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Detecting Floors in Residential Buildings
Abstract
Knowing the number of floors of all buildings in a city is vital in many areas of urban planning such as energy demand prediction, estimation of inhabitant numbers of specific buildings or the calculation of population densities. Also, novel augmented reality use cases strongly rely on exact numbers and positions of floors. However, in many cases floor numbers are unknown, its collection is mostly a manual process or existing data is not up-to-date. A major difficulty in automating floor counting lies in the architectural variety of buildings from different decades. So far approaches are only rough geometric approximations. More recently approaches apply neural networks to achieve more precise results. But, these neural network approaches rely on various sources of input that are not available to every municipality. They also tend to fail on building types they have not been trained on and existing approaches are completely black-box so that it is difficult to determine when and why the prediction is wrong.
In this paper we propose a grey-box approach. In a stepwise process we can predict floor counts with high quality and remain explainable and parametrizable. By using data that is easy to obtain, namely the image of a building, we introduce two configurable methods to derive the number of floors. We demonstrate that the correct prediction quality can be significantly improved. In a thorough evaluation we analyze the quality depending on a number of factors such as image quality or building types.
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The UK construction sector is central to the climate and housing crises and must now deliver vast amounts of residential accommodation whilst reaching net zero emissions by 2050. Housing provision through the vertical extension of existing buildings offers opportunity to achieve this, reducing embodied carbon emissions and creating more efficient high-density settlements. In England, permitted development (PD) rights allow for residential vertical extensions without the requirement for conventional planning permission. Despite this, and due to limited uptake of PD rights and a lack of existing studies, the potential for housing provision through widespread extension is unknown. This paper develops a framework to assess the ability of vertical extensions in providing housing at different scales and applies this to Sheffield, England. The generation of new dwellings through PD vertical extension could house up to 175,000 in Sheffield, with detached buildings and those in residential use being most suited to extension. PD rights favour the enlargement of existing dwellings over the generation of new residential units, potentially limiting their effectiveness in tackling the housing crisis.
Street view imagery has rapidly ascended as an important data source for geospatial data collection and urban analytics, deriving insights and supporting informed decisions. Such surge has been mainly catalysed by the proliferation of large-scale imagery platforms, advances in computer vision and machine learning, and availability of computing resources. We screened more than 600 recent papers to provide a comprehensive systematic review of the state of the art of how street-level imagery is currently used in studies pertaining to the built environment. The main findings are that: (i) street view imagery is now clearly an entrenched component of urban analytics and GIScience; (ii) most of the research relies on data from Google Street View; and (iii) it is used across myriads of domains with numerous applications – ranging from analysing vegetation and transportation to health and socio-economic studies. A notable trend is crowdsourced street view imagery, facilitated by services such as Mapillary and KartaView, in some cases furthering geographical coverage and temporal granularity, at a permissive licence.
The purpose of this paper is to provide structural and architectural technological solutions applied in the construction of high-rise buildings, and present the possibilities of technological evolution in this field. Tall buildings always have relied on technological innovations in engineering and scientific progress. New technological developments have been continuously taking place in the world. It is closely linked to the search for efficient construction materials that enable buildings to be constructed higher, faster and safer. This paper presents a survey of the main technological advancements on the example of selected tall buildings erected in the last decade, with an emphasis on geometrical form, the structural system, sophisticated damping systems, sustainability, etc. The famous architectural studios (e.g., for Skidmore, Owings and Merill, Nikhen Sekkei, RMJM, Atkins and WOHA) that specialize, among others, in the designing of skyscrapers have played a major role in the development of technological ideas and architectural forms for such extraordinary engineering structures. Among their completed projects, there are examples of high-rise buildings that set a precedent for future development.
In order for a risk assessment to deliver sensible results, exposure in the concerned area must be known or at least estimated in a reliable manner. Exposure estimation, though, may be tricky, especially in urban areas, where large-scale surveying is generally expensive and impractical; yet, it is in urban areas that most assets are at stake when a disaster strikes. Authoritative sources such as cadastral data and business records may not be readily accessible to private stakeholders such as insurance companies; airborne and especially satellite-based Earth-Observation data obviously cannot retrieve all relevant pieces of information. Recently, a growing interest is recorded in the exploitation of street-level pictures, procured either through crowdsourcing or through specialized services like Google Street View. Pictures of building facades convey a great amount of information, but their interpretation is complex. Recently, however, smarter image analysis methods based on deep learning started appearing in literature, made possible by the increasing availability of computational power. In this paper, we leverage such methods to design a system for large-scale, systematic scanning of street-level pictures intended to map floor numbers in urban buildings. Although quite simple, this piece of information is a relevant exposure proxy in risk assessment. In the proposed system, a series of georeferenced images are automatically retrieved from the repository where they sit. A tailored deep learning net is first trained on sample images tagged through visual interpretation, and then systematically applied to the entire retrieved dataset. A specific algorithm allows attaching “number of floors” tags to the correct building in a dedicated GIS (Geographic Information System) layer, which is finally output by the system as an “exposure proxy” layer.
Compact cities have been attributed to lower per capita energy use. However, the complexity of relationships between the elements that constitute energy consumption in the urban system is poorly understood. Little or no research exist on the relation between energy costs of building taller, and transportation and infrastructure energy benefits of building denser. This study provides a theoretical assessment of how energy use is related to urban density in a densely populated area, to aid the development of sustainable cities and land-use planning. The paper builds a holistic parametric model to estimate the total urban energy use for space heating, embodied building energy, transportation energy, and road infrastructure energy, and how these relate to urban density. It does so by varying building height and other urban characteristics related to density, with the aim of identifying the most influential parameters with regard to energy consumption. The possibility of an optimal building height and urban density is also investigated. A much denser and taller city structure than what is normal in cities today appears to be optimal for low urban energy use. The most influential urban density indicators are found to be the dwelling service level (m²/cap) and the building design lifetime. Transportation energy becomes increasingly important with a rise in population. Results indicate that depending on population and building lifetime there exists an optimal building height in the range of 7-27 stories. Climate is found to significantly influence the energy results. These preliminary findings are indicative of general trends, but further research and development of the model are needed to reduce uncertainties.
Rapid urbanization, especially in developing countries, means that the worldwide tradition of living in low-rise housing is giving way to life in urban apartments. This implies huge environmental and sociocultural changes. For sustainability, dense cities offer some advantages, including efficient land use and transport systems. But there are also many possible negatives of such urbanization, and particularly for lower income groups.
A widespread model is high-rise urban “superblocks”. The reasoning is often said to be the need to house many people in very compact cities. This argument is not strictly true. Equally high population densities can be achieved in several ways, including quite low-rise, with equal energy efficiency as well as environmental and social qualities. We explore these choices and assess options for sustainable living in future urban residential areas.
Life cycle analysis is often applied to individual buildings but less often to urban development seen as a whole. We suggest some important “new” considerations need to be taken into account in deciding which urban forms to choose. In particular, high-rise as compared to low-dense options have implications as regards embodied energy, recurrent costs, flexibility and post use, which have to date been little discussed in the research literature.
Data on the number of floors is required for several applications, for instance, energy demand estimation, population estimation, and flood response plans. Despite this, open data on the number of floors is very rare, even when a 3D city model is available. In practice, it is most often inferred with a geometric method: elevation data is used to estimate the height of a building, which is divided by an assumed storey height and rounded. However, as we demonstrate in this paper with a large dataset of residential buildings, this method is unreliable: <70% of the buildings have a correct estimate. We demonstrate that other attributes and characteristics of buildings can help us better predict the number of floors. We propose several indicators (e.g. construction year, cadastral attributes, building geometry, and neighbourhood census data), and we present a predictive model that was trained with 172,000 buildings in the Netherlands. Our model achieves an accuracy of 94.5% for residential buildings with five floors or less, which is an improvement of about 25% over the geometric approach. Above five floors, our model has only a slight improvement on the geometric approach (5%). The main culprit is the lack of training data for tall buildings, which is uncommon in the Netherlands.
The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR). This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. Unfortunately, very little data is available for training such a network – current street-view datasets contain either semantic annotations that lack geometric attributes, or rectified facades without perspective priors. To address this issue, we first compile a new dataset and develop a new data augmentation scheme to synthesize training samples by harassing (i) the rich semantics of existing rectified facades and (ii) perspective priors of buildings in diverse street views. Next, we design
FloorLevel-Net
, a multi-task learning network that associates explicit features of building facades and implicit floor-level lines, along with a height-attention mechanism to help enforce a vertical ordering of floor-level lines. The generated segmentations are then passed to a second-stage geometry post-processing to exploit self-constrained geometric priors for plausible and consistent reconstruction of floor-level lines. Quantitative and qualitative evaluations conducted on assorted facades in existing datasets and street views from Google demonstrate the effectiveness of our approach. Also, we present context-aware image overlay results and show the potentials of our approach in enriching AR-related applications.
Project website: https://wumengyangok.github.io/Project/FloorLevelNet .</i
Many cities across the United States have turned to building energy disclosure (or benchmarking) laws to encourage transparency in energy efficiency markets and to support sustainability and carbon reduction plans. In addition to direct peer-to-peer comparisons, the benchmarking data published under these laws have been used as a tool by researchers and policy-makers to study the distribution and determinants of energy use in large buildings. However, these policies only cover a small subset of the building stock in a given city, and thus capture only a fraction of energy use at the urban scale. To overcome this limitation, we develop a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely-available property and zoning information. We use statistical models to predict the energy use of 1.1 million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city's energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level using actual consumption data from calendar year 2014. We find the OLS model performs best when generalizing to the City as a whole, and SVM results in the lowest mean absolute error for predicting energy use within the LL84 sample. Our median predicted electric energy use intensity for office buildings is 71.2 kbtu/sf and for residential buildings is 31.2 kbtu/sf with mean absolute log accuracy ratio of 0.17. Building age is found to be a significant predictor of energy use, with newer buildings (particularly those built since 1991) found to have higher consumption levels than those constructed before 1930. We also find higher electric consumption in office and retail buildings, although the sign is reversed for natural gas. In general, larger buildings use less energy per square foot, while taller buildings with more stories, controlling for floor area, use more energy per square foot. Attached buildings – those with adjacent buildings and a shared party wall – are found to have lower natural gas use intensity. The results demonstrate that electricity consumption can be reliably predicted using actual data from a relatively small subset of buildings, while natural gas use presents a more complicated problem given the bimodal distribution of consumption and infrastructure availability.
Despite the increasing popularity of route guidance systems, current digital maps are still inadequate for many advanced applications in automotive safety and convenience. Among the drawbacks are the insufficient accuracy of road geometry and the lack of fine-grained information, such as lane positions and intersection structure. In this paper, we present an approach to induce high-precision maps from traces of vehicles equipped with differential GPS receivers. Since the cost of these systems is rapidly decreasing and wireless technology is advancing to provide the communication infrastructure, we expect that in the next few years large amounts of car data will be available inexpensively. Our approach consists of successive processing steps: individual vehicle trajectories are divided into road segments and intersections; a road centerline is derived for each segment; lane positions are determined by clustering the perpendicular offsets from it; and the transitions of traces between segments are utilized in the generation of intersection models. This paper describes an approach to this complex data-mining task in a contiguous manner. Among the new contributions are a spatial clustering algorithm for inferring the connectivity structure, more powerful lane finding algorithms that are able to handle lane splits and merges, and an approach to inferring detailed intersection models.
OpenCV RANSAC is now USAC
- D Mishkin
Mishkin, D.: OpenCV RANSAC is now USAC (Mar 2023), https://opencv.org/
evaluating-opencvs-new-ransacs/