  This paper describes a methodology to produce a 7-classes land cover map of
urban areas from very high resolution images and limited noisy labeled data.
The objective is to make a segmentation map of a large area (a french
department) with the following classes: asphalt, bare soil, building,
grassland, mineral material (permeable artificialized areas), forest and water
from 20cm aerial images and Digital Height Model. We created a training dataset
on a few areas of interest aggregating databases, semi-automatic
classification, and manual annotation to get a complete ground truth in each
class. A comparative study of different encoder-decoder architectures (U-Net,
U-Net with Resnet encoders, Deeplab v3+) is presented with different loss
functions. The final product is a highly valuable land cover map computed from
model predictions stitched together, binarized, and refined before
vectorization.
