=== versions ===
python 3.12.0 | torch 2.14.1+cpu | segmentation-models-pytorch 0.5.0 | rasterio 1.5.2 (GDAL 3.12.2) | geopandas 1.2.0 | shapely 2.2.0 | odc-stac 0.5.3

=== model ===
unet_resnet18_s2_nottingham.pt: U-Net / resnet18, 13 input channels, classes ['Building', 'Other built-up', 'Trees', 'Grass and crops', 'Water']

=== new area ===
leicester: 3000 x 3000 pixels of 10 m (30 km square); lon/lat bounds (-1.357, 52.497, -0.902, 52.773)
10 items, 10 dates, 10 at least 20% clear: 31 May, 05 Jun, 08 Jun, 13 Jun, 15 Jun, 23 Jun, 18 Jul, 20 Jul, 04 Aug, 12 Aug
s2_leicester_2021_cog.tif: 12 bands, 512 px internal tiles, overviews [2, 4, 8], 244 MB on disk

=== sliding-window inference ===
blended: 256 windows of 256 px with 64 px overlap in 16 s (0.57 Mpx/s, 57 km2/s)
naive:   144 windows, no overlap, in 6 s
naive and blended maps differ on 1.5% of pixels: 5.2% within 8 px of a naive tile edge, 1.0% elsewhere

=== Cloud Optimised GeoTIFFs ===
unet_classes_leicester_2021.tif: GTiff, uint8, tiles (512, 512), overviews [2, 4, 8], layout COG, 0.9 MB
unet_confidence_leicester_2021.tif: GTiff, uint8, tiles (512, 512), overviews [2, 4, 8], layout COG, 5.5 MB
mean confidence 0.89; pixels below 0.6: 9.2%

=== vectorise ===
sieved specks < 4 px: 0.5% of pixels changed
27,614 polygons in 3 s; vertices 1,271,480 -> 1,020,390 after simplify (20% fewer)
                 polygons  area_km2  median_m2
class                                         
Building            10243     18.27      700.0
Other built-up       3925    111.10     1400.0
Trees               11313    178.22     2100.0
Grass and crops      1813    587.12     4000.0
Water                 320      5.29     3200.0
saved unet_landcover_leicester_2021.gpkg (21.6 MB)

=== accuracy against the reference labels (236,886 Overture buildings, ESA WorldCover 2021) ===
                   IoU     F1  precision  recall  ref share  pred share
Building         0.218  0.358      0.438   0.303      0.034       0.024
Other built-up   0.578  0.732      0.671   0.805      0.101       0.122
Trees            0.644  0.783      0.779   0.788      0.195       0.197
Grass and crops  0.897  0.946      0.955   0.937      0.664       0.652
Water            0.759  0.863      0.812   0.921      0.005       0.006
overall accuracy 0.873, mean IoU 0.619 (Nottingham test blocks: mean IoU 0.643)
confusion matrix (rows: reference, columns: prediction, % of each reference class):
                 Building  Other built-up  Trees  Grass and crops  Water
Building             30.3            59.2    8.1              2.4    0.0
Other built-up        9.9            80.5    7.1              2.4    0.1
Trees                 1.3             6.2   78.8             13.3    0.3
Grass and crops       0.1             1.1    5.0             93.7    0.0
Water                 0.7             2.8    3.5              0.9   92.1

Overture buildings touched by a predicted building polygon, by footprint area (m2):
          buildings  share found
area_m2                         
<50           62257        0.073
50-100        68515        0.102
100-200       75698        0.135
200-500       22501        0.273
500-2000       6539        0.632
>2000          1376        0.877
all buildings: 14.0%; area-weighted: 38.6%
predicted building polygons that touch no Overture footprint: 740 of 10243 (4.8% of predicted building area)

figures written to output/