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Fine-Grained Road Mining from Satellite Images with Bilateral Xception and DeepLab

With the recent development of remote sensing and deep learning techniques, automatic and robust road extraction from satellite imaging data has become one of the most popular topics in both fields of Geographic Information System (GIS) and Computer Vision. Despite of the superior performance of Convolutional Neural Networks (DCNNs), a common problem of choosing between the classification and segmentation DCNNs still remains. By comparing two state-of-the-art baseline classification/segmentation DCNNs in several industrial application scenarios, we illustrate that their relative performance may vary, leading to different choices. Based on that observation, we propose a general fusion strategy that conveniently combines the strength of both classification and segmentation DCNNs using an end-to-end network architecture; this paradigm only requires pre-train segmentation/classification DCNNs once, which then can be reused in different road feature mining tasks. The task-specific experiments show that our fusion strategy guarantees superior results in all tested industrial scenarios.