1,721,094 research outputs found
Assessing bikeability with street view imagery and computer vision
No full textTransportation Research Part C: Emerging Technologies13210337
A Global Feature-Rich Network Dataset of Cities and Dashboard for Comprehensive Urban Analyses
No full textAbstract Urban network analytics has become an essential tool for understanding and modeling the intricate complexity of cities. We introduce the Urbanity data repository to nurture this growing research field, offering a comprehensive, open spatial network resource spanning 50 major cities in 29 countries worldwide. Our workflow enhances OpenStreetMap networks with 40 + high-resolution indicators from open global sources such as street view imagery, building morphology, urban population, and points of interest, catering to a diverse range of applications across multiple fields. We extract streetscape semantic features from more than four million street view images using computer vision. The dataset’s strength lies in its thorough processing and validation at every stage, ensuring data quality and consistency through automated and manual checks. Accompanying the dataset is an interactive, web-based dashboard we developed which facilitates data access to even non-technical stakeholders. Urbanity aids various GeoAI and city comparative analyses, underscoring the growing importance of urban network analytics research
A comprehensive framework for evaluating the quality of street view imagery
No full text10.1016/j.jag.2022.103094International Journal of Applied Earth Observation and Geoinformation115103094-10309
Water View Imagery: Perception and evaluation of urban waterscapes worldwide
No full textGathering knowledge about physical settings and visual information of places has long been of interest to a wide variety of fields as they affect the experience of observers. Previous studies have relied on on-site surveys, low-throughput methods, and limited data sources, which especially hinder analyzing waterscape features. Thus, detecting the relationships between the human perception results of large-scale urban water areas and the waterfront features at high spatial resolutions remains challenging, and worldwide studies have not been conducted. We investigate an alternative: a data-driven waterscapes evaluation approach based on computer vision (CV) to analyze water view imagery (WVI) in 16 cities around the world and measure how people perceive scenes using virtual reality (VR). We bring attention to WVI – the counterpart of street view imagery (SVI) on water bodies, which is readily available for many cities thanks to the usual SVI services, but has been entirely overlooked in research hitherto. Specifically, a deep learning model, which has been trained with 500 segmented water-level photos, was developed to analyze them, achieving the mean pixel accuracy (MPA) of 94%, which advances state of the art. These panoramic images have been assessed through a virtual experience survey in which 60 participants indicated their perceptions across multiple dimensions. Afterwards, a series of statistical analyses were conducted to determine the visual indicators that drive perceptions, and the relationship between the people’s subjective visual perceptions and objective waterscape environment as seen by machines has been established. The results take researchers and watercourse planners one step toward understanding the interactions of the perceptions and semantics of water areas globally. The large-scale dataset we produced in this research has been released openly as the first such instance of open segmented water view imagery, and it is intended to support future studies
Urbanity: automated modelling and analysis of multidimensional networks in cities
No full textAbstract Urban networks play a vital role in connecting multiple urban components and developing our understanding of cities and urban systems. Despite the significant progress we have made in understanding how city networks are connected and spread out, we still have a lot to learn about the meaning and context of these networks. The increasing availability of open data offers opportunities to supplement urban networks with specific location information and create more expressive urban machine-learning models. In this work, we introduce Urbanity, a network-based Python package to automate the construction of feature-rich urban networks anywhere and at any geographical scale. We discuss data sources, the features of our software, and a set of data representing the networks of five major cities around the world. We also test the usefulness of added context in our networks by classifying different types of connections within a single network. Our findings extend accumulated knowledge about how spaces and flows within city networks work, and affirm the importance of contextual features for analyzing city networks
Challenges of urban digital twins: A systematic review and a Delphi expert survey
No full text10.1016/j.autcon.2022.104716Automation in Construction147104716-10471
District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images
Full text linkThe paper describes a dataset that was collected by infrared thermography,
which is a non-contact, non-intrusive technique to collect data and analyze the
built environment in various aspects. While most studies focus on the city and
building scales, the rooftop observatory provides high temporal and spatial
resolution observations with dynamic interactions on the district scale. The
rooftop infrared thermography observatory with a multi-modal platform that is
capable of assessing a wide range of dynamic processes in urban systems was
deployed in Singapore. It was placed on the top of two buildings that overlook
the outdoor context of the campus of the National University of Singapore. The
platform collects remote sensing data from tropical areas on a temporal scale,
allowing users to determine the temperature trend of individual features such
as buildings, roads, and vegetation. The dataset includes 1,365,921 thermal
images collected on average at approximately 10 seconds intervals from two
locations during ten months
How might an Lod Logic Framework Help to Bridge the 3D Cadastre Research-to-Practice Gap?: A Proposal for a Level of Implementation Framework
No full textDuring the past decade, hundreds of research papers have been published on the challenge of registering multi-level properties in land administration and cadastral registrations. In addition, many pilots have been carried out to show potential solutions. However, fundamental and standardised solutions for 3D cadastre are still rare. In this article we analyse the reasons for few 3D cadastre solutions in practice and we propose a 3D cadastre definition framework that can distinguish between different levels of 3D cadastre implementation depending on a specific context. Based on a level of detail logic, it supports an incremental pathway for the implementation of 3D cadastre solutions. We list the scope of the framework and finish with conclusions and future work.Urban Data Scienc
THE ISPRS-EUROSDR GEOBIM BENCHMARK 2019
Full text linkStandardised data formats and data models are essential for data integration and interoperability, which in turn adds value to data by allowing its reuse in multiple contexts. For this reason, in recent years extensive efforts have been focused on standards development. When representing the built environment, 3D city models and Building Information Models are particularly relevant, and their integration is now required to underpin use cases that cover the full life-cycle of a built asset, including design and planning as well as operations and management, and to support legal applications such as cadastral systems. For those kinds of data, CityGML by the Open Geospatial Consortium and Industry Foundation Classes by buildingSMART are the most popular reference standards. However, many users report, often through informal channels, the difficulties of working with these formats. This paper summarizes the outcomes of the GeoBIM Benchmark 2019, a scientific initiative funded by ISPRS and EuroSDR to collect insights into the most relevant issues encountered in the management of CityGML and IFC within existing software. Alongside data management (import, visualisation, analysis, export) problems, issues of particular consequence in terms of integration relate to georeferencing IFC files and the conversions among the two kinds of formats and models. Thus, the benchmark was designed to explore these tasks in available software. Following analysis of the benchmark results, a key outcome is the impossibility to find clear patterns in the behaviour of tools, which consequently means there is no consistency in the implementation of standards. Although the results could seem disappointing, the criticality in managing these standards as they are was described and this awareness can be the starting point for further research or further standards development. Finally, this project was useful to gather a wide community around this topic, and the discussion about the GeoBIM-related issues was definitely pushed. Urban Data Scienc
EUBUCCO v0.1: European building stock characteristics in a common and open database for 200+ million individual buildings.
No full text10.1038/s41597-023-02040-2Sci Data101147
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