631 research outputs found
Modeling urbanization patterns with generative adversarial networks
In this study we propose a new method to simulate hyper-realistic urban
patterns using Generative Adversarial Networks trained with a global urban
land-use inventory. We generated a synthetic urban "universe" that
qualitatively reproduces the complex spatial organization observed in global
urban patterns, while being able to quantitatively recover certain key
high-level urban spatial metrics.Comment: 4 pages, 4 figure
Scalable Population Synthesis with Deep Generative Modeling
Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.Comment: 27 pages, 15 figures, 4 table
Prediction of Transportation Index for Urban Patterns in Small and Medium-sized Indian Cities using Hybrid RidgeGAN Model
The rapid urbanization trend in most developing countries including India is
creating a plethora of civic concerns such as loss of green space, degradation
of environmental health, clean water availability, air pollution, traffic
congestion leading to delays in vehicular transportation, etc. Transportation
and network modeling through transportation indices have been widely used to
understand transportation problems in the recent past. This necessitates
predicting transportation indices to facilitate sustainable urban planning and
traffic management. Recent advancements in deep learning research, in
particular, Generative Adversarial Networks (GANs), and their modifications in
spatial data analysis such as CityGAN, Conditional GAN, and MetroGAN have
enabled urban planners to simulate hyper-realistic urban patterns. These
synthetic urban universes mimic global urban patterns and evaluating their
landscape structures through spatial pattern analysis can aid in comprehending
landscape dynamics, thereby enhancing sustainable urban planning. This research
addresses several challenges in predicting the urban transportation index for
small and medium-sized Indian cities. A hybrid framework based on Kernel Ridge
Regression (KRR) and CityGAN is introduced to predict transportation index
using spatial indicators of human settlement patterns. This paper establishes a
relationship between the transportation index and human settlement indicators
and models it using KRR for the selected 503 Indian cities. The proposed hybrid
pipeline, we call it RidgeGAN model, can evaluate the sustainability of urban
sprawl associated with infrastructure development and transportation systems in
sprawling cities. Experimental results show that the two-step pipeline approach
outperforms existing benchmarks based on spatial and statistical measures
What Can Artificial Intelligence Do for Scientific Realism?
The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling
Deep learning in urban analysis for health
The application of deep learning to urban health analysis is in its early stages, but offers new and promising capabilities in using large image-based datasets to better understand the built environment and its effects on human health. This chapter will introduce and explore some of these capabilities, providing the allied design fields with a roadmap of this emerging area of research, its potentials, and current challenges. The chapter begins with a brief overview of existing research related to urban morphology and health, in which precedent work using traditional methods as well as deep learning are introduced. Next, research is presented demonstrating methods for the use of discriminative and generative deep learning processes for both urban health estimation and analysis. The chapter then concludes with a discussion of key challenges and directions for future work in this emerging field of research
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