Learning Digital Terrain Models From Point Clouds: ALS2DTM Dataset and Rasterization-Based GAN

Learning Digital Terrain Models From Point Clouds: ALS2DTM Dataset and Rasterization-Based GAN

Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy bet...

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Bibliographic Details
Main Authors: Hoang-An Le, Florent Guiotte, Minh-Tan Pham, Sebastien Lefevre, Thomas Corpetti
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Airborne laser scanning (ALS) point cloud
dataset
deep networks
digital terrain model (DTM)
generative adversarial network (GAN)
rasterization
Online Access:https://ieeexplore.ieee.org/document/9794452/

Internet

https://ieeexplore.ieee.org/document/9794452/

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