Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling

Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristic...

Full description

Bibliographic Details
Main Authors: Michael Wurm, Ariane Droin, Thomas Stark, Christian Geiß, Wolfgang Sulzer, Hannes Taubenböck
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:ISPRS International Journal of Geo-Information
Subjects:
building stock model
building type
deep learning
heat demand modeling
digital surface model
aerial image
Online Access:https://www.mdpi.com/2220-9964/10/1/23

Similar Items

Similar Items