Abstract
The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology.
Highlights
Nowadays the detection of building footprints and the extraction of road networks have become one the most important remote sensing tasks, since they are of paramount importance for countries to better understand the impacts of urban growing in different ecosystems
We have studied the advantages of fusing optical (S2) and Synthetic Aperture Radar (SAR) (S1) data for these tasks
Thereafter, a few simple modifications have been proposed to the U-Net architecture to enhance the resolution of the generated segmentation mappings
Summary
Nowadays the detection of building footprints and the extraction of road networks have become one the most important remote sensing tasks, since they are of paramount importance for countries to better understand the impacts of urban growing in different ecosystems. To date, these tasks have been mainly performed by human experts, occasionally assisted by semi-automatic tools, resulting in a very costly and time-consuming process. The scarcity of open high-resolution earth observation data is one of the main challenges for developing models that automatically generate fine-grained up-to-date building and road mappings. The ESA is developing seven Sentinel missions under the Copernicus Programme focusing on providing data
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