多直升机应急着陆场选择的屋顶障碍物自动分类
Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection
摘要：
地理信息系统（GIS）现在提供地形、道路、水道以及建筑物覆盖区和高度的精确地图。飞机，特别是小型无人驾驶飞机系统，可以利用建筑物屋顶结构等额外信息来提高导航精度和安全性，特别是在城市地区。本文提出了一种自动标注建筑屋顶形状类型的方法。来自德国Witten的卫星图像和LIDAR数据被馈送到卷积神经网络（CNN）以提取显著特征向量。监督训练集是从OpenStreetMap数据库中包含的预先标记的建筑物自动生成的。训练和测试多个CNN架构，其中性能最好的网络为支持向量机和决策树分类器提供浓缩特征集。卫星和激光雷达数据融合提供更大的分类精度比通过单独使用任何数据类型。
英文摘要：
Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
