ROOFN3D: DEEP LEARNING TRAINING DATA FOR 3D BUILDING RECONSTRUCTION

Wichmann, A.; Agoub, A.; Kada, M.

doi:https://doi.org/10.5194/isprs-archives-XLII-2-1191-2018

Articles | Volume XLII-2

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Articles | Volume XLII-2

https://doi.org/10.5194/isprs-archives-XLII-2-1191-2018

Articles | Volume XLII-2

Article
Metrics
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30 May 2018

| 30 May 2018

ROOFN3D: DEEP LEARNING TRAINING DATA FOR 3D BUILDING RECONSTRUCTION

A. Wichmann, A. Agoub, and M. Kada

A. Wichmann

Institute of Geodesy and Geoinformation Science (IGG), Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany

A. Agoub

Institute of Geodesy and Geoinformation Science (IGG), Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany

M. Kada

Institute of Geodesy and Geoinformation Science (IGG), Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany

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DEEP LEARNING FOR 3D BUILDING RECONSTRUCTION: A REVIEW

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DEEP LEARNING BASED BUILDING FOOTPRINT EXTRACTION FROM VERY HIGH RESOLUTION TRUE ORTHOPHOTOS AND NDSM

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A SWEEP-PLANE ALGORITHM FOR THE SIMPLIFICATION OF 3D BUILDING MODELS IN THE APPLICATION SCENARIO OF WIND SIMULATIONS

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Andreas Wichmann and Martin Kada

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ROOFN3D: DEEP LEARNING TRAINING DATA FOR 3D BUILDING RECONSTRUCTION

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