Beril Sirmacek

Beril Sirmacek
Saxion University of Applied Sciences · School of Creative Technology

Doctor of Engineering

About

104
Publications
37,011
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1,609
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Introduction
Smart city development for climate change adaptation, using remote sensing and artificial intelligence techniques.

Publications

Publications (104)
Article
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, we address some of the opportunities brought by satellite remote sensing imaging and artificial intelligence (AI) in order to measure...
Preprint
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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 Goals (SDGs) proposed by the United Nations (UN) for the 2030 Agenda. The present contribution focuses on three SDGs related to healthy and sustainable societies, i.e. SDG...
Preprint
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Several environmental tipping points and self-reinforcing feedback loops are frequently dismissed by scientists. Thus, existing climate models are prepared with tipping points which insufficiently represent the actual environmental conditions, dismissing the strong correlations and self-reinforcements. Calculation of the Arctic sea ice loss is most...
Article
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 between temperature increase and temperature thresholds which might trigger extinction of species. These temperature and mass-extinction relation graphs are found practical...
Preprint
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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 citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact,...
Preprint
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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 solutions compliant with the Sustainable Development Goals (SDGs). We highlight the potential of extracting truly-interpretable models from deep-learning methods, for inst...
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Song et al. (Song et al., 2021) published "Thresholds of temperature changes for mass extinctions" on 4 August 2021. They described the correlation between an increase in global-average temperature and Mass Extinction Events. This response intends to provide a more comprehensive evaluation of the environmental thresholds required to sustain a habit...
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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. Automating the analysis of TPUS data will help in growing our understanding of pelvic floor related problems. In this study we present a U-net like neural network with some co...
Article
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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 measureme...
Preprint
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 in...
Preprint
Full-text available
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 c...
Preprint
Full-text available
Deep Reinforcement Learning has shown its ability in solving complicated problems directly from high-dimensional observations. However, in end-to-end settings, Reinforcement Learning algorithms are not sample-efficient and requires long training times and quantities of data. In this work, we proposed a framework for sample-efficient Reinforcement L...
Preprint
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, such degrees of intelligence are not easy to obtain. Reinforcement learning algorithms have proven to be capable of solving complicated robotics tasks in an end-to-end fash...
Article
Full-text available
Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms...
Article
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Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, and mapping unknown environments. The reinfo...
Preprint
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This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as an intermediate step for the reconstruction of wall-parallel velocity fields from coarse wall measurements. The analysis has been carried out with a database of a turbulent open-channel flow with friction Reynolds number $Re_{\tau}=180$ generated t...
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Indoor occupancy prediction is a prerequisite for the management of energy consumption, security, health, and other systems in smart buildings. Previous studies have shown that buildings that automatize their heating, lighting, air conditioning, and ventilation systems through considering the occupancy and activity information might reduce energy c...
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Purpose We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have a deeper and wider network architecture while still accommodates the 3D information. However, there h...
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Solving the challenge of occupancy prediction is crucial in order to design efficient and sustainable office spaces and automate lighting, heating, and air circulation in these facilities. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the cos...
Conference Paper
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In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared...
Preprint
Full-text available
In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the...
Article
Full-text available
Collaborative swarms of robots/UAVs constitute a promising solution for precision agriculture and for automatizing agricultural processes. Since agricultural fields have complex topologies and different constraints, the problem of optimized path routing of these swarms is important to be tackled. Hence, this paper deals with the problem of optimizi...
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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 policy directly from the sensory inputs, i.e the observations, often requires processing and storage of a huge amount of data. In the context of robotics, the cost of data fro...
Article
Functional performance of handheld laser speckle contrast imaging (LSCI) is compromised by movement artefacts. Here we quantify the movements of a handheld LSCI system employing electromagnetic (EM) tracking and measure the applied translational, tilt and on-surface laser beam speeds. By observing speckle contrast on static objects, the magnitudes...
Preprint
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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...
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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 of children tends to change when they reach an age of 6. This, thus makes it very difficult to monitor their respiratory state easily. In this paper, we present a cheap n...
Preprint
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, compression therapy or bandaging. However, the therapy methods are still insufficiently evaluated. Especially, because of not having a reliable method to measure the chang...
Article
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Recently, semantic video segmentation gained high attention especially for supporting autonomous driving systems. Deep learning methods made it possible to implement real-time segmentation and object identification algorithms on videos. However, most of the available approaches process each video frame independently disregarding their sequential re...
Preprint
Full-text available
Recently, semantic video segmentation gained high attention especially for supporting autonomous driving systems. Deep learning methods made it possible to implement real time segmentation and object identification algorithms on videos. However, most of the available approaches process each video frame independently disregarding their sequential re...
Preprint
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 necessary. The central research question in this paper is "How to segment skin lesion images using a neural network with low available data?". This question is divided into...
Conference Paper
Purpose Estimation of respiratory-induced motion of hepatic lesions can improve the outcomes of liver cancer treatment. In this study the aim is to assess the influence of different surrogate signals selection and also the size of population on the success of a novel intelligent approach in prediction of tumor respiratory motion. Materials and meth...
Article
Full-text available
We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired ta...
Article
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We introduce a fully automated only path planning approach especially for drones. This novel method relies on usage of a stereo camera mounted at the bottom of a hexagonal drone for real-time point cloud reconstruction and localization. The real-time point cloud is analyzed in a software loop where the entropy of the point cloud and the surface nor...
Conference Paper
Full-text available
We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired ta...
Article
Full-text available
3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (w...
Article
Full-text available
3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (w...
Article
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Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is wher...
Article
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We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the requirements...
Article
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We present a comparison of point cloud generation and quality of data acquired by Zebedee (Zeb1) and Leica C10 devices which are used in the same building interior. Both sensor devices come with different practical and technical advantages. As it could be expected, these advantages come with some drawbacks. Therefore, depending on the requirements...
Article
Full-text available
Recent technological developments help us to acquire high quality 3D measurements of our urban environment. However, these measurements, which come as point clouds or Digital Surface Models (DSM), do not directly give 3D geometrical models of buildings. In addition to that, they are not suitable for fast 3D rendering. Therefore, detection and 3D re...
Article
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In urbanized Western Europe trees are considered an important component of the built-up environment. This also means that there is an increasing demand for tree inventories. Laser mobile mapping systems provide an efficient and accurate way to sample the 3D road surrounding including notable roadside trees. Indeed, at, say, 50 km/h such systems col...
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The European FP7 project IQmulus yearly organizes several processing contests, where submissions are requested for novel algorithms for point cloud and other big geodata processing. This paper describes the set-up and execution of a contest having the purpose to evaluate state-of-the-art algorithms for Mobile Mapping System point clouds, in order t...
Article
We introduce a method to texture 3D urban models with photographs that even works for Google Streetview images and can be done with currently available free software. This allows realistic texturing, even when it is not possible or cost-effective to (re)visit a scanned site to take textured scans or photographs. Mapping a photograph onto a 3D model...
Article
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Development of laser scanning technologies has promoted tree monitoring studies to a new level, as the laser scanning point clouds enable accurate 3D measurements in a fast and environmental friendly manner. In this paper, we introduce a probability matrix computation based algorithm for automatically classifying laser scanning point clouds into ’t...
Article
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Low-cost sensor generated 3D models can be useful for quick 3D urban model updating, yet the quality of the models is questionable. In this article, we evaluate the reliability of an automatic point cloud generation method using multi-view iPhone images or an iPhone video file as an input. We register such automatically generated point cloud on a T...
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The paper presents a home-made 3d scanner, consisting of off-the-shelf components: a camera and a projector. It is intended for monitoring dynamics of riverbed mophology observed under laboratory conditions in a flume, which is currently under construction. Special attention is paid to satisfying high requirements concerning accuracy and precision...
Article
Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different...
Conference Paper
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The SYDDARTA project is an on-going European Commission funded initiative under the 7th Framework Programme. Its main objective is the development of a pre-industrial prototype for diagnosing the deterioration of movable art assets. The device combines two different optical techniques for the acquisition of data. On one hand, hyperspectral imaging...
Conference Paper
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Deterioration of artwork, in particular paintings, can be produced by environmental factors such as temperature fluctuations, relative humidity variations, ultraviolet radiation and biological factors among others. The effects of these parameters produce changes in both the painting structure and chemical composition. While well established analyti...
Article
We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a loca...
Article
Road network detection from very high resolution satellite and aerial images has diverse and important usage areas such as map generation and updating. Although an expert can label road pixels in a given image, this operation is prone to errors and quite time consuming. Therefore, an automated system is needed to detect the road network in a given...
Article
Full-text available
Recently, analysis of man events in real-time using computer vision techniques became a very important research field. Especially, understanding motion of people can be helpful to prevent unpleasant conditions. Understanding behavioral dynamics of people can also help to estimate future states of underground passages, shopping center like public en...
Conference Paper
Full-text available
A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye) new remote sensing data are available for a better detection, de...
Conference Paper
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The façades of buildings are almost always organized according to Gestalt principles such as good continuation, repetition in similarity, or symmetry etc. Coding such principles in production systems yields a very flexible frame to explore the usefulness of such principles in automatic façade understanding. Capturing images and image sequences of f...
Conference Paper
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Three-dimensional urban region representations can be used for detailed urban monitoring, change and damage detection purposes. In order to obtain three-dimensional representation, one of the easiest and cheapest way is to use Digital Surface Models (DSMs) which are generated from very high resolution stereo satellite images using stereovision tech...
Conference Paper
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Since remote sensing provides new sensors and techniques to accumulate stereo data on urban regions, three-dimensional (3D) repre- sentation of these regions gained much interest for various applications. 3D urban region representation can e.g. be used for detailed urban monitoring, change and damage detection purposes. In order to obtain 3D repres...
Conference Paper
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Real-time monitoring of crowded regions has crucial importance to avoid overload of people in certain areas. Understanding dynamics of large people crowds can also help to estimate future status of public areas. In order to bring an automatic solution to the problem, herein we introduce four different approaches based on feature extraction from ai...
Conference Paper
Full-text available
For effective conservation management, it is very important to provide accurate estimates of animal populations with certain time intervals. So far many studies are performed visually/manually which requires much time and is prone to errors. Besides, only a limited area can be considered for counting because of the effort required. In order to brin...
Article
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Using the capability of WorldView-2 to acquire very high resolution (VHR) stereo imagery together with as much as eight spectral channels allows the worldwide monitoring of any built up areas, like cities in evolving states. In this paper we show the benefit of generating a high resolution digital surface model (DSM) from multi-view stereo data (PA...