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Semantic segmentation is in-demand in satellite imagery processing. Becauseof the complex environment, automatic categorization and segmentation of landcover is a challenging problem. Solving it can help to overcome many obstaclesin urban planning, environmental engineering or natural landscape monitoring.In this paper, we propose an approach for automatic multi-class landsegmentation based on a fully convolutional neural network of feature pyramidnetwork (FPN) family. This network is consisted of pre-trained on ImageNetResnet50 encoder and neatly developed decoder. Based on validation results,leaderboard score and our own experience this network shows reliable resultsfor the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge.Moreover, this network moderately uses memory that allows using GTX 1080 or1080 TI video cards to perform whole training and makes pretty fastpredictions.

Feature Pyramid Network for Multi-Class Land Segmentation