Using street based metrics to characterize urban typologies

[EN] Urban spatial structures reflect local particularities produced during the development of a city. High spatial resolution imagery and LiDAR data are currently used to derive numerical attributes to describe in detail intra-urban structures and morphologies. Urban block boundaries have been frequently used to define the units for extracting metrics from remotely sensed data. In this paper, we propose to complement these metrics with a set of novel descriptors of the streets surrounding the urban blocks under consideration. These metrics numerically describe geometrical properties in addition to other distinctive aspects, such as presence and properties of vegetation and the relationship between the streets and buildings. For this purpose, we also introduce a methodology for partitioning the street area related to an urban block into polygons from which the street urban metrics are derived. We achieve the assessment of these metrics through application of a one-way ANOVA procedure, the winnowing technique, and a decision tree classifier. Our results suggest that street metrics, and particularly those describing the street geometry, are suitable for enhancing the discrimination of complex urban typologies and help to reduce the confusion between certain typologies. The overall classification accuracy increased from 72.7% to 81.1% after the addition street of descriptors. The results of this study demonstrate the usefulness of these metrics for describing street properties and complementing information derived from urban blocks to improve the description of urban areas. Street metrics are of particular use for the characterization of urban typologies and to study the dynamics of cities.The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE, and the data made available by the Spanish Instituto Geográfico Nacional (IGN)Hermosilla, T.; Palomar-Vázquez, J.; Balaguer Beser, ÁA.; Balsa Barreiro, J.; Ruiz Fernández, LÁ. (2014). Using street based metrics to characterize urban typologies. Computers, Environment and Urban Systems. 44:68-79. https://doi.org/10.1016/j.compenvurbsys.2013.12.002S68794

Automatic building detection and land-use classification in urban areas using multispectral high-spatial resolution imagery and LiDAR data

Urban areas areimportant environments, accounting for approximately half the population of theworld. Cities attract residents partly because they offer ample opportunitiesfor development, which often results in urban sprawl and its complex environmentalimplications. It is therefore necessary to develop technologies andmethodologies that permit monitoring the effects of various problems that havebeen or are thought to be associated with urban sprawl. These technologieswould facilitate the adoption of policies seeking to minimize the negativeeffects of urban sprawl. Solutions require a precise knowledge of the urbanenvironment under consideration to enable the development of more efficienturban zoning plans. The high dynamism of urban areas produces seeminglycontinuous alterations of land cover and use; consequently, cartographicinformation becomes quickly and is oftentimes outdated. Hence, the availabilityof detailed and up-to-date cartographic and geographic information is imperativefor an adequate management and planning of urban areas. Usually the process ofcreating land-use/land-cover maps of urban areas involves field visits andclassical photo-interpretation techniques employing aerial imagery. Thesemethodologies are expensive, time consuming, and also subjective. Digital imageprocessing techniques help reduce the volume of information that needs to bemanually interpreted. The aim of thisstudy is to establish a methodology to automatically detect buildings and toautomatically classify land use in urban environments using multispectralhigh-spatial resolution imagery and LiDAR data. These data were acquired in theframework of the Spanish National Plan for Airborne Orthophotographs, having beenavailable for public Spanish administrations. Two mainapproaches for automatic building detection and localization using high spatialresolution imagery and LiDAR data are evaluated The thresholding-based approachis founded on the establishment of two threshold values: one is the minimumheight to be considered as a building, defined using the LiDAR data; the other isthe presence of vegetation, defined with the spectral response. The otherapproach follows the standard scheme of object-based image classification:segmentation, feature extraction and selection, and classification, hereperformed using decision trees. In addition, the effect of including contextualrelations with shadows in the building detection process is evaluated. Qualityassessment is performed at both area and object levels. Area-level assessments evaluatethe building delineation performance whereas object-level assessments evaluatethe accuracy in the spatial location of individual buildings. Urban land-useclassification is achieved by applying object-based image analysis techniques. Objects are defined using the boundaries of cadastral plots. The plots were characterizedto achieve the classification by employing a descriptive feature setspecifically designed to describe urban environments. The proposed descriptivefeatures aim to emulate human cognition by numerically quantifying theproperties of the image elements and so enable each to be distinguishable. These features describe each plot as a single entity based on several aspectsthat reflect the information used: spectral, three-dimensional, and geometrictypologies. In addition, a set of contextual features at both the internal andexternal levels is defined. Internal context features describe an object withrespect to the land cover types contained within the plots, which were, in thiscase, buildings and vegetation. External context features characterise eachobject by considering the common properties of adjacent objects that, whencombined, create an aggregate in a higher level than plot level: urban blocks. Results show that thresholding-based building detection approachperforms better in the different scenarios assessed. This method produces amore accurate building delineation and object detection than the object-basedclassification method. The building type appears as a key factor in thebuilding detection performance. Thus, urban and industrial areas show betteraccuracies in detection metrics than suburban areas, due to the small size ofsuburban constructions, combined with the prominent presence of trees insuburban classes, hindering the building detection process. The relationsbetween buildings and shadows improve the object-level detection, removingsmall objects erroneously detected as buildings that negatively affect to thequality indices. Classificationtest results show that internal and external context features complement theimage-derived features, improving the classification accuracy values of urbanclasses, especially between classes that show similarities in their image-basedand three-dimensional features. Context features enable a superiordiscrimination of suburban building typologies, of planned urban areas andhistorical areas, and also of planned urban areas and isolated buildings. The outcomes showthat these automatic methodologies are especially suitable for computing usefulinformation for constructing and updating land-use/land-cover geospatialdatabases. Digital image processing-based methodologies provide better resultsthan visual interpretation-based methods. Thus, automatic building detectiontechniques produce a superior estimation of built-up surface in an objectivemanner, independent of human operators. The combination of building detectionand automatic classification of land use in urban areas enable the distinguishingand describing of different urban typologies, contributing to greater accuracyand information than standard visual interpretation-based techniques. Theproposed methodology, based on an automated descriptive feature extraction fromLiDAR images and data, is appropriate for city mapping, urban landscapecharacterisation and management, and the updating of geospatial databases, allof which provide novel tools to increase the frequency and efficiency of thestudy of complex urban areas

The role of urban form and socio-economic variables for estimating the building energy savings potential at the urban scale

In the past, to make the city liveable, the urban morphology has always be considered taking into account the climate, the buildings’ density and characteristics, the type of inhabitants and their social condition. On the contrary, recently in the urban planning process the morphological aspects are no more included even if they influence the energy consumption, the thermal comfort of the urban spaces and the district air quality. Moreover, the socio-economic conditions of inhabitants might strongly affect the lifestyle choice and behavior of building occupants and thus, the probability of success of urban planning measures for energy conservation. The present study aims to: 1) identify the correlation between thermal energy consumption for space heating and urban variables and 2) investigate the role of socio-economic variables in energy savings potential. The city of Turin is suitable for these analyses because it is characterized by different urban forms and urban spaces and by various characteristics of the population. By using a GIS tool, the district 3, chosen as a case study, has been divided into different urban textures considering their urban and socio-economic characteristics. The results of this study show that the measured energy consumption of single building depends on the physical building features (f.i. thermal insulation level, the compactness, the energy system efficiency etc.) but also on the urban form and the streets’ orientation. Another important result is that the social and economic situation of inhabitants has a relevant role in the success of sustainable policies. These conclusions may support urban planners in the definition of new urban areas with some “preliminary” energy savings measures at no cost and in formulating tailored policies according to socio-economic conditions from district to district. (Presented at the AIGE Conference 2015

The 'Paris-end' of town? Urban typology through machine learning

The confluence of recent advances in availability of geospatial information, computing power, and artificial intelligence offers new opportunities to understand how and where our cities differ or are alike. Departing from a traditional `top-down' analysis of urban design features, this project analyses millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics. A (novel) neural network-based framework is trained with imagery from the largest 1692 cities in the world and the resulting models are used to compare within-city locations from Melbourne and Sydney to determine the closest connections between these areas and their international comparators. This work demonstrates a new, consistent, and objective method to begin to understand the relationship between cities and their health, transport, and environmental consequences of their design. The results show specific advantages and disadvantages using each type of imagery. Neural networks trained with map imagery will be highly influenced by the mix of roads, public transport, and green and blue space as well as the structure of these elements. The colours of natural and built features stand out as dominant characteristics in satellite imagery. The use of street view imagery will emphasise the features of a human scaled visual geography of streetscapes. Finally, and perhaps most importantly, this research also answers the age-old question, ``Is there really a `Paris-end' to your city?''

Measuring urban form : overcoming terminological inconsistencies for a quantitative and comprehensive morphologic analysis of cities

Unprecedented urbanisation processes characterise the Great Acceleration, urging urban researchers to make sense of data analysis in support of evidence-based and large-scale deci- sion-making. Urban morphologists are no exception since the impact of urban form on funda- mental natural and social patterns (equity, prosperity and resource consumption’s efficiency) is now fully acknowledged. However, urban morphology is still far from offering a comprehensive and reliable framework for quantitative analysis. Despite remarkable progress since its emergence in the late 1950s, the discipline still exhibits significant terminological inconsistencies with regards to the definition of the fundamental components of urban form, which prevents the establishment of objective models for measuring it. In this article, we present a study of existing methods for measuring urban form, with a focus on terminological inconsistencies, and propose a systematic and comprehensive framework to classify urban form characters, where ‘urban form character’ stands for a characteristic (or feature) of one kind of urban form that distinguishes it from another kind. In particular, we introduce the Index of Elements that allows for a univocal and non-interpretive description of urban form characters. Based on such Index of Elements, we develop a systematic classification of urban form according to six categories (dimension, shape, spatial distribution, intensity, connectivity and diversity) and three conceptual scales (small, medium, large) based on two definitions of scale (extent and grain). This framework is then applied to identify and organise the urban form characters adopted in available literature to date. The resulting classification of urban form characters reveals clear gaps in existing research, in particular, in relation to the spatial distribution and diversity characters. The proposed framework reduces the current inconsistencies of urban morphology research, paving the way to enhanced methods of urban form systematic and quantitative analysis at a global scale

Tracking a city’s center of gravity over 500 years of growth from a time series of georectified historical maps

\ua9 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. It is surprising difficult to define where a city center lies, yet its location has a profound effect on a city’s structure and function. We examine whether city center typicality points can be consistently located on historical maps such that their centroid identifies a meaningful central location over a 500-year period in Southampton, UK. We compare movements of this city center centroid against changes in the geographical center of the city as defined by its boundary. Southampton’s historical maps were georectified with a mean accuracy of 21 m (range 9.9 to 47 m), and 18 to 102 typicality points were identified per map, enough to chart changes in the city center centroid through time. Over nearly 500 years, Southampton’s center has moved just 343 m, often corresponding with the key retail attractants of the time, while its population has increased 80-fold, its administrative area 60-fold and its geographical center moved 1985 m. This inertia to change in the city center presents environmental challenges for the present-day, made worse by the geography of Southampton, bounded by the sea, rivers and major roads. Geographical context, coupled with planning decisions in the past that maintain a city center in its historical location, place limits on the current sustainability of a city

Predicting residential building age from map data

The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points