6 research outputs found
Modelling urban networks using Variational Autoencoders
A long-standing question for urban and regional planners pertains to the ability to describe urban patterns quantitatively. Cities’ transport infrastructure, particularly street networks, provides an invaluable source of information about the urban patterns generated by peoples’ movements and their interactions. With the increasing availability of street network datasets and the advancements in deep learning methods, we are presented with an unprecedented opportunity to push the frontiers of urban modelling towards more data-driven and accurate models of urban forms.In this study, we present our initial work on applying deep generative models to urban street network data to create spatially explicit urban models. We based our work on Variational Autoencoders (VAEs) which are deep generative models that have recently gained their popularity due to the ability to generate realistic images. Initial results show that VAEs are capable of capturing key high-level urban network metrics using low-dimensional vectors and generating new urban forms of complexity matching the cities captured in the street network data
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images
Recent researches have shown the increasing use of machine learn-ing methods
in geography and urban analytics, primarily to extract features and patterns
from spatial and temporal data using a supervised approach. Researches
integrating geographical processes in machine learning models and the use of
unsupervised approacheson geographical data for knowledge discovery had been
sparse. This research contributes to the ladder, where we show how latent
variables learned from unsupervised learning methods on urbanimages can be used
for geographic knowledge discovery. In particular, we propose a simple approach
called Convolutional-PCA(ConvPCA) which are applied on both street level and
street network images to find a set of uncorrelated and ordered visual
latentcomponents. The approach allows for meaningful explanations using a
combination of geographical and generative visualisations to explore the latent
space, and to show how the learned representation can be used to predict urban
characteristics such as streetquality and street network attributes. The
research also finds that the visual components from the ConvPCA model achieves
similaraccuracy when compared to less interpretable dimension reduction
techniques.Comment: SigSpatial 2019 GeoA
Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids
This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader