Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images

Xiangyu Zhuo,Peter Reinartz,Friedrich Fraundorfer,Franz Kurz

doi:10.3390/rs10040624

Abstract

Building footprint information is vital for 3D building modeling. Traditionally, in remote sensing, building footprints are extracted and delineated from aerial imagery and/or LiDAR point cloud. Taking a different approach, this paper is dedicated to the optimization of OpenStreetMap (OSM) building footprints exploiting the contour information, which is derived from deep learning-based semantic segmentation of oblique images acquired by the Unmanned Aerial Vehicle (UAV). First, a simplified 3D building model of Level of Detail 1 (LoD 1) is initialized using the footprint information from OSM and the elevation information from Digital Surface Model (DSM). In parallel, a deep neural network for pixel-wise semantic image segmentation is trained in order to extract the building boundaries as contour evidence. Subsequently, an optimization integrating the contour evidence from multi-view images as a constraint results in a refined 3D building model with optimized footprints and height. Our method is leveraged to optimize OSM building footprints for four datasets with different building types, demonstrating robust performance for both individual buildings and multiple buildings regardless of image resolution. Finally, we compare our result with reference data from German Authority Topographic-Cartographic Information System (ATKIS). Quantitative and qualitative evaluations reveal that the original OSM building footprints have large offset, but can be significantly improved from meter level to decimeter level after optimization.

Highlights

OpenStreetMap (OSM) is a collaborative project for creating a free editable map of the world based on volunteered geographic information
The proposed workflow is comprised of the following steps: (1) geo-registration of oblique-view Unmanned Aerial Vehicle (UAV) images; (2) semantic segmentation of UAV images using a Fully Convolutional Network (FCN); and (3) optimization of the building model initialized from OSM footprints
We present a novel framework for optimizing OSM building footprints based on the contour information derived from deep learning-based semantic segmentation of UAV images

Summary

Introduction

OpenStreetMap (OSM) is a collaborative project for creating a free editable map of the world based on volunteered geographic information. It is able to provide free and updated geographic information despite restrictions on usage or availability of georeferenced data across most of the world. OSM has widely expanded its coverage and gained increasing popularity in many applications. One example is the generation of 3D building models from OSM building footprints [1]. The quality of building reconstruction and modeling strongly relies on the quality of building footprints. A detailed analysis for OSM building footprints [2] assessed a high completeness accuracy and a position accuracy of about 4 m on average for these data. OSM building footprints can be safely regarded as a rough approximation of the real scene

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