Urban areas around the world are changing constantly and therefore it is necessary to update urban land cover maps regularly. Remote sensing techniques have been used to monitor changes and update land-use/land-cover information in urban areas for decades. Optical imaging systems have received most of the attention in urban studies. The development of SAR applications in urban monitoring has been accelerated with more and more advanced SAR systems operating in space.   This research investigated object-based and rule-based classification methodologies for extracting urban land-cover information from high resolution SAR data. The study area is located in the north and northwest part of the Greater Toronto Area (GTA), Ontario, Canada, which has been undergoing rapid urban growth during the past decades. Five-date RADARSAT-1 fine-beam C-HH SAR images with a spatial resolution of 10 meters were acquired during May to August in 2002. Three-date RADARSAT-2 ultra-fine-beam C-HH SAR images with a spatial resolution of 3 meters were acquired during June to September in 2008.   SAR images were pre-processed and then segmented using multi-resolution segmentation algorithm. Specific features such as geometric and texture features were selected and calculated for image objects derived from the segmentation of SAR images. Both neural network (NN) and support vector machines (SVM) were investigated for the supervised classification of image objects of RADARSAT-1 SAR images, while SVM was employed to classify image objects of RADARSAT-2 SAR images. Knowledge-based rules were developed and applied to resolve the confusion among some classes in the object-based classification results.   The classification of both RADARSAT-1 and RADARSAT-2 SAR images yielded relatively high accuracies (over 80%). SVM classifier generated better result than NN classifier for the object-based supervised classification of RADARSAT-1 SAR images. Well-designed knowledge-based rules could increase the accuracies of some classes after the object-based supervised classification. The comparison of the classification results of RADARSAT-1 and RADARSAT-2 SAR images showed that SAR images with higher resolution could reveal more details, but might produce lower classification accuracies for certain land cover classes due to the increasing complexity of the images. Overall, the classification results indicate that the proposed object-based and rule-based approaches have potential for operational urban land cover mapping from high-resolution space borne SAR images. / QC 20101209

The return of objects that belong to ethnographic collections to their places of origin is one of the topics of discussion that, despite not being new, has been gaining more and more relevance today. Taking the Iku indigenous craft collection in the archives of the National Museum of World Cultures in Gothenburg as a case study, I pursue to develop an object-based methodology that increases and deepens the understanding of the notion of ethical stewardship, while joining current debates on indigenous heritage and decoloniality. This work aims to reveal material and immaterial aspects embedded in textile objects. The methodology included field visits to the museum archive, material culture analysis, and semi-structured interviews. The work evokes a decolonial discussion regarding the need to engage with epistemologies from the “South” and with methodologies not fully recognized by the dominant western-modern educational frameworks in order to achieve a more inclusive and assertive production of knowledge. / La restitución de colecciones etnográficas a sus lugares de origen es uno de los temas de discusión que, a pesar de no ser nueva, ha ido cobrando cada vez más relevancia en la actualidad. Tomando la colección de artesanía indígena Iku/arhuaca en los archivos del Museo Nacional de las Culturas del Mundo en Gotemburgo como estudio de caso, busco desarrollar una metodología basada en objetos que aumente y profundice la comprensión de la noción de administración ética, mientras me sumo a los debates actuales sobre patrimonio indígena y decolonialidad. Este trabajo tiene como objetivo revelar los aspectos materiales e inmateriales incrustados en los objetos textiles. La metodología incluyó visitas de campo al archivo del museo, análisis de cultura material y entrevistas semiestructuradas. El trabajo evoca una discusión decolonial sobre la necesidad de involucrar epistemologías del “Sur” y metodologías no plenamente reconocidas por los marcos educativos occidentales-modernos dominantes para lograr una producción de conocimiento más inclusiva y asertiva.

Land managers need to rapidly assess vegetation composition and bare ground to effectively evaluate, manage, and restore shrub steppe communities that have been encroached by pinyon and juniper (P-J) trees. A major part of this process is assessing where to apply mechanical and prescribed fire treatments to reduce fuel loads and maintain or restore sagebrush steppe rangelands. Geospatial technologies, particularly remote sensing, offers an efficient option to assess rangelands across multiple spatial scales while reducing the need for ground-based sampling measurements. High-spatial resolution color-infrared imagery (0.06-m pixels) was acquired for sagebrush steppe communities invaded by P-J trees at five sites in Oregon, California, Nevada, and Utah with a Vexcel Ultra CamX digital camera in June/July 2009. In addition to untreated P-J woodlands, imagery was acquired over P-J woodlands where fuels were reduced by either prescribed fire, tree cutting, or mastication treatments. Ground measurements were simultaneously collected at each site in 2009 on 0.1-hectare subplots as part of the Sagebrush Steppe Treatment Evaluation Project (SageSTEP). We used Trimble eCognition Developer to 1) develop efficient methods to estimate land cover classes found in P-J woodlands; 2) determine the relationship between ground measurements and object-based image analysis (OBIA) land cover measurements for the following classes: trees (live, burned, cut, and masticated), shrubs, perennial herbaceous vegetation, litter (including annual species), and bare ground; and 3) evaluate eCognition rule-sets (models) across four spatial scales (subplot, site, region, and network) using untreated P-J woodland imagery. At the site scale, the overall accuracy of our thematic maps for untreated P-J woodlands was 84% with a kappa statistic of 0.80. For treatments, the overall accuracy and kappa statistic for prescribed fire was 85% and 0.81; cut and fell 82% and 0.77, and mastication 84% and 0.80, respectively, each indicating strong agreement between OBIA classification and ground measured data. Differences between mean cover estimates using OBIA and ground-measurements were not consistently higher or lower for any land cover class and when evaluated for individual sites, were within 5% of each other; all regional and network OBIA mean cover estimates were within 10% of the ground measurements. The trade-off for decreased precision over a larger area (region and network scale) may be useful to prioritize fuel-management strategies but will unlikely capture subtle shifts in understory plant communities that site and subplot spatial scales often capture. Although cover assessments from OBIA differed somewhat from ground measurements, they were accurate enough for many landscape-assessment applications such as evaluating treatment success and assessing the spatial distribution of fuels following fuel-reduction treatments on a site scale.

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

The intensification of aridity due to anthropogenic climate change is likely to have a large impact on the growth and survival of plant species in the southwestern U.S. where species are already vulnerable to high temperatures and limited precipitation. Global climate change impacts plants through a rising temperature effect, CO2 effect, and land management. In order to forecast the impacts of global climate change, it is necessary to know the current conditions and create a baseline for future comparisons and to understand the factors and players that will affect what happens in the future. The objective of Chapter 1 is to create the very first high resolution, accurate, park-wide map that shows the distribution of dominant plants on the Colorado Plateau and serves as a baseline for future comparisons of species distribution. If we are going to forecast what species have already been impacted by global change or will likely be impacted in the future, we need to know their physiology. Chapter 2 surveys the physiology of the twelve most abundant non-tree species on the Colorado Plateau to help us forecast what climate change might do and to understand what has likely already occurred. Chapter 1. Our objective was to create an accurate species-level classification map using a combination of multispectral data from the World View-3 satellite and hyperspectral data from a handheld radiometer to compare pixel-based and object-based classification. We found that overall, both methods were successful in creating an accurate landscape map. Different functional types could be classified with fairly good accuracy in a pixel-based classification but to get more accurate species-level classification, object-based methods were more effective (0.915, kappa coefficient=0.905) than pixel-based classification (0.79, kappa coefficient=0.766). Although spectral reflectance values were important in classification, the addition of other features such as brightness, texture, number of pixels, size, shape, compactness, and asymmetry improved classification accuracy.Chapter 2. We sought to understand if patterns of gas exchange to changes in temperature and CO2 can explain why C3 shrubs are increasing, and C3 and C4 grasses are decreasing in the southwestern U.S. We conducted seasonal, leaf-level gas exchange surveys, and measured temperature response curves and A-Ci response curves of common shrub, forb, and grass species in perennial grassland ecosystems over the year. We found that the functional trait of being evergreen is increasingly more successful in climate changing conditions with warmer winter months. Grass species in our study did not differentiate by photosynthetic pathway; they were physiologically the same in all of our measurements. Increasing shrub species, Ephedra viridis and Coleogyne ramosissima displayed functional similarities in response to increasing temperature and CO2.

Universally across modern cultures children acquire the meaning of the words one, two, and three in order. While much research has focused on how children acquire this knowledge and what this knowledge represents, the question of why children learn numbers in order has been comparatively neglected. To address this question, a non-verbal anticipatory looking task was implemented. In this task, 35 14- to 23-month-old infants were assessed on their ability to form implicit category structures for the numbers one, two, and three. We hypothesized that children would be able to form the implicit category structure for the number one but not for two or three because sets of two and three objects would exceed the working memory capacities of infants. We found results consistent with this hypothesis; infants (regardless of age) were able form a category for sets with one object, as evidenced by their looking behavior while the looking behavior for the numbers two and three did not demonstrate a statistically significant pattern. We interpret our results as consistent with our hypothesis and discuss implications for parallel individuation, number acquisition theories, and the development of working memory resources.

Creating the optimal balance between dialogue level and ambient sound is extremely important in media productions. This process is however inherently difficult due to that people’s requirements and preferences are not uniform. Speech intelligibility is affected by a multitude of factors, such as hearing impairments, audio quality and listening equipment. Recent EU directives on accessibility calls for improved audio clarity features for broadcast content. To accommodate these requirements, the broadcast industry needs to develop functionality for enhanced dialogue clarity and, optimally, put listeners in control of these features. Many speech enhancement techniques exist, this paper uses Sveriges Radio as a case study to evaluate several of these methods. A study on enhancing speech intelligibility through the use of dynamic range control and blind source separation is presented and results show that both methods can have a positive impact. Dynamic range control proves efficient in increasing intelligibility by reducing dynamic variations. It is also well suited to implement in an existing two-channel infrastructure, common in the radio industry, due to being included in novel audio codecs. Blind source separation is found to best be used in moderation due to the risk of audio quality degradation, and is primarily suited for prerecorded material on account of the processing time needed. / Att skapa en optimal balans mellan dialognivå och bakgrundsljud är oerhört viktigt i medieproduktioner. Detta är dock i sig komplicerat på grund av människors olika förutsättningar och preferenser. Taluppfattbarheten påverkas av en mängd faktorer, såsom hörselnedsättningar, ljudkvalitet och lyssningsutrustning. Nya EU-direktiv om tillgänglighet ställer krav på förbättrade funktioner för ljudtydlighet i etermedia. För att tillgodose dessa krav behöver branschen utveckla funktionalitet för ökad dialogtydlighet och företrädesvis också stöd för att lyssnarna själva ska kunna styra dessa funktioner. Det finns många tekniker för att öka taluppfattbarheten, denna artikel använder Sveriges Radio som fallstudie för att utvärdera flera av dessa metoder. En studie om möjligheten till förbättrad taluppfattbarhet genom kontroll av ljudets dynamik (dynamic range control) och blindkallseparation (blind source separation) presenteras, och resultaten visar att båda metoderna kan ha en positiv inverkan. Dynamisk kontroll visar sig vara effektiv för att öka taluppfattbarheten genom att jämna ut dynamiskt innehåll. Tekniken lämpar sig också bra för implementering i en befintlig tvåkanals-infrastruktur, vilket är vanligt inom radioindustrin, på grund av att den inkluderas i nya ljud-kodekar. Blindkallseparation används bäst med måtta på grund av risk for ljudkvalitetsförsämring och är i första hand lämpad för förinspelat material på grund av den processeringstid som behövs.

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[pt] A sísmica é uma importante ferramenta utilizada no processo de exploração de petróleo e gás natural. A partir dos estudos sísmicos é possível obter informações referentes a probabilidade de encontrar situações favoráveis a acumulação de hidrocarbonetos. O presente trabalho visa integrar os dados adquiridos através deste método geofísico a um modelo de simulação de canais baseados em esqueletos em um ambiente deposicional turbidítico, e também apresentar a modelagem de tais canais condicionados a localização de um poço. / [en] The use of seismic data is an important tool in oil and gas research. It can show us the probability of having a high concentration of hydrocarbon in a possible reservoir. This work intends to condition skeleton-based modeling of channels reservoir in a turbidite system to seismic data. We also present such modeling process constraint by a well previously defined.

As Machine Learning (ML) methods have gained traction in recent years, someproblems regarding the construction of such methods have arisen. One such problem isthe collection and labeling of data sets. Specifically when it comes to many applicationsof Computer Vision (CV), one needs a set of images, labeled as either being of someclass or not. Creating such data sets can be very time consuming. This project setsout to tackle this problem by constructing an end-to-end system for searching forobjects in images (i.e. an Object Based Image Retrieval (OBIR) method) using an objectdetection framework (You Only Look Once (YOLO) [16]). The goal of the project wasto create a method that; given an image of an object of interest q, search for that sameor similar objects in a set of other images S. The core concept of the idea is to passthe image q through an object detection model (in this case YOLOv5 [16]), create a”fingerprint” (can be seen as a sort of identity for an object) from a set of feature mapsextracted from the YOLOv5 [16] model and look for corresponding similar parts of aset of feature maps extracted from other images. An investigation regarding whichvalues to select for a few different parameters was conducted, including a comparisonof performance for a couple of different similarity metrics. In the table below,the parameter combination which resulted in the highest F_Top_300-score (a measureindicating the amount of relevant images retrieved among the top 300 recommendedimages) in the parameter selection phase is presented. Layer: 23Pool Methd: maxSim. Mtrc: eucFP Kern. Sz: 4 Evaluation of the method resulted in F_Top_300-scores as can be seen in the table below. Mouse: 0.820Duck: 0.640Coin: 0.770Jet ski: 0.443Handgun: 0.807Average: 0.696 / Medan ML-metoder har blivit mer populära under senare år har det uppstått endel problem gällande konstruktionen av sådana metoder. Ett sådant problem ärinsamling och annotering av data. Mer specifikt när det kommer till många metoderför datorseende behövs ett set av bilder, annoterande att antingen vara eller inte varaav en särskild klass. Att skapa sådana dataset kan vara väldigt tidskonsumerande.Metoden som konstruerades för detta projekt avser att bekämpa detta problem genomatt konstruera ett end-to-end-system för att söka efter objekt i bilder (alltså en OBIR-metod) med hjälp av en objektdetekteringsalgoritm (YOLO). Målet med projektet varatt skapa en metod som; givet en bild q av ett objekt, söka efter samma eller liknandeobjekt i ett bibliotek av bilder S. Huvudkonceptet bakom idén är att köra bilden qgenom objektdetekteringsmodellen (i detta fall YOLOv5 [16]), skapa ett ”fingerprint”(kan ses som en sorts identitet för ett objekt) från en samling feature maps extraheradefrån YOLOv5-modellen [16] och leta efter liknande delar av samlingar feature maps iandra bilder. En utredning angående vilka värden som skulle användas för ett antalolika parametrar utfördes, inklusive en jämförelse av prestandan som resultat av olikalikhetsmått. I tabellen nedan visas den parameterkombination som gav högst F_Top_300(ett mått som indikerar andelen relevanta bilder bland de 300 högst rekommenderadebilderna). Layer: 23Pool Methd: maxSim. Mtrc: eucFP Kern. Sz: 4 Evaluering av metoden med parameterval enligt tabellen ovan resulterade i F_Top_300enligt tabellen nedan. Mouse: 0.820Duck: 0.640Coin: 0.770Jet ski: 0.443Handgun: 0.807Average: 0.696