  Automation of objects labeling in aerial imagery is a computer vision task
with numerous practical applications. Fields like energy exploration require an
automated method to process a continuous stream of imagery on a daily basis. In
this paper we propose a pipeline to tackle this problem using a stack of
convolutional neural networks (U-Net architecture) arranged end-to-end. Each
network works as post-processor to the previous one. Our model outperforms
current state-of-the-art on two different datasets: Inria Aerial Image Labeling
dataset and Massachusetts Buildings dataset each with different characteristics
such as spatial resolution, object shapes and scales. Moreover, we
experimentally validate computation time savings by processing sub-sampled
images and later upsampling pixelwise labeling. These savings come at a
negligible degradation in segmentation quality. Though the conducted
experiments in this paper cover only aerial imagery, the technique presented is
general and can handle other types of images.
