U-Net Semantic Segmentation of Digital Maps Using Google Satellite Images

Abstract

Satellite images contain an enormous data warehouse and give us details to the general perspective of what is happening on the earth’s surface. These images are essential for agricultural development research, urban planning, surveying and, especially for evaluating the location design of broadcast stations, the input of coverage simulation and signal quality in telecommunications. The analysis of large amounts of complex satellite imagery is challenging while the evolving semantic segmentation approaches based on convolution neural network (CNN) can assist in analyzing this amount of data. In this paper, we introduce an approach for constructing digital maps with dataset provided by Google. We utilize the efficient U-Net architecture, which is an efficient combination of EfficientNet, namely EfficientNet-B0 as the encoder to extract the geographic features with U-Net as decoder to reconstruct the detailed features map. We evaluate our models using Google satellite images which demonstrate the efficiency in terms of Dice Loss and Categorical Cross-Entropy.