We describe an actual case of applying deep neural networks for area assessment of different types of objects in selected geographical region through analysis of satellite images. The case was to detect, segment and asses area of buildings and agricultural lands on satellite images. We illustrate our framework of solving the problem and results validation methods. We compare performance of different convolutional neural networks in applying to our case and discuss the best quality segmentation

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... odel that was found -the U-net convolutional network. There was no training dataset of images and their corresponding masks available for our geographical region, but we constructed our own training set. Paper reports in detail on the processes of satellite imagery data preparation, images pre-processing, construction of training dataset and learning neural networks.