15 research outputs found
Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies
Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research
Sustainable urban development indicators in Great Britain from 2001 to 2016
Current planning strategies promoting suburbanisation, land use zoning and low built-up density areas tend to increase the environmental footprint of cities. In the last decades, international and local government plans are increasingly targeted at making urban areas more sustainable. Urban structure has been proved to be an important factor guiding urban smart growth policies that promote sustainable urban environments and improve neighbourhood social cohesion. This paper draws on a series of unique historical datasets obtained from Ordnance Survey, covering the largest British urban areas over the last 15 years (2001–2016) to develop a set of twelve indicators and a composite Sustainable Urban Development Index to quantitatively measure and assess key built environment features and their relative change compared to other areas at each point in time based on regular 1 km2 grids. The results show that there is a relative increase in urban structure sustainability of areas in and around city centres and identify that the primary built environment feature driving these improvements was an increase in walkable spaces
Spatial scale analysis of landscape processes for digital soil mapping in Ireland
Soil is one of the most precious resources on Earth because of its role in storing
and recycling water and nutrients essential for life, providing a variety of
ecosystem services. This vulnerable resource is at risk from degradation by
erosion, salinity, contamination and other effects of mismanagement. Information
from soil is therefore crucial for its sustainable management. While the demand
for soil information is growing, the quantity of data collected in the field is reducing
due to financial constraints. Digital Soil Mapping (DSM) supports the creation of
geographically referenced soil databases generated by using field observations
or legacy data coupled, through quantitative relationships, with environmental
covariates. This enables the creation of soil maps at unexplored locations at
reduced costs. The selection of an optimal scale for environmental covariates is
still an unsolved issue affecting the accuracy of DSM.
The overall aim of this research was to explore the effect of spatial scale
alterations of environmental covariates in DSM. Three main targets were
identified: assessing the impact of spatial scale alterations on classifying soil
taxonomic units; investigating existing approaches from related scientific fields
for the detection of scale patterns and finally enabling practitioners to find a
suitable scale for environmental covariates by developing a new methodology for
spatial scale analysis in DSM.
Three study areas, covered by detailed reconnaissance soil survey, were
identified in the Republic of Ireland. Their different pedological and
geomorphological characteristics allowed to test scale behaviours across the
spectrum of conditions present in the Irish landscape. The investigation started
by examining the effects of scale alteration of the finest resolution environmental
covariate, the Digital Elevation Model (DEM), on the classification of soil
taxonomic units. Empirical approaches from related scientific fields were
subsequently selected from the literature, applied to the study areas and
compared with the experimental methodology. Wavelet analysis was also
employed to decompose the DEMs into a series of independent components at
varying scales and then used in DSM analysis of soil taxonomic units. Finally, a
new multiscale methodology was developed and evaluated against the previously
presented experimental results.
The results obtained by the experimental methodology have proved the
significant role of scale alterations in the classification accuracy of soil taxonomic
units, challenging the common practice of using the finest available resolution of
DEM in DSM analysis. The set of eight empirical approaches selected in the
literature have been proved to have a detrimental effect on the selection of an
optimal DEM scale for DSM applications. Wavelet analysis was shown effective
in removing DEM sources of variation, increasing DSM model performance by
spatially decomposing the DEM. Finally, my main contribution to knowledge has
been developing a new multiscale methodology for DSM applications by
combining a DEM segmentation technique performed by k-means clustering of
local variograms parameters calculated in a moving window with an experimental
methodology altering DEM scales. The newly developed multiscale methodology
offers a way to significantly improve classification accuracy of soil taxonomic units
in DSM.
In conclusion, this research has shown that spatial scale analysis of
environmental covariates significantly enhances the practice of DSM, improving
overall classification accuracy of soil taxonomic units. The newly developed
multiscale methodology can be successfully integrated in current DSM analysis
of soil taxonomic units performed with data mining techniques, so advancing the
practice of soil mapping. The future of DSM, as it successfully progresses from
the early pioneering years into an established discipline, will have to include scale
and in particular multiscale investigations in its methodology. DSM will have to
move from a methodology of spatial data with scale to a spatial scale
methodology. It is now time to consider scale as a key soil and modelling attribute
in DSM
Mapping Trusted Paths to VGI
We propose a novel method of assessing OpenStreetMap data using the concept of Data Maturity. Based on research into data quality and trust in user generated content, this is a set of measurements that can be derived from provenance data extracted from OpenStreetMap edit history
Multi-Criteria Framework for Routing on Access Land: A Case Study on Dartmoor National Park
Creating routes across open areas is challenging due to the absence of a defined routing network and the complexity of the environment, in which multiple criteria may affect route choice. In the context of urban environments, research has found Visibility and Spider-Grid subgraphs to be effective approaches that generate realistic routes. However, the case studies presented typically focus on plazas or parks with defined entry and exit points; little work has been carried out to date on creating routes across open areas in rural settings, which are complex environments with varying terrain and obstacles and undefined entry or exit points. To address this gap, this study proposes a method for routing across open areas based on a Spider-Grid subgraph using queen contiguity. The method leverages a Weighted Sum–Dijkstra’s algorithm to allow multiple criteria such as surface condition, total time, and gradient to be considered when creating routes. The method is tested on the problem of routing across two areas of Dartmoor National Park, United Kingdom. The generated routes are compared with benchmark algorithms and real paths created by users of the Ordnance Survey’s Maps App. The generated routes are found to be more realistic than those of the benchmark methods and closer to the real paths. Furthermore, the routes are able to bypass hazards and obstacles while still providing realistic and flexible routes to the user
Impact of Climate Change on the Heating Demand of Buildings. A District Level Approach
There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change
Recognising place under distinct weather variability, a comparison between end-to-end and metric learning approaches
Autonomous driving requires robust and accurate real time localisation information to navigate and perform trajectory planning. Although Global Navigation Satellite Systems (GNSS) are most frequently used in this application, they are unreliable within urban environments because of multipath and non-line-of-sight errors. Alternative solutions exist that exploit rich visual content from images that can be corresponded with a stored representation, such as a map, to determine the vehicles location. However, one major cause of reduced location accuracy are variations in environmental conditions between the images captured and those stored in the representation. We tackle this issue directly by collecting a simulated and real-world dataset captured over a single route under multiple environmental conditions. We demonstrate the effectiveness of an end-to-end approach in recognising place and by extension determining vehicle location
An integrated geospatial data model for active travel infrastructure
Active travel has received increased investment and interest in many countries both due to COVID-19 and to policies which promote a shift in mobility behaviours to support a wide range of public and individual goods. However, while there has often been substantial investment in the physical infrastructure that can help facilitate active travel, there has not so far always been commensurate investment in the data infrastructure which can help enable people to shift trips to active modes. Current fragmented data and data models and a lack of data standards pose a barrier to the development of the applications which are needed to support planners, users and journey planning. There is therefore a need for a more integrated, better-connected and more richly attributed active travel geospatial network model. This paper describes the development of such a data model, and its initial application to a case study of Great Britain. It demonstrates how the development, population and maintenance of such a data model could facilitate a range of novel applications, such as the personalisation of active travel journey planning to address different user needs and capabilities. If the potential societal benefits from investment in physical active travel infrastructure are to be fully realised, this needs to be supported by the availability of a robust spatial data infrastructure capable of providing the information required by walkers, wheeled users and cyclists to make effective use of the active travel network