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 In addition, residential buildings that were built with outdated standards usually face difficulty meeting current energy performance standards.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.

A growing body of evidence suggests that there is a strong association between neurodegenerative diseases such as Alzheimer's Diseases and the abnormality of the cerebral vasculature, in particular the microvessels/capillaries that are responsible for the exchange of nutrients across the blood-brain barrier [1]. Many microvessels are described as being kinked or distorted [2], implying that they are modified by some destructive process. Imaging devices such as microCT can achieve resolutions on the order of several μm, allowing imaging the three dimensional (3D) microvasculature down to the capillary level. However, the main weakness of using microCT for vascular research is considered to be the lack of software for 3D quantification of microvasculature and microvascular image databases for developing and testing algorithms. In this paper we describe a multifractal analysis method for the microvasculature automatically segmented from microCT images of the mouse brain. Due to the lack of a benchmark microCT image database, the method has been tested using a surrogate database - a publicly available retinal vessel database. The results are preliminary indication of the multifractal properties of mouse brain vasculature. A potential solution to automated classification of healthy and disease brains are discussed.

The transition to Net Zero rests partly on the widespread adoption of energy-efficient retrofit measures for domestic dwellings. The scale of retrofit efforts is extensive, as up to 80% of the UK’s domestic housing stock for 2050 has already been built. To address the scope, data and models will play a crucial role in informing design decisions and optimising retrofit strategies. While new methods and tools for data and modelling in retrofit continue to be developed, the perspectives of professionals using these tools on their quality remain mainly absent from discussion across academia and practice. This study investigated the experiences and perceptions of data and modelling from professionals working in the planning stages of domestic retrofit, serving as a needs-finding exercise driving retrofit planning. Through semi-structured interviews and qualitative coding, the results highlight a critical trade-off between precision, confidence, and the burden of data collection. These findings underscore the need to balance precision, ease of use, and adaptability in data and modelling retrofit tools. Issues around data availability and wider access to data and modelling results across stakeholders emerged as a missed opportunity.