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

Michael Wurm,Ariane Droin,Wolfgang Sulzer,Thomas Stark,Christian Geiß,Hannes Taubenböck

doi:10.3390/ijgi10010023

Abstract

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 characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario.

Highlights

With increasing urbanization, cities represent an important component for ensuring a sustainable future for our planet
Non-residential buildings are much more heterogeneous in terms of thermal behavior and more difficult to model without specific knowledge; Year of construction: Many thermal regulations have been introduced over the past decades, making newly constructed or refurbished buildings more efficient in their energy behavior than buildings in their original state [24]
After the trained U-net Inceptionresnetv2 was applied on all 93,417 patches of the aerial image, each of the patches with the dimensions of 224 × 224 pixels was segmented into a binary classification: Buildings were labeled with ‘value 1’ and background was labeled with ‘value 0’

Summary

Introduction

Cities represent an important component for ensuring a sustainable future for our planet. The efficiency of building standards has increased significantly over the past decades, making modern constructions very energy-efficient; older buildings constructed before the turn of the millennium do not meet current thermal standards [4]. In addition to simulation tools for modeling building energy [8], the development of a dataset on the existing building stock model (BSM) is an important task for UBEM [7]. Besides the modeling of current energy demands of buildings at a city scale, BSMs are most helpful for retrofit analyses [10,17]. Non-residential buildings are much more heterogeneous in terms of thermal behavior and more difficult to model without specific knowledge; Year of construction: Many thermal regulations have been introduced over the past decades, making newly constructed or refurbished buildings more efficient in their energy behavior than buildings in their original state [24]

Results

Discussion

Conclusion