Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection

TL;DR

This paper introduces a CNN-based method to automatically classify building roof shapes using satellite imagery and LIDAR data, enhancing urban navigation safety for small unmanned aircraft systems.

Contribution

It presents a novel approach combining CNNs and data fusion from satellite and LIDAR to improve roof shape classification accuracy.

Findings

Data fusion improves classification accuracy

CNN architectures effectively extract salient features

Automatic labeling from OpenStreetMap accelerates training data generation

Abstract

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.