1,660 research outputs found

    Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment : a review

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    Quantitative image analysis techniques have gained an undeniable role in several fields of research during the last decade. In the field of biological wastewater treatment (WWT) processes, several computer applications have been developed for monitoring microbial entities, either as individual cells or in different types of aggregates. New descriptors have been defined that are more reliable, objective, and useful than the subjective and time-consuming parameters classically used to monitor biological WWT processes. Examples of this application include the objective prediction of filamentous bulking, known to be one of the most problematic phenomena occurring in activated sludge technology. It also demonstrated its usefulness in classifying protozoa and metazoa populations. In high-rate anaerobic processes, based on granular sludge, aggregation times and fragmentation phenomena could be detected during critical events, e.g., toxic and organic overloads. Currently, the major efforts and needs are in the development of quantitative image analysis techniques focusing on its application coupled with stained samples, either by classical or fluorescent-based techniques. The use of quantitative morphological parameters in process control and online applications is also being investigated. This work reviews the major advances of quantitative image analysis applied to biological WWT processes.The authors acknowledge the financial support to the project PTDC/EBB-EBI/103147/2008 and the grant SFRH/BPD/48962/2008 provided by Fundacao para a Ciencia e Tecnologia (Portugal)

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Proceedings of the European Conference on Agricultural Engineering AgEng2021

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    This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

    Automated vision-based generation of event statistics for decision support

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    Many tasks require surveillance and analysis in order to make decisions regarding the next course of action. The people responsible for these tasks are usually concerned with any event that affects their bottom-line. Traditionally, human operators have had to either actively man a set of video displays to determine if specific events were occurring or manually review hours of collected video data to see if a specific event occurred. Actively monitoring video stream or manually reviewing and analyzing the data collected, however, is a tedious and long process which is prone to errors due to biases and inattention. Automatically processing and analyzing the video provides an alternate way of getting more accurate results because it can reduce the likelihood of missing important events and the human factors that lead to decreased efficiency. The thesis aims to contribute to the area of using computer vision as a decision support tool by integrating detector, tracker, re-identification, activity status estimation, and event processor modules to generate the necessary event statistics needed by a human operator. The contribution of this thesis is a system that uses feedback from each of the modules to provide better target detection, and tracking results for event statistics generation over an extended period of time. To demonstrate the efficacy of the proposed system, it is first used to generate event statistics that measure productivity on multiple construction work sites. The versatility of the proposed system is also demonstrated in an indoor assisted living environment by using it to determine how much of an influence a technology intervention had on promoting interactions amongst older adults in a shared space.Ph.D

    UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

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    Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

    A PHYSIOCRATIC SYSTEMS FRAMEWORK FOR OPEN SOURCE AGRICULTURAL RESEARCH AND DEVELOPMENT

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    This dissertation presents a new participatory approach to agricultural research and development. It surveys the biological, sociological, economic, and technical landscape and proposes a framework for adaptive management based on the 18th century Physiocratic school of land-based economics. Industrial specialization and heavy emphasis on deductive approaches to science have contributed to the disconnection of large portions of the population from natural systems. Conventional agriculture and agricultural research methods following this pattern have created expensive social, environmental, and economic external costs, while adaptive management and resilient agricultural systems have been hindered by the cost and complexity of quantifying environmental services. However, the convergence of low cost computing, sensors, memory, and resulting data analytic methods, combined with new collaborative tools and social media, have created an exciting open source environment with the potential to engage more people in analyzing and managing our natural environment

    Modélisation tridimensionnelle précise de l'environnement à l’aide des systèmes de photogrammétrie embarqués sur drones

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    Abstract : Images acquired from unmanned aerial vehicles (UAVs) can provide data with unprecedented spatial and temporal resolution for three-dimensional (3D) modeling. Solutions developed for this purpose are mainly operating based on photogrammetry concepts, namely UAV-Photogrammetry Systems (UAV-PS). Such systems are used in applications where both geospatial and visual information of the environment is required. These applications include, but are not limited to, natural resource management such as precision agriculture, military and police-related services such as traffic-law enforcement, precision engineering such as infrastructure inspection, and health services such as epidemic emergency management. UAV-photogrammetry systems can be differentiated based on their spatial characteristics in terms of accuracy and resolution. That is some applications, such as precision engineering, require high-resolution and high-accuracy information of the environment (e.g. 3D modeling with less than one centimeter accuracy and resolution). In other applications, lower levels of accuracy might be sufficient, (e.g. wildlife management needing few decimeters of resolution). However, even in those applications, the specific characteristics of UAV-PSs should be well considered in the steps of both system development and application in order to yield satisfying results. In this regard, this thesis presents a comprehensive review of the applications of unmanned aerial imagery, where the objective was to determine the challenges that remote-sensing applications of UAV systems currently face. This review also allowed recognizing the specific characteristics and requirements of UAV-PSs, which are mostly ignored or not thoroughly assessed in recent studies. Accordingly, the focus of the first part of this thesis is on exploring the methodological and experimental aspects of implementing a UAV-PS. The developed system was extensively evaluated for precise modeling of an open-pit gravel mine and performing volumetric-change measurements. This application was selected for two main reasons. Firstly, this case study provided a challenging environment for 3D modeling, in terms of scale changes, terrain relief variations as well as structure and texture diversities. Secondly, open-pit-mine monitoring demands high levels of accuracy, which justifies our efforts to improve the developed UAV-PS to its maximum capacities. The hardware of the system consisted of an electric-powered helicopter, a high-resolution digital camera, and an inertial navigation system. The software of the system included the in-house programs specifically designed for camera calibration, platform calibration, system integration, onboard data acquisition, flight planning and ground control point (GCP) detection. The detailed features of the system are discussed in the thesis, and solutions are proposed in order to enhance the system and its photogrammetric outputs. The accuracy of the results was evaluated under various mapping conditions, including direct georeferencing and indirect georeferencing with different numbers, distributions and types of ground control points. Additionally, the effects of imaging configuration and network stability on modeling accuracy were assessed. The second part of this thesis concentrates on improving the techniques of sparse and dense reconstruction. The proposed solutions are alternatives to traditional aerial photogrammetry techniques, properly adapted to specific characteristics of unmanned, low-altitude imagery. Firstly, a method was developed for robust sparse matching and epipolar-geometry estimation. The main achievement of this method was its capacity to handle a very high percentage of outliers (errors among corresponding points) with remarkable computational efficiency (compared to the state-of-the-art techniques). Secondly, a block bundle adjustment (BBA) strategy was proposed based on the integration of intrinsic camera calibration parameters as pseudo-observations to Gauss-Helmert model. The principal advantage of this strategy was controlling the adverse effect of unstable imaging networks and noisy image observations on the accuracy of self-calibration. The sparse implementation of this strategy was also performed, which allowed its application to data sets containing a lot of tie points. Finally, the concepts of intrinsic curves were revisited for dense stereo matching. The proposed technique could achieve a high level of accuracy and efficiency by searching only through a small fraction of the whole disparity search space as well as internally handling occlusions and matching ambiguities. These photogrammetric solutions were extensively tested using synthetic data, close-range images and the images acquired from the gravel-pit mine. Achieving absolute 3D mapping accuracy of 11±7 mm illustrated the success of this system for high-precision modeling of the environment.Résumé : Les images acquises à l’aide d’aéronefs sans pilote (ASP) permettent de produire des données de résolutions spatiales et temporelles uniques pour la modélisation tridimensionnelle (3D). Les solutions développées pour ce secteur d’activité sont principalement basées sur des concepts de photogrammétrie et peuvent être identifiées comme des systèmes photogrammétriques embarqués sur aéronefs sans pilote (SP-ASP). Ils sont utilisés dans plusieurs applications environnementales où l’information géospatiale et visuelle est essentielle. Ces applications incluent notamment la gestion des ressources naturelles (ex. : agriculture de précision), la sécurité publique et militaire (ex. : gestion du trafic), les services d’ingénierie (ex. : inspection de bâtiments) et les services de santé publique (ex. : épidémiologie et gestion des risques). Les SP-ASP peuvent être subdivisés en catégories selon les besoins en termes de précision et de résolution. En effet, dans certains cas, tel qu’en ingénierie, l’information sur l’environnement doit être de haute précision et de haute résolution (ex. : modélisation 3D avec une précision et une résolution inférieure à un centimètre). Pour d’autres applications, tel qu’en gestion de la faune sauvage, des niveaux de précision et de résolution moindres peut être suffisants (ex. : résolution de l’ordre de quelques décimètres). Cependant, même dans ce type d’applications les caractéristiques des SP-ASP devraient être prises en considération dans le développement des systèmes et dans leur utilisation, et ce, pour atteindre les résultats visés. À cet égard, cette thèse présente une revue exhaustive des applications de l’imagerie aérienne acquise par ASP et de déterminer les challenges les plus courants. Cette étude a également permis d’établir les caractéristiques et exigences spécifiques des SP-ASP qui sont généralement ignorées ou partiellement discutées dans les études récentes. En conséquence, la première partie de cette thèse traite des aspects méthodologiques et d’expérimentation de la mise en place d’un SP-ASP. Le système développé a été évalué pour la modélisation précise d’une gravière et utilisé pour réaliser des mesures de changement volumétrique. Cette application a été retenue pour deux raisons principales. Premièrement, ce type de milieu fournit un environnement difficile pour la modélisation, et ce, en termes de changement d’échelle, de changement de relief du terrain ainsi que la grande diversité de structures et de textures. Deuxièment, le suivi de mines à ciel ouvert exige un niveau de précision élevé, ce qui justifie les efforts déployés pour mettre au point un SP-ASP de haute précision. Les composantes matérielles du système consistent en un ASP à propulsion électrique de type hélicoptère, d’une caméra numérique à haute résolution ainsi qu’une station inertielle. La composante logicielle est composée de plusieurs programmes développés particulièrement pour calibrer la caméra et la plateforme, intégrer les systèmes, enregistrer les données, planifier les paramètres de vol et détecter automatiquement les points de contrôle au sol. Les détails complets du système sont abordés dans la thèse et des solutions sont proposées afin d’améliorer le système et la qualité des données photogrammétriques produites. La précision des résultats a été évaluée sous diverses conditions de cartographie, incluant le géoréférencement direct et indirect avec un nombre, une répartition et des types de points de contrôle variés. De plus, les effets de la configuration des images et la stabilité du réseau sur la précision de la modélisation ont été évalués. La deuxième partie de la thèse porte sur l’amélioration des techniques de reconstruction éparse et dense. Les solutions proposées sont des alternatives aux techniques de photogrammétrie aérienne traditionnelle et adaptée aux caractéristiques particulières de l’imagerie acquise à basse altitude par ASP. Tout d’abord, une méthode robuste de correspondance éparse et d’estimation de la géométrie épipolaire a été développée. L’élément clé de cette méthode est sa capacité à gérer le pourcentage très élevé des valeurs aberrantes (erreurs entre les points correspondants) avec une efficacité de calcul remarquable en comparaison avec les techniques usuelles. Ensuite, une stratégie d’ajustement de bloc basée sur l’intégration de pseudoobservations du modèle Gauss-Helmert a été proposée. Le principal avantage de cette stratégie consistait à contrôler les effets négatifs du réseau d’images instable et des images bruitées sur la précision de l’autocalibration. Une implémentation éparse de cette stratégie a aussi été réalisée, ce qui a permis de traiter des jeux de données contenant des millions de points de liaison. Finalement, les concepts de courbes intrinsèques ont été revisités pour l’appariement stéréo dense. La technique proposée pourrait atteindre un haut niveau de précision et d’efficacité en recherchant uniquement dans une petite portion de l’espace de recherche des disparités ainsi qu’en traitant les occlusions et les ambigüités d’appariement. Ces solutions photogrammétriques ont été largement testées à l’aide de données synthétiques, d’images à courte portée ainsi que celles acquises sur le site de la gravière. Le système a démontré sa capacité a modélisation dense de l’environnement avec une très haute exactitude en atteignant une précision 3D absolue de l’ordre de 11±7 mm

    A look at land cover classification methods in Northern California with the use of high spatial resolution geospatial data

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    Land use and land cover (LULC) mapping plays a vital role in understanding the state of the world, showing us a visual representation of the natural and anthropogenic features covering our planet. Northern California in the United States is home to many critical habitats that provide for a variety of endemic and some threatened and engendered species, making it an area of particular concern to better understand and monitor. There is a greater need to identify specific methods for vegetation modeling in Northern California due to its unique species; to do this we examined two case studies with the following objectives: 1) Determine whether unmanned aerial system (UAS) image analysis can provide similar estimates of eelgrass biometrics, such as percent coverage, to those obtained in situ using traditional field survey methods; 2) To develop a GIS data fusion workflow for high-resolution habitat classification in the Napa Watershed of central California with a focus on oak savanna habitat. UAS Imagery for two eelgrass sites were collected during June, 2019 using a DJI Matrice 100 equipped with MicaSense RedEdge Multispectral sensor (5-band). Following UAS image collection, ground survey data were collected at three tidal elevation transects per site, with 20 quadrats stationed randomly along each transect. Eelgrass percent coverage was measured for each quadrat and then compared to eelgrass classification models derived from UAS derived imagery. In the Napa watershed, we examined methods necessary to accurately incorporate ancillary geospatial spatial datasets into a remote sensing land cover classification. By doing so, I developed a habitat distribution dataset that may better analyze interactions of wildlife, humans, and the endemic habitat types of the Napa watershed in California. UAVs provided a means to obtain high resolution remote sensing imagery of eelgrass at a resolution of 3.46 – 3.70 cm per pixel or greater at specific tidal periods, providing a useful methodology that allowed for percent coverage estimates with an R2 value of 0.6496 compared to in situ measurements. While developing a land cover classification workflow for the Napa watershed, I found that by incorporating ancillary geospatial data, remotely sensed data, and threshold classification, I could obtain a LULC model that more accurately depicts the endemic land use and land cover features of the Napa watershed. With an overall accuracy of 70.20% and a kappa statistic of 0.6140, this modeling method proved more accurate than traditional image classification methods. With ground sampled reference data and remotely sensed data gathered at the same temporal and spatial scales these classification methods would be robust and replicable for future analyses

    The Multispectral Imaging Science Working Group. Volume 2: Working group reports

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    Summaries of the various multispectral imaging science working groups are presented. Current knowledge of the spectral and spatial characteristics of the Earth's surface is outlined and the present and future capabilities of multispectral imaging systems are discussed
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