The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.

中文翻译：

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将更改检测从遥感更改为指导OpenStreetMap标签

越来越多的公开可用的，米级的地理空间垂直航空影像以及需要OpenStreetMap（OSM）项目进行连续更新的机会，带来了利用前者来帮助后者的机会，例如通过利用最新的遥感数据。结合最先进的计算机视觉方法，以协助OSM社区进行标签工作。本文报告了我们在利用人工神经网络（ANN）进行OSM数据变化检测以更新地图方面的进展。此外，我们旨在确定制图者需要集中精力完成全球OSM数据集的地理空间区域。我们的方法在技术上得到了大型地理空间数据平台物理分析集成存储库和服务（PAIRS）的支持。