132 citations · 180 across the 17 of their papers we have counts for
18 papers
Land Classification in Satellite Images by Injecting Traditional Features to CNN Models
Mehmet Cagri Aksoy, Beril Sirmacek, Cem Unsalan
Deep learning methods have been successfully applied to remote sensing problems for several years. Among these methods, CNN based models have high accuracy in solving the land clas…
The potential of artificial intelligence for achieving healthy and sustainable societies
B. Sirmacek, S. Gupta, F. Mallor +10
In this chapter we extend earlier work (Vinuesa et al., Nature Communications 11, 2020) on the potential of artificial intelligence (AI) to achieve the 17 Sustainable Development G…
Rapid loss of Arctic sea ice
Beril Sirmacek
Several environmental tipping points and self-reinforcing feedback loops are still disregarded within the frequently used climate models. Thus, existing climate models are not very…
Aim in Climate Change and City Pollution
Pablo Torres, Beril Sirmacek, Sergio Hoyas +1
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citi…
Interpretable deep-learning models to help achieve the Sustainable Development Goals
Ricardo Vinuesa, Beril Sirmacek
We discuss our insights into interpretable artificial-intelligence (AI) models, and how they are essential in the context of developing ethical AI systems, as well as data-driven s…
Environmental thresholds for mass-extinction events
Guy R. McPherson, Beril Sirmacek, Ricardo Vinuesa
While the global-average temperatures are rapidly rising, more researchers have been shifting their focus towards the past mass-extinction events in order to show the relations bet…
Recurrent U-net for automatic pelvic floor muscle segmentation on 3D ultrasound
Frieda van den Noort, Beril Sirmacek, Cornelis H. Slump
The prevalance of pelvic floor problems is high within the female population. Transperineal ultrasound (TPUS) is the main imaging modality used to investigate these problems. Autom…
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Nicolò Botteghi, Luuk Grefte, Mannes Poel +4
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, t…
Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
Nicolò Botteghi, Mannes Poel, Beril Sirmacek +1
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learni…
Low Dimensional State Representation Learning with Robotics Priors in Continuous Action Spaces
Nicolò Botteghi, Khaled Alaa, Mannes Poel +4
Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, su…
Remote sensing and AI for building climate adaptation applications
Beril Sirmacek, Ricardo Vinuesa
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 t…
From coarse wall measurements to turbulent velocity fields through deep learning
Alejandro Güemes, Stefano Discetti, Andrea Ianiro +3
This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse…
Low Dimensional State Representation Learning with Reward-shaped Priors
Nicolò Botteghi, Ruben Obbink, Daan Geijs +5
Reinforcement Learning has been able to solve many complicated robotics tasks without any need for feature engineering in an end-to-end fashion. However, learning the optimal polic…
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach
Nicolò Botteghi, Beril Sirmacek, Khaled A. A. Mustafa +2
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…
A low-cost real-time 3D imaging system for contactless asthma observation
Sheona M. M. D. P. Sequeira, Beril Sirmacek
Asthma is becoming a very serious problem with every passing day, especially in children. However, it is very difficult to detect this disorder in them, since the breathing motion…
3D tissue reconstruction with Kinect to evaluate neck lymphedema
Gerrit Brugman, Beril Sirmacek
Lymphedema is a condition of localized tissue swelling caused by a damaged lymphatic system. Therapy to these tissues is applied manually. Some of the methods are lymph drainage, c…
Sequential image processing methods for improving semantic video segmentation algorithms
Beril Sirmacek, Nicolò Botteghi, Santiago Sanchez Escalonilla Plaza
Recently, semantic video segmentation gained high attention especially for supporting autonomous driving systems. Deep learning methods made it possible to implement real time segm…
Semantic Segmentation of Skin Lesions using a Small Data Set
Beril Sirmacek, Max Kivits
Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is ne…