas 1.2.0 | osmnx 2.1.1
device: CPU (8 threads), CUDA available: False

=== NAIP imagery ===
2 NAIP tiles from 2023 overlap the patch, e.g. ia_m_4209359_sw_15_030_20230902_20240104
flown 02 Sep 2023 | 0.3 m | EPSG:26915 | bands: red, green, blue, near-infrared
mosaic of 2 tiles; empty pixels: 0
patch: 1024 x 1024 pixels at 0.5 m = 512 x 512 m, resampled from 0.3 m; vegetated (NDVI > 0.2): 18%

=== OpenStreetMap buildings ===
146 building footprints entirely inside the patch; area median 212 m², range 7-6,449 m²
building
house                 121
semidetached_house     14
apartments              9
school                  1
yes                     1

=== automatic mask generation (SAM ViT-B, CPU) ===
model loaded in 0.5 s: sam_vit_b_01ec64.pth, 375 MB, 94 M parameters
generate(): 306 masks in 47 s
vectorised to 306 polygons in sam_masks.gpkg; 34% of the patch is inside a mask
       area_m2  compactness  rectangularity  elongation    ndvi
count   306.00       306.00          306.00      306.00  306.00
10%      53.88         0.26            0.51        1.05   -0.10
50%     198.12         0.55            0.77        1.23   -0.01
90%     507.62         0.63            0.88        2.08    0.30

=== filter: building-like segments ===
  + 40 m² <= area <= 12,000 m²     ->  284 segments left
  + NDVI < 0.15 (not vegetation)   ->  211 segments left
  + rectangularity >= 0.75         ->  142 segments left
  + elongation <= 4 (not a road)   ->  139 segments left
precision: 81 of 139 kept segments match an OSM building with IoU >= 0.5 (58%)
recall before filtering: 107 of 146 OSM buildings have a SAM mask with IoU >= 0.5 (73%)
recall after filtering:  81 of 146 (55%); median IoU of matched buildings 0.73
recall after filtering, buildings < 100 m² (garages, sheds): 0% of 1; >= 100 m²: 56% of 145

=== point prompts ===
set_image(): image embedding computed in 3.9 s (model load included)
school roof        96 ms | 3 candidate masks: 5,698 m² (score 0.74), 1,320 m² (score 0.87), 637 m² (score 0.95)
playing field      34 ms | 3 candidate masks: 55,036 m² (score 0.63), 11,163 m² (score 0.72), 5,292 m² (score 0.95)
playground         33 ms | 3 candidate masks: 3,664 m² (score 0.70), 1,328 m² (score 0.90), 627 m² (score 0.62)
apartment block    31 ms | 3 candidate masks: 2,237 m² (score 0.69), 1,394 m² (score 0.69), 275 m² (score 0.81)
best mask for the school point written to school_point_mask.tif

=== box prompts from OpenStreetMap footprints ===
146 box prompts in 4.0 s (28 ms per box)
IoU with the OSM footprint: median 0.71, mean 0.71; >= 0.5: 99%, >= 0.75: 28%
          buildings  median_iou
area_m2                        
<50               1        0.19
100-300         131        0.70
300-1000          8        0.80
>1000             6        0.84
wrote 146 traced building polygons to sam_box_buildings.gpkg

figures and outputs written to output/