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Article

IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery

1
School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland
2
School of Mathematical Sciences, Dublin City University, Dublin 9, Ireland
3
Ordnance Survey Ireland, Dublin 8, Ireland
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(17), 2719; https://doi.org/10.3390/rs12172719
Received: 24 July 2020 / Revised: 14 August 2020 / Accepted: 19 August 2020 / Published: 22 August 2020
(This article belongs to the Special Issue 3D Urban Modeling by Fusion of Lidar Point Clouds and Optical Imagery)
Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial imagery is a challenging inherently ill-posed problem that we address in this paper by resorting to machine learning. We propose an end-to-end trainable convolutional-deconvolutional deep neural network architecture that enables learning mapping from a single aerial imagery to a DSM for analysis of urban scenes. We perform multisensor fusion of aerial optical and aerial light detection and ranging (Lidar) data to prepare the training data for our pipeline. The dataset quality is key to successful estimation performance. Typically, a substantial amount of misregistration artifacts are present due to georeferencing/projection errors, sensor calibration inaccuracies, and scene changes between acquisitions. To overcome these issues, we propose a registration procedure to improve Lidar and optical data alignment that relies on Mutual Information, followed by Hough transform-based validation step to adjust misregistered image patches. We validate our building height estimation model on a high-resolution dataset captured over central Dublin, Ireland: Lidar point cloud of 2015 and optical aerial images from 2017. These data allow us to validate the proposed registration procedure and perform 3D model reconstruction from single-view aerial imagery. We also report state-of-the-art performance of our proposed architecture on several popular DSM estimation datasets. View Full-Text
Keywords: building height estimation; digital surface model; optical aerial imagery; aerial Lidar; image coregistration; convolutional neural networks building height estimation; digital surface model; optical aerial imagery; aerial Lidar; image coregistration; convolutional neural networks
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MDPI and ACS Style

Liu, C.-J.; Krylov, V.A.; Kane, P.; Kavanagh, G.; Dahyot, R. IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery. Remote Sens. 2020, 12, 2719. https://doi.org/10.3390/rs12172719

AMA Style

Liu C-J, Krylov VA, Kane P, Kavanagh G, Dahyot R. IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery. Remote Sensing. 2020; 12(17):2719. https://doi.org/10.3390/rs12172719

Chicago/Turabian Style

Liu, Chao-Jung, Vladimir A. Krylov, Paul Kane, Geraldine Kavanagh, and Rozenn Dahyot. 2020. "IM2ELEVATION: Building Height Estimation from Single-View Aerial Imagery" Remote Sensing 12, no. 17: 2719. https://doi.org/10.3390/rs12172719

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