abc was researching and developing innovative tools and technologies that would ensure buildings of all scales contribute to a reduction in carbon emissions and a more sustainable built environment. the abc research program, which was funded by an epsrc grant, brought together a consortium of leading academics across a ten-university partnership. combining expertise from both the built-environment and energy sectors. our bit in focused on scalable identification and characterization of existing buildings for retrofit.
my time on abc contributed towards a few papers.
References
papers
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Component-Level Residential Building Material Stock Characterization Using Computer Vision Techniques
Menglin Dai, Jakub Jurczyk, Hadi Arbabi, Ruichang Mao,
Wil Ward,
Martin Mayfield, Gang Liu, and Danielle Densley Tingley
Environmental Science & Technology, Feb 2024
Residential building material stock constitutes a significant part of the built environment, providing crucial shelter and habitat services. The hypothesis concerning stock mass and composition has garnered considerable attention over the past decade. While previous research has mainly focused on the spatial analysis of building masses, it often neglected the component-level stock analysis or where heavy labor cost for onsite survey is required. This paper presents a novel approach for efficient component-level residential building stock accounting in the United Kingdom, utilizing drive-by street view images and building footprint data. We assessed four major construction materials: brick, stone, mortar, and glass. Compared to traditional approaches that utilize surveyed material intensity data, the developed method employs automatically extracted physical dimensions of building components incorporating predicted material types to calculate material mass. This not only improves efficiency but also enhances accuracy in managing the heterogeneity of building structures. The results revealed error rates of 5 and 22% for mortar and glass mass estimations and 8 and 7% for brick and stone mass estimations, with known wall types. These findings represent significant advancements in building material stock characterization and suggest that our approach has considerable potential for further research and practical applications. Especially, our method establishes a basis for evaluating the potential of component-level material reuse, serving the objectives of a circular economy.
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Estimating Energy Consumption of Residential Buildings at Scale with Drive-by Image Capture
Building and Environment, Mar 2023
Data driven approaches to addressing climate change are increasingly becoming a necessary solution to deal with the scope and scale of interventions required to reach net zero. In the UK, housing contributes to over 30% of the national energy consumption, and a massive rollout of retrofit is needed to meet government targets for net zero by 2050. This paper introduces a modular framework for quantifying building features using drive-by image capture and utilising them to estimate energy consumption. The framework is demonstrated on a case study of houses in a UK neighbourhood, showing that it can perform comparatively with gold standard datasets. The paper reflects on the modularity of the proposed framework, potential extensions and applications, and highlights the need for robust data collection in the pursuit of efficient, large-scale interventions.
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Towards an Automated Workflow for Large-Scale Housing Retrofit
Environmental Research Letters, Mar 2023
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Scalable Residential Building Geometry Characterisation Using Vehicle-Mounted Camera System
Energies, Mar 2022
Residential buildings are an important sector in the urban environment as they provide essential dwelling space, but they are also responsible for a significant share of final energy consumption. In addition, residential buildings that were built with outdated standards usually face difficulty meeting current energy performance standards. The situation is especially common in Europe, as 35% of buildings were built over fifty years ago. Building retrofitting techniques provide a choice to improve building energy efficiency while maintaining the usable main structures, as opposed to demolition. The retrofit assessment requires the building stock information, including energy demand and material compositions. Therefore, understanding the building stock at scale becomes a critical demand. A significant piece of information is the building geometry, which is essential in building energy modelling and stock analysis. In this investigation, an approach has been developed to automatically measure building dimensions from remote sensing data. The approach is built on a combination of unsupervised machine learning algorithms, including K-means++, DBSCAN and RANSAC. This work is also the first attempt at using a vehicle-mounted data-capturing system to collect data as the input to characterise building geometry. The developed approach is tested on an automatically built and labelled point cloud model dataset of residential buildings and shows capability in acquiring comprehensive geometry information while keeping a high level of accuracy when processing an intact model.
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Mapping Resource Effectiveness across Urban Systems
npj Urban Sustainability, Mar 2021
Cities and their growing resource demands threaten global resource security. This study identifies the hotspots of imports in cities to redirect resources to where they are most needed, based on the system overall resource effectiveness to maximise the use of all resources available. This paper develops a taxonomy of resource-use behaviour based on the clustering patterns of resource utilisation and conversion across interconnected urban systems. We find high tendencies of consumer-like behaviour in a multi-city system because tertiary sectors are concentrated in urban areas while the producing sectors are located outside and hence, results in high utilisation but low output. The clustering taxonomy emphasises that the absence of producers in the system causes cities to rely on the imported resources for growth. Cities can be resource-effective by having a more diversified industrial structure to extend the pathways of resource flows, closing the circularity gap between the suppliers and consumers.
confs
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Deep Multimodal Learning for Residential Building Energy Prediction
In IOP Conference Series: Earth and Environmental Science, Mar 2022
The residential sector has become the second-largest energy consumer since 1987 in the UK. Approximately 24 million existing dwellings in England made up over 32% of the overall energy consumption in 2020. A robust understanding of existing buildings’ energy performance is therefore critical in guiding proper home retrofit measures to accelerate towards meeting the UK’s climate targets. A substantial number of predictions at a city scale rely on available data, e.g., Energy Performance Certificates (EPCs) and GIS products, to develop statistical and machine learning models to estimate energy consumption. However, issues with existing data are not negligible. This work adopted the idea of deep multimodal learning to study the potential for using Google Street View (GSV) images as an additional input for residential building energy prediction. 20,031 GSV images of 5,933 residential buildings in central Barnsley, UK, have been selected for a case study. All images were pre-processed using a state-of-the-art object detection algorithm to minimise the noise caused by other elements that may appear nearby. Building specifications that cannot be easily determined by the appearance are extracted from existing EPC information as text-based inputs for prediction. A multimodal model was designed to jointly take images and texts as inputs. These inputs are first propagated through a convolutional neural network and multi-layer perceptron, respectively, before being combined into a connected network for final energy prediction. The multi-input model was trained and tested on the case study area and predicted an annual energy consumption with a mean absolute difference of 0.01kWh/m2 per annum on average compared with what is recorded in the EPC. The difference between the predicted results and the EPC may also provide some hints on the bias the certificates potentially contain.
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Measuring the Cityscape: A Pipeline from Street-Level Capture to Urban Quantification
In IOP Conference Series: Earth and Environmental Science, Mar 2022
Any solution to achieving climate targets must be performed at scale. Data driven methods allow expert modelling to be emulated over a large scope. In the UK, there are nearly 30 million residential properties, contributing to over 30% of the national energy consumption. As part of the UK Government’s requirement to meet net-zero emissions by 2050, retrofitting residential buildings forms a significant part of the national strategy. This work addresses the problem of identifying, characterising and quantifying urban features at scale. A pipeline incorporating photogrammetry, automatic labelling using machine learning, and 3-D geometry has been developed to automatically reconstruct and extract dimensional and spatial features of a building from street-level mobile sensing.