However, DCNN require a heavy amount of human annotated data which is highly time consuming. For example, it takes 8 hours for a human GIS expert to generate pixel-wise annotation on a 10 000 x 10 000 pixels image. Also, the image distribution change along the years so a model trained on one year cannot generalize to overhead images taken decades before. For example, the images taken in 2015 are digital BGR images whereas the 1955 images are digitized black and white analog images taken in 1955. The latter present a change in color domain, texture and saturation that prevent the generalization of a model trained on the 2015 data.