OpenStreetMap Boosting using Simulation-Based Remote Sensing Data Fusion (OSMSim)

本项目的主要科学研究课题是利用基于仿真的异构遥感数据融合改进OpenStreetMap （OSM）中的建筑信息（几何、属性），并将更新后的OSM数据用于后续应用。工作基础是模拟环境SimGeoI，通过利用遥感采集的元数据以及调查场景的可用几何先验知识，可以对不同传感器的成像过程进行建模。SimGeoI不仅允许对场景进行粗略的语义解释，还允许对相应的场景元素进行与对象相关的对齐。本项目利用SimGeoI将不同传感器配置、不同采集时间下产生的遥感数据与几何OSM信息进行对比，利用不同遥感数据融合得到的几何修正量（位置、高度）和属性（如建筑类型、屋顶结构）丰富OSM。该项目的科学研究主要针对三个核心主题：首先，将开发一个有条理的框架，允许使用一对遥感图像（光学，SAR或混合），基于建筑物形状的预测和比较，对OpenStreetMap数据进行几何校正。第二步，利用几何改进的OSM信息从多模态遥感数据中提取建筑相关属性。最后，将对所开发方法的可移植性进行实验分析，并对后续应用（开放事件映射、气候事件门户、虚拟现实）的接口进行研究。该方法将伴随着验证，以评估衍生结果的位置、主题和时间准确性。

The main subject of the scientific investigations in this project is the improvement of building information (geometry, attributes) in OpenStreetMap (OSM) using a simulation-based fusion of heterogeneous remote sensing data and to use the updated OSM data for follow-up applications. The working basis is the simulation environment SimGeoI, which enables the modeling of imaging processes of different sensors by exploiting the metadata of remote sensing acquisitions as well as available geometric prior knowledge of the scene under investigation. SimGeoI allows not only a coarse semantic interpretation of the scene, but also an object-related alignment of corresponding scene elements. In this project, SimGeoI is used to compare geometric OSM information with remote sensing data produced under different sensor configurations and at different acquisition times, and to enrich OSM with geometric corrections (position, height) and attributes (e.g. building type, roof structure) gained from a fusion of the different remote sensing data. The scientific investigations of this project aim at three core themes: First, a methodical framework will be developed, which allows the geometric correction of OpenStreetMap data based on the prediction and comparison of building shapes, using a pair of remote sensing images (optical, SAR or mixed). In a second step, geometrically improved OSM information will be used to extract building-related attributes from multi-modal remote sensing data. Finally, the transferability of the developed methods will be experimentally analyzed and interfaces to follow-up applications (Open Event Mapping, Climate Event Portal, Virtual Reality) will be investigated. The methodology will be accompanied by validation in order to evaluate the positional, thematic and temporal accuracy of derived results.