52 research outputs found
Remote sensing, AI and innovative prediction methods for adapting cities to the impacts of the climate change
Urban areas are not only one of the biggest contributors to climate change,
but also they are one of the most vulnerable areas with high populations who
would together experience the negative impacts. In this paper, I address some
of the opportunities brought by satellite remote sensing imaging and artificial
intelligence (AI) in order to measure climate adaptation of cities
automatically. I propose an AI-based framework which might be useful for
extracting indicators from remote sensing images and might help with predictive
estimation of future states of these climate adaptation related indicators.
When such models become more robust and used in real-life applications, they
might help decision makers and early responders to choose the best actions to
sustain the wellbeing of society, natural resources and biodiversity. I
underline that this is an open field and an ongoing research for many
scientists, therefore I offer an in depth discussion on the challenges and
limitations of AI-based methods and the predictive estimation models in
general
Using Local Features to Measure Land Development in Urban Regions
Monitoring urban development in a given region provides valuable information to researchers. Currently available, very high resolution satellite images can be used for this purpose. However, manually monitoring land development using these large and complex images is time consuming and prone to errors. To handle this problem, an automated system is needed to measure development in urban regions. Therefore, in this study we propose such an automated method to measure land development in a given urban region imaged in different times. We benefit from novel land development measures for this purpose. They are based on local features obtained from sequential images. As a novel contribution, we represent these local features in a spatial voting matrix. Then, we propose five different land development measures on the formed voting matrix. We test our method on 19 sets of sequential panchromatic Ikonos images. Our test results indicate the possible use of our method in measuring land development automatically
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
We present a map-less path planning algorithm based on Deep Reinforcement
Learning (DRL) for mobile robots navigating in unknown environment that only
relies on 40-dimensional raw laser data and odometry information. The planner
is trained using a reward function shaped based on the online knowledge of the
map of the training environment, obtained using grid-based Rao-Blackwellized
particle filter, in an attempt to enhance the obstacle awareness of the agent.
The agent is trained in a complex simulated environment and evaluated in two
unseen ones. We show that the policy trained using the introduced reward
function not only outperforms standard reward functions in terms of convergence
speed, by a reduction of 36.9\% of the iteration steps, and reduction of the
collision samples, but it also drastically improves the behaviour of the agent
in unseen environments, respectively by 23\% in a simpler workspace and by 45\%
in a more clustered one. Furthermore, the policy trained in the simulation
environment can be directly and successfully transferred to the real robot. A
video of our experiments can be found at: https://youtu.be/UEV7W6e6Zq
From coarse wall measurements to turbulent velocity fields through deep learning
This work evaluates the applicability of super-resolution generative
adversarial networks (SRGANs) as a methodology for the reconstruction of
turbulent-flow quantities from coarse wall measurements. The method is applied
both for the resolution enhancement of wall fields and the estimation of
wall-parallel velocity fields from coarse wall measurements of shear stress and
pressure. The analysis has been carried out with a database of a turbulent
open-channel flow with friction Reynolds number generated
through direct numerical simulation. Coarse wall measurements have been
generated with three different downsampling factors from the
high-resolution fields, and wall-parallel velocity fields have been
reconstructed at four inner-scaled wall-normal distances .
We first show that SRGAN can be used to enhance the resolution of coarse wall
measurements. If compared with direct reconstruction from the sole coarse wall
measurements, SRGAN provides better instantaneous reconstructions, both in
terms of mean-squared error and spectral-fractional error. Even though lower
resolutions in the input wall data make it more challenging to achieve highly
accurate predictions, the proposed SRGAN-based network yields very good
reconstruction results. Furthermore, it is shown that even for the most
challenging cases the SRGAN is capable of capturing the large-scale structures
that populate the flow. The proposed novel methodology has great potential for
closed-loop control applications relying on non-intrusive sensing
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Inspection and maintenance are two crucial aspects of industrial pipeline
plants. While robotics has made tremendous progress in the mechanic design of
in-pipe inspection robots, the autonomous control of such robots is still a big
open challenge due to the high number of actuators and the complex manoeuvres
required. To address this problem, we investigate the usage of Deep
Reinforcement Learning for achieving autonomous navigation of in-pipe robots in
pipeline networks with complex topologies. Moreover, we introduce a
hierarchical policy decomposition based on Hierarchical Reinforcement Learning
to learn robust high-level navigation skills. We show that the hierarchical
structure introduced in the policy is fundamental for solving the navigation
task through pipes and necessary for achieving navigation performances superior
to human-level control
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