Flood events cause substantial damage to urban and rural areas. Monitoring water extent during large-scale flooding is crucial in order to identify the area affected and to evaluate damage. During such events, spatial assessments of floodwater may be derived from satellite or airborne sensing platforms. Meanwhile, an increasing availability of smartphones is leading to documentation of flood events directly by individuals, with information shared in real-time using social media. Topographic data, which can be used to determine where floodwater can accumulate, are now often available from national mapping or governmental repositories. In this work, we present and evaluate a method for rapidly estimating flood inundation extent based on a model that fuses remote sensing, social media and topographic data sources. Using geotagged photographs sourced from social media, optical remote sensing and high-resolution terrain mapping, we develop a Bayesian statistical model to estimate the probability of flood inundation through weights-of-evidence analysis. Our experiments were conducted using data collected during the 2014 UK flood event and focus on the Oxford city and surrounding areas. Using the proposed technique, predictions of inundation were evaluated against ground-truth flood extent. The results report on the quantitative accuracy of the multisource mapping process, which obtained area under receiver operating curve values of 0.95 and 0.93 for model fitting and testing, respectively.

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 thisa 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 crossvalidated 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.

Volunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm.

A 3D model communicates more effectively than a 2D model, hence the applications of 3D city models are rapidly gaining significance in urban studies. However, presently, there is a dearth of free of cost, high-resolution 3D city models available for use. This paper offers potential solutions to this problem by providing a globally replicable methodology to generate low-cost 3D city models from open source 2D building data in conjunction with open satellite-based elevation datasets. Two geographically and morphologically different case studies were used to develop and test this methodology: the Chinese city of Shanghai and the city of Nottingham in the UK. The method is based principally on OpenStreetMap (OSM) and Advanced Land Observing Satellite World 3D digital surface model (AW3D DSM) data and use GMTED 2010 DTM data for undulating terrain. Further enhancement of the resultant 3D model, though not compulsory, uses higher resolution elevation models that are not always open source, but if available can be used (i.e., airborne LiDAR generated DTM). Further we test and develop methods to improve the accuracy of the generated 3D models, employing a small subset of high resolution data that are not open source but can be purchased with a minimal budgets. Given these scenarios of data availability are globally applicable and time-efficient for 3D building generation (where 2D building footprints are available), our proposed methodology has the potential to accelerate the production of 3D city models, and thus to facilitate their dependent applications (e.g., disaster management) wherever commercial 3D city models are unavailable.

Abstract:Creating as-built plans of building interiors is a challenging task. In this paper we present a semi-automatic modelling system for creating residential building interior plans and their integration with existing map data to produce building models. Taking a set of imprecise measurements made with an interactive mobile phone room mapping application, the system performs spatial adjustments in accordance with soft and hard constraints imposed on the building plan geometry. The approach uses an optimisation model that exploits a high accuracy building outline, such as can be found in topographic map data, and the building topology to improve the quality of interior measurements and generate a standardised output. We test our system on building plans of five residential homes. Our evaluation shows that the approach enables construction of accurate interior plans from imprecise measurements. The experiments report an average accuracy of 0.24 m, close to the 0.20 m recommended by the CityGML LoD4 specification.

Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

ABSTRACT:Volunteer geographical information (VGI) either in the context of citizen science, active crowdsourcing and even passive crowdsourcing has been proven useful in various societal domains such as natural hazards, health status, disease epidemic and biological monitoring. Nonetheless, the variable degrees or unknown quality due to the crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process in which a quality assurance (QA) is needed is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF) combining the crowdsourced data into one view using potentially other data sources as well. Using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF in order to determine whether a disentanglement can be beneficial or not to a better understanding of the data curation process and to its methodology with respect to crowdsourcing data. Far from rejecting the usability quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible but still in integrating them within an approach analogous to a Bayesian paradigm.

This work investigates constructing plans of building interiors using learned building measurements. In particular, we address the problem of accurately estimating dimensions of rooms when measurements of the interior space have not been captured. Our approach focuses on learning the geometry, orientation and occurrence of rooms from a corpus of real-world building plan data to form a predictive model. The trained predictive model may then be queried to generate estimates of room dimensions and orientations. These estimates are then integrated with the overall building footprint and iteratively improved using a two-stage optimisation process to form complete interior plans. The approach is presented as a semi-automatic method for constructing plans which can cope with a limited set of known information and constructs likely representations of building plans through modelling of soft and hard constraints. We evaluate the method in the context of estimating residential house plans and demonstrate that predictions can effectively be used for constructing plans given limited prior knowledge about the types of rooms and their topology.