  Large scale datasets created from user labels or openly available data have
become crucial to provide training data for large scale learning algorithms.
While these datasets are easier to acquire, the data are frequently noisy and
unreliable, which is motivating research on weakly supervised learning
techniques. In this paper we propose an iterative learning method that extracts
the useful information from a large scale change detection dataset generated
from open vector data to train a fully convolutional network which surpasses
the performance obtained by naive supervised learning. We also propose the
guided anisotropic diffusion algorithm, which improves semantic segmentation
results using the input images as guides to perform edge preserving filtering,
and is used in conjunction with the iterative training method to improve
results.
