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

Xiangyu Zhuo, Friedrich Fraundorfer, Franz Kurz, Peter Reinartz

Research output: Contribution to journalArticleResearchpeer-review

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
Original languageEnglish
Article number624
JournalRemote Sensing
Volume10
Issue number4
DOIs
Publication statusPublished - 2018

Fingerprint

footprint
segmentation
ATKIS
image resolution
vehicle
pixel
imagery
information system
learning
remote sensing
modeling

Keywords

    Cite this

    Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images. / Zhuo, Xiangyu; Fraundorfer, Friedrich; Kurz, Franz; Reinartz, Peter.

    In: Remote Sensing , Vol. 10, No. 4, 624, 2018.

    Research output: Contribution to journalArticleResearchpeer-review

    @article{929e1994af914cf4b839682e7e18c17a,
    title = "Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images",
    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",
    keywords = "building footprint; oblique UAV images; semantic segmentation; deep neural network",
    author = "Xiangyu Zhuo and Friedrich Fraundorfer and Franz Kurz and Peter Reinartz",
    year = "2018",
    doi = "10.3390/rs10040624",
    language = "English",
    volume = "10",
    journal = "Remote Sensing",
    issn = "2072-4292",
    publisher = "MDPI AG",
    number = "4",

    }

    TY - JOUR

    T1 - Optimization of OpenStreetMap Building Footprints Based on Semantic Information of Oblique UAV Images

    AU - Zhuo, Xiangyu

    AU - Fraundorfer, Friedrich

    AU - Kurz, Franz

    AU - Reinartz, Peter

    PY - 2018

    Y1 - 2018

    N2 - 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

    AB - 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

    KW - building footprint; oblique UAV images; semantic segmentation; deep neural network

    U2 - 10.3390/rs10040624

    DO - 10.3390/rs10040624

    M3 - Article

    VL - 10

    JO - Remote Sensing

    JF - Remote Sensing

    SN - 2072-4292

    IS - 4

    M1 - 624

    ER -