Millions of slum dwellers suffer from poor accessibility to urban services
due to inadequate road infrastructure within slums, and road planning for slums
is critical to the sustainable development of cities. Existing re-blocking or
heuristic methods are either time-consuming which cannot generalize to
different slums, or yield sub-optimal road plans in terms of accessibility and
construction costs. In this paper, we present a deep reinforcement learning
based approach to automatically layout roads for slums. We propose a generic
graph model to capture the topological structure of a slum, and devise a novel
graph neural network to select locations for the planned roads. Through masked
policy optimization, our model can generate road plans that connect places in a
slum at minimal construction costs. Extensive experiments on real-world slums
in different countries verify the effectiveness of our model, which can
significantly improve accessibility by 14.3% against existing baseline methods.
Further investigations on transferring across different tasks demonstrate that
our model can master road planning skills in simple scenarios and adapt them to
much more complicated ones, indicating the potential of applying our model in
real-world slum upgrading.

本文提出了一种基于深度强化学习的方法，以自动布置贫民窟的道路，在不同国家的真实城市中进行了广泛的实验，表明该方法可以显著提高可达性 14.3％，具有应用在真实的贫民窟升级中的潜力。

利用深度强化学习进行贫民窟道路规划

Road Planning for Slums via Deep Reinforcement Learning

Roads are critically important infrastructure to societal and economic
development, with huge investments made by governments every year. However,
methods for monitoring those investments tend to be time-consuming, laborious,
and expensive, placing them out of reach for many developing regions. In this
work, we develop a model for monitoring the quality of road infrastructure
using satellite imagery. For this task, we harness two trends: the increasing
availability of high-resolution, often-updated satellite imagery, and the
enormous improvement in speed and accuracy of convolutional neural
network-based methods for performing computer vision tasks. We employ a unique
dataset of road quality information on 7000km of roads in Kenya combined with
50cm resolution satellite imagery. We create models for a binary classification
task as well as a comprehensive 5-category classification task, with accuracy
scores of 88 and 73 percent respectively. We also provide evidence of the
robustness of our methods with challenging held-out scenarios, though we note
some improvement is still required for confident analysis of a never before
seen road. We believe these results are well-positioned to have substantial
impact on a broad set of transport applications.

利用卫星图像、卷积神经网络等技术，开发了一种监测道路基础设施的模型，并取得了较为优异的分类效果，该方法可在交通运输等众多领域具有广泛应用前景。