论文标题
地图修复：深层地图地图对准和时间不一致在卫星图像中修复
Map-Repair: Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
论文作者
论文摘要
在迅速发展的国家，很难追踪新的建筑物建设或旧结构破坏，从而保留最新的卡达斯特地图。此外，由于城市区域的复杂性或用于开发地图提取的数据的不一致性，因此未对准形式的错误是一个常见的问题。在这项工作中，我们提出了一种端到端的深度学习方法，该方法能够通过纠正标签噪声来解决输入强度图像和可用建筑物足迹之间的不一致之处，并同时在需要时进行未对准。获得的结果证明了所提出的方法的鲁棒性，即使严重未对准的示例，这可能使其可能适合实际应用，例如OpenStreetMap校正。
In the fast developing countries it is hard to trace new buildings construction or old structures destruction and, as a result, to keep the up-to-date cadastre maps. Moreover, due to the complexity of urban regions or inconsistency of data used for cadastre maps extraction, the errors in form of misalignment is a common problem. In this work, we propose an end-to-end deep learning approach which is able to solve inconsistencies between the input intensity image and the available building footprints by correcting label noises and, at the same time, misalignments if needed. The obtained results demonstrate the robustness of the proposed method to even severely misaligned examples that makes it potentially suitable for real applications, like OpenStreetMap correction.
