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  • Rural Digital Europe
  • 2017-2021
  • Doctoral thesis
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    Authors: Bárbara Barros Carlos;

    Robotics has revolutionized several industries across the globe through technologies never seen before. However, society still expects more than what today’s robots are really capable of. To deal with such expectations, innovative algorithms must be translated into viable solutions so that robots can continue to improve labor in workplaces and ordinary people’s lives. Among the most important are motion generation algorithms, whose advancements can solve some of the biggest barriers towards more flexible and safe human-robot interactions and operations in dynamic environments. The developments in optimization algorithms and computer processor technology have led nonlinear model predictive control (NMPC) to gain popularity in robotics as a motion generation technique. The main reason for that is its ability to minimize a cost function while respecting a set of constraints that typically represent the system’s physical and operational limitations. Ultimately, these are the elements that grant NMPC an improved performance compared to classic approaches. Despite being a promising technique, the non-negligible computational burden associated with the online solution of the underlying optimal control problems has decisively limited its roll-out to robotic systems subject to short sampling times and resource-constrained hardware. This thesis proposes multiple tailored algorithms for real-time motion generation in robotic systems based on high-performance implementations of NMPC. The primary computational bottlenecks concern the numerical simulation of the continuous-time nonlinear dynamic models and the online solution of the stemming large yet well-structured nonlinear program. The thesis shows that it is possible to achieve significant speed-ups in solution times while preserving nonlinearities through efficient software implementations that cover standard building blocks from nonlinear programming, tailored quadratic programming solvers, and fast approximate schemes for NMPC. Additionally, a discussion is provided in terms of dynamic modeling. It encompasses the design decisions required to create a model that ex- poses the system limitations so that high-quality motions can be attained due to a more accurate representation. Among the ever-growing plethora of robotic systems, the thesis focuses on the motion generation of a double inverted pendulum and a quadrotor. In particular, two numerical simulations addressing human-robot interaction and operation in dynamic environments and two real-world applications dealing with position control demonstrate a significant improvement in control performance, with solution times in the range of micro- and milliseconds.

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    ZENODO
    Other literature type . 2021
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      ZENODO
      Other literature type . 2021
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    Authors: Berg, Amanda;

    Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing camera price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as there exists a measurable temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of automatic image analysis in thermal infrared images with a focus on machine learning methods. The main purpose of this thesis is to study the variations of processing required due to the thermal infrared data modality. In particular, three different problems are addressed: visual object tracking, anomaly detection, and modality transfer. All these are research areas that have been and currently are subject to extensive research. Furthermore, they are all highly relevant for a number of different real-world applications. The first addressed problem is visual object tracking, a problem for which no prior information other than the initial location of the object is given. The main contribution concerns benchmarking of short-term single-object (STSO) visual object tracking methods in thermal infrared images. The proposed dataset, LTIR (Linköping Thermal Infrared), was integrated in the VOT-TIR2015 challenge, introducing the first ever organized challenge on STSO tracking in thermal infrared video. Another contribution also related to benchmarking is a novel, recursive, method for semi-automatic annotation of multi-modal video sequences. Based on only a few initial annotations, a video object segmentation (VOS) method proposes segmentations for all remaining frames and difficult parts in need for additional manual annotation are automatically detected. The third contribution to the problem of visual object tracking is a template tracking method based on a non-parametric probability density model of the object's thermal radiation using channel representations. The second addressed problem is anomaly detection, i.e., detection of rare objects or events. The main contribution is a method for truly unsupervised anomaly detection based on Generative Adversarial Networks (GANs). The method employs joint training of the generator and an observation to latent space encoder, enabling stratification of the latent space and, thus, also separation of normal and anomalous samples. The second contribution is the previously unaddressed problem of obstacle detection in front of moving trains using a train-mounted thermal camera. Adaptive correlation filters are updated continuously and missed detections of background are treated as detections of anomalies, or obstacles. The third contribution to the problem of anomaly detection is a method for characterization and classification of automatically detected district heat leakages for the purpose of false alarm reduction. Finally, the thesis addresses the problem of modality transfer between thermal infrared and visual spectrum images, a previously unaddressed problem. The contribution is a method based on Convolutional Neural Networks (CNNs), enabling perceptually realistic transformations of thermal infrared to visual images. By careful design of the loss function the method becomes robust to image pair misalignments. The method exploits the lower acuity for color differences than for luminance possessed by the human visual system, separating the loss into a luminance and a chrominance part.

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    ZENODO
    Other literature type . Book . 2019 . Peer-reviewed
    License: CC BY NC
    Data sources: ZENODO; Crossref
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      ZENODO
      Other literature type . Book . 2019 . Peer-reviewed
      License: CC BY NC
      Data sources: ZENODO; Crossref
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    Authors: Lukas Wille;

    Pea (Pisum sativum L.) is a valuable and healthy protein source for food and feed. In addition to the nutritional benefits, pea is an invaluable agro-ecological asset for sustainable cropping systems through positive effects on soil fertility and soil microbial diversity. The symbiosis with nitrogen-fixing bacteria allows pea and other legume crops to supply the soil with nitrogen and, therefore, to significantly reduce the application of external nitrogen fertilisers. Therefore, pea plays an important role especially in low-input farming systems. The growing market for plant- based protein supply is likely to promote pea cultivation in the near future. However, pea production is severely challenged by various soil-borne pathogens that form a Pea Root Rot Complex (PRRC) causing root-rot diseases. Despite considerable progress in resistance breeding against individual pathogens, current pea varieties lack resistance against multiple interacting pathogens. The overall goal of this thesis was to contribute to the understanding of resistance against root rot pathogen complexes in pea. Chapter 1 gives an overview of the importance of pea as a future key player in agricultural systems and the food sector before introducing the pea root rot complex concept and its relevance for research on resistance. Furthermore, the most recent developments in molecular biology relevant for molecular plant breeding of pea are briefly summarised and an overview of quantitative real-time PCR relevant for research on microbial interactions in the pea root rot complex is given. Chapter 2 reviews the current knowledge of resistance against root- rot pathogens in major grain legumes, highlights the importance of the host genotype in determining the composition of plant-associated microbial communities and how the root associated microbiome relates to plant health. In addition, major findings on the role of root exudation in disease susceptibility and resistance of grain legumes are summarised. Finally, it delineates how this knowledge could be integrated in resistance breeding of grain legumes. In Chapter 3, a resistance screening assay was established based on infested soil from an agricultural field that showed severe pea root rot pressure. This approach was chosen in order to account for the whole rhizosphere microbiome - including the naturally occuring pathogen complex - in the assessment of root rot resistance in pea. The initial ITS- amplicon sequencing of the fungal rhizosphere community of diseased pea roots grown in the infested soil showed a root community of evenly abundant fungal taxonomic units not dominated by a few taxa. This finding points at complex interactions within the PRRC. Two hundred and sixty-one pea cultivars, landraces and breeding lines were screened for resistance on the naturally infested field soil in a controlled conditions experiment. The screening system allowed for a reproducible assessment of disease parameters among the tested genotypes. Broad sense heritabilities on the infested soil were H2 = 0.89 for plant emergence, H2 = 0.43 for root rot index and H2 = 0.51 for relative shoot dry weight. The resistance ranking was verified in an on-farm experiment with nine pea genotypes in two field sites: The controlled conditions root rot index showed a significant correlation with the resistance ranking in the field site with high PRRC infestation (Spearman's ρ = 0.73, p = .03). The screening system offers a tool for selection at early stages of the plant development, and for the study of plant resistance in the light of complex plant-microbe interactions. For Chapter 4, a subset of five resistant and three susceptible pea genotypes was selected based on the initial screening. In analogy to the previous experiment, a controlled conditions experiment was setup up in order to assess and validate resistance of the eight pea genotypes on four soils. Plant growth was significantly reduced on the three sick soils compared to the healthy soil. Despite the significantly different levels of disease pressure in the three infested soils (ANOVA: p < .001) and the strong genotype effect (p < .001), no significant soil × genotype interaction (p < .342) was found for plant growth reduction. In addition to disease assessments, ten key microbial taxa (eight putative pea pathogens and two putative beneficials) were quantified in the roots by quantitative real-time PCR (qPCR). Fusarium solani, F. oxysporum and Aphanomyces euteiches were the most abundant pathogens in diseased roots from the three sick soils. Further, various levels of the pathogens F. avenaceum, F. redolens, Rhizoctonia solani, D. pinodella and Pythium sp. as well as the potential antagonist Clonostachys rosea were quantified by qPCR. The contribution of individual pathogens to root rot and growth reduction differed among the three sick soils: F. solani and F. oxysporum showed significant correlations (Spearman correlations; p < 0.05) with root rot index and relative shoot dry weight in the two soils with the highest infestation level; A. euteiches showed significant relations with disease in two sick soils from Germany. The quantities of arbuscular mycorrhizal fungi were negatively correlated with root rot index and positively correlated with relative shoot dry weight in all sick soils. Furthermore, the root microbial composition differed significantly among the pea genotypes (PERMANOVA; p < .0001) and the soils (p < .0001) and a significant pea genotype × soil interaction was evidenced (p < .0001). In addition, resistant pea genotypes showed significantly lower F. solani and A. euteiches, and higher arbuscular mycorrhizal fungi abundance in the roots (Wilcoxon rank-sum test; p < .05). These results give insights into the complex interaction between key microorganisms of the PRRC and the plant, by pointing out potential key microorganisms in the root rot pathobiome. Further disentanglement of this complex and the validation of key microbial players can be harnessed by resistance breeding. Chapter 5 reviews the experimental approaches and results from the previous chapters before discussing the major findings and implications for future research and resistance breeding. I also raise the question if and how knowledge about complex soil microorganisms-plant feedbacks can be incorporated in resistance screenings and breeding efforts to conclude that today we are at a point where information on microbial complexes could indeed assist resistance breeding. However, our current state of knowledge does not yet allow to design specific microbiome-enabled selection-tools. This last chapter will also give short outlooks and indicate possible future lines of research in the field of microbe-mediated plant resistance.

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    Research Collection
    Doctoral thesis . 2020
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      Research Collection
      Doctoral thesis . 2020
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    Authors: Grippa, Taïs;

    Nowadays, in sub-Saharan Africa (SSA), about 40% of the population is urban and this region is expected to face the highest growth rates during the next decades. By 2100, the three most populated cities in the world will be located in SSA. As a consequence of the extremely fast transformations experienced during the last decades, SSA cities are facing social and environmental issues combined with a lack of financial means and capacity in urban planning and management. The poorest often constitute a large part of the urban population that is extremely vulnerable to health and disaster risks.In SSA cities, up-to-date and spatially detailed geographic information is often missing. This lack of information is an important issue for many scientific studies focusing on different urban issues and there is a real need to improve the availability of geoinformation for these cities in order to support urban planning, urban management, environment monitoring, epidemiology or risk assessment, etc. The work presented in this thesis aims to develop different frameworks for the production of geoinformation. For this purpose, advantage is taken of Very-High Resolution Remote Sensing imagery (0.5 meters) and open-source software. These frameworks implement cutting-edge methods and can handle a large amount of data in a semi-automated fashion to produce maps covering very large areas of interest. In the spirit of open science, the processing chains are entirely based on open-source software and are released publicly in open-access for any interested researchers, in order to make the methods developed completely transparent and in order to contribute to the creation of a pool of common tools and scientific knowledge. These frameworks are used to produce very detailed land-cover and land-use maps that provide essential information such as the built-up density, or the fact that a neighborhood is residential or not. This detailed geoinformation is then used as indicators of presence of populated places to improve existing population models at the intra-urban level. Option Géographie du Doctorat en Sciences info:eu-repo/semantics/nonPublished

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    Doctoral thesis . 2019
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    ZENODO
    Other literature type . 2019
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      Doctoral thesis . 2019
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      Other literature type . 2019
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    Authors: Bárbara Barros Carlos;

    Robotics has revolutionized several industries across the globe through technologies never seen before. However, society still expects more than what today’s robots are really capable of. To deal with such expectations, innovative algorithms must be translated into viable solutions so that robots can continue to improve labor in workplaces and ordinary people’s lives. Among the most important are motion generation algorithms, whose advancements can solve some of the biggest barriers towards more flexible and safe human-robot interactions and operations in dynamic environments. The developments in optimization algorithms and computer processor technology have led nonlinear model predictive control (NMPC) to gain popularity in robotics as a motion generation technique. The main reason for that is its ability to minimize a cost function while respecting a set of constraints that typically represent the system’s physical and operational limitations. Ultimately, these are the elements that grant NMPC an improved performance compared to classic approaches. Despite being a promising technique, the non-negligible computational burden associated with the online solution of the underlying optimal control problems has decisively limited its roll-out to robotic systems subject to short sampling times and resource-constrained hardware. This thesis proposes multiple tailored algorithms for real-time motion generation in robotic systems based on high-performance implementations of NMPC. The primary computational bottlenecks concern the numerical simulation of the continuous-time nonlinear dynamic models and the online solution of the stemming large yet well-structured nonlinear program. The thesis shows that it is possible to achieve significant speed-ups in solution times while preserving nonlinearities through efficient software implementations that cover standard building blocks from nonlinear programming, tailored quadratic programming solvers, and fast approximate schemes for NMPC. Additionally, a discussion is provided in terms of dynamic modeling. It encompasses the design decisions required to create a model that ex- poses the system limitations so that high-quality motions can be attained due to a more accurate representation. Among the ever-growing plethora of robotic systems, the thesis focuses on the motion generation of a double inverted pendulum and a quadrotor. In particular, two numerical simulations addressing human-robot interaction and operation in dynamic environments and two real-world applications dealing with position control demonstrate a significant improvement in control performance, with solution times in the range of micro- and milliseconds.

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    Authors: Berg, Amanda;

    Thermal cameras have historically been of interest mainly for military applications. Increasing image quality and resolution combined with decreasing camera price and size during recent years have, however, opened up new application areas. They are now widely used for civilian applications, e.g., within industry, to search for missing persons, in automotive safety, as well as for medical applications. Thermal cameras are useful as soon as there exists a measurable temperature difference. Compared to cameras operating in the visual spectrum, they are advantageous due to their ability to see in total darkness, robustness to illumination variations, and less intrusion on privacy. This thesis addresses the problem of automatic image analysis in thermal infrared images with a focus on machine learning methods. The main purpose of this thesis is to study the variations of processing required due to the thermal infrared data modality. In particular, three different problems are addressed: visual object tracking, anomaly detection, and modality transfer. All these are research areas that have been and currently are subject to extensive research. Furthermore, they are all highly relevant for a number of different real-world applications. The first addressed problem is visual object tracking, a problem for which no prior information other than the initial location of the object is given. The main contribution concerns benchmarking of short-term single-object (STSO) visual object tracking methods in thermal infrared images. The proposed dataset, LTIR (Linköping Thermal Infrared), was integrated in the VOT-TIR2015 challenge, introducing the first ever organized challenge on STSO tracking in thermal infrared video. Another contribution also related to benchmarking is a novel, recursive, method for semi-automatic annotation of multi-modal video sequences. Based on only a few initial annotations, a video object segmentation (VOS) method proposes segmentations for all remaining frames and difficult parts in need for additional manual annotation are automatically detected. The third contribution to the problem of visual object tracking is a template tracking method based on a non-parametric probability density model of the object's thermal radiation using channel representations. The second addressed problem is anomaly detection, i.e., detection of rare objects or events. The main contribution is a method for truly unsupervised anomaly detection based on Generative Adversarial Networks (GANs). The method employs joint training of the generator and an observation to latent space encoder, enabling stratification of the latent space and, thus, also separation of normal and anomalous samples. The second contribution is the previously unaddressed problem of obstacle detection in front of moving trains using a train-mounted thermal camera. Adaptive correlation filters are updated continuously and missed detections of background are treated as detections of anomalies, or obstacles. The third contribution to the problem of anomaly detection is a method for characterization and classification of automatically detected district heat leakages for the purpose of false alarm reduction. Finally, the thesis addresses the problem of modality transfer between thermal infrared and visual spectrum images, a previously unaddressed problem. The contribution is a method based on Convolutional Neural Networks (CNNs), enabling perceptually realistic transformations of thermal infrared to visual images. By careful design of the loss function the method becomes robust to image pair misalignments. The method exploits the lower acuity for color differences than for luminance possessed by the human visual system, separating the loss into a luminance and a chrominance part.

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    Authors: Lukas Wille;

    Pea (Pisum sativum L.) is a valuable and healthy protein source for food and feed. In addition to the nutritional benefits, pea is an invaluable agro-ecological asset for sustainable cropping systems through positive effects on soil fertility and soil microbial diversity. The symbiosis with nitrogen-fixing bacteria allows pea and other legume crops to supply the soil with nitrogen and, therefore, to significantly reduce the application of external nitrogen fertilisers. Therefore, pea plays an important role especially in low-input farming systems. The growing market for plant- based protein supply is likely to promote pea cultivation in the near future. However, pea production is severely challenged by various soil-borne pathogens that form a Pea Root Rot Complex (PRRC) causing root-rot diseases. Despite considerable progress in resistance breeding against individual pathogens, current pea varieties lack resistance against multiple interacting pathogens. The overall goal of this thesis was to contribute to the understanding of resistance against root rot pathogen complexes in pea. Chapter 1 gives an overview of the importance of pea as a future key player in agricultural systems and the food sector before introducing the pea root rot complex concept and its relevance for research on resistance. Furthermore, the most recent developments in molecular biology relevant for molecular plant breeding of pea are briefly summarised and an overview of quantitative real-time PCR relevant for research on microbial interactions in the pea root rot complex is given. Chapter 2 reviews the current knowledge of resistance against root- rot pathogens in major grain legumes, highlights the importance of the host genotype in determining the composition of plant-associated microbial communities and how the root associated microbiome relates to plant health. In addition, major findings on the role of root exudation in disease susceptibility and resistance of grain legumes are summarised. Finally, it delineates how this knowledge could be integrated in resistance breeding of grain legumes. In Chapter 3, a resistance screening assay was established based on infested soil from an agricultural field that showed severe pea root rot pressure. This approach was chosen in order to account for the whole rhizosphere microbiome - including the naturally occuring pathogen complex - in the assessment of root rot resistance in pea. The initial ITS- amplicon sequencing of the fungal rhizosphere community of diseased pea roots grown in the infested soil showed a root community of evenly abundant fungal taxonomic units not dominated by a few taxa. This finding points at complex interactions within the PRRC. Two hundred and sixty-one pea cultivars, landraces and breeding lines were screened for resistance on the naturally infested field soil in a controlled conditions experiment. The screening system allowed for a reproducible assessment of disease parameters among the tested genotypes. Broad sense heritabilities on the infested soil were H2 = 0.89 for plant emergence, H2 = 0.43 for root rot index and H2 = 0.51 for relative shoot dry weight. The resistance ranking was verified in an on-farm experiment with nine pea genotypes in two field sites: The controlled conditions root rot index showed a significant correlation with the resistance ranking in the field site with high PRRC infestation (Spearman's ρ = 0.73, p = .03). The screening system offers a tool for selection at early stages of the plant development, and for the study of plant resistance in the light of complex plant-microbe interactions. For Chapter 4, a subset of five resistant and three susceptible pea genotypes was selected based on the initial screening. In analogy to the previous experiment, a controlled conditions experiment was setup up in order to assess and validate resistance of the eight pea genotypes on four soils. Plant growth was significantly reduced on the three sick soils compared to the healthy soil. Despite the significantly different levels of disease pressure in the three infested soils (ANOVA: p < .001) and the strong genotype effect (p < .001), no significant soil × genotype interaction (p < .342) was found for plant growth reduction. In addition to disease assessments, ten key microbial taxa (eight putative pea pathogens and two putative beneficials) were quantified in the roots by quantitative real-time PCR (qPCR). Fusarium solani, F. oxysporum and Aphanomyces euteiches were the most abundant pathogens in diseased roots from the three sick soils. Further, various levels of the pathogens F. avenaceum, F. redolens, Rhizoctonia solani, D. pinodella and Pythium sp. as well as the potential antagonist Clonostachys rosea were quantified by qPCR. The contribution of individual pathogens to root rot and growth reduction differed among the three sick soils: F. solani and F. oxysporum showed significant correlations (Spearman correlations; p < 0.05) with root rot index and relative shoot dry weight in the two soils with the highest infestation level; A. euteiches showed significant relations with disease in two sick soils from Germany. The quantities of arbuscular mycorrhizal fungi were negatively correlated with root rot index and positively correlated with relative shoot dry weight in all sick soils. Furthermore, the root microbial composition differed significantly among the pea genotypes (PERMANOVA; p < .0001) and the soils (p < .0001) and a significant pea genotype × soil interaction was evidenced (p < .0001). In addition, resistant pea genotypes showed significantly lower F. solani and A. euteiches, and higher arbuscular mycorrhizal fungi abundance in the roots (Wilcoxon rank-sum test; p < .05). These results give insights into the complex interaction between key microorganisms of the PRRC and the plant, by pointing out potential key microorganisms in the root rot pathobiome. Further disentanglement of this complex and the validation of key microbial players can be harnessed by resistance breeding. Chapter 5 reviews the experimental approaches and results from the previous chapters before discussing the major findings and implications for future research and resistance breeding. I also raise the question if and how knowledge about complex soil microorganisms-plant feedbacks can be incorporated in resistance screenings and breeding efforts to conclude that today we are at a point where information on microbial complexes could indeed assist resistance breeding. However, our current state of knowledge does not yet allow to design specific microbiome-enabled selection-tools. This last chapter will also give short outlooks and indicate possible future lines of research in the field of microbe-mediated plant resistance.

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    Doctoral thesis . 2020
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      Doctoral thesis . 2020
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    Authors: Grippa, Taïs;

    Nowadays, in sub-Saharan Africa (SSA), about 40% of the population is urban and this region is expected to face the highest growth rates during the next decades. By 2100, the three most populated cities in the world will be located in SSA. As a consequence of the extremely fast transformations experienced during the last decades, SSA cities are facing social and environmental issues combined with a lack of financial means and capacity in urban planning and management. The poorest often constitute a large part of the urban population that is extremely vulnerable to health and disaster risks.In SSA cities, up-to-date and spatially detailed geographic information is often missing. This lack of information is an important issue for many scientific studies focusing on different urban issues and there is a real need to improve the availability of geoinformation for these cities in order to support urban planning, urban management, environment monitoring, epidemiology or risk assessment, etc. The work presented in this thesis aims to develop different frameworks for the production of geoinformation. For this purpose, advantage is taken of Very-High Resolution Remote Sensing imagery (0.5 meters) and open-source software. These frameworks implement cutting-edge methods and can handle a large amount of data in a semi-automated fashion to produce maps covering very large areas of interest. In the spirit of open science, the processing chains are entirely based on open-source software and are released publicly in open-access for any interested researchers, in order to make the methods developed completely transparent and in order to contribute to the creation of a pool of common tools and scientific knowledge. These frameworks are used to produce very detailed land-cover and land-use maps that provide essential information such as the built-up density, or the fact that a neighborhood is residential or not. This detailed geoinformation is then used as indicators of presence of populated places to improve existing population models at the intra-urban level. Option Géographie du Doctorat en Sciences info:eu-repo/semantics/nonPublished

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