Journal
REMOTE SENSING
Volume 13, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/rs13122417
Keywords
building height estimation; deep learning; digital surface model; aerial imagery; LiDAR; convolutional neural networks; remote sensing; digital elevation models
Categories
Funding
- European Union [739578]
- Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy
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The study introduces a deep learning model to estimate the height of buildings and vegetation in single aerial images, showing it outperforms other state-of-the-art DL approaches by training on aerial images with corresponding DSM and DTM.
Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.
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