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Your satisfaction is our top priority! Experience and Expertise For more than eights years, we've written hundreds of doctoral-level thesis papers and dissertations for research—24 hours a day, 7 days a week—on incredibly intricate topics.His supervisor is prof. Prediction plays a crucial role in mapping urban land cover, land use, and wildfire risks. Data-related challenges concern the spectral confusion of the classes of interest, noisy classification results, highly abstracted class definitions, and the large amount of spatial, temporal, structural, and typological variation.
Standard methods tackle these issues by introducing pre- and post-prediction steps such as image fusion, feature extraction, and label regularization. These additional steps are often approximate and detached from the prediction task at hand, making them sub-optimal. This research explores end-to-end predictive models for several remote sensing applications, integrating independent additional steps within a single prediction pipeline. The first objective addresses image fusion in the context of urban land cover classification.
I present a single-stage framework embedding feature extraction and image fusion in a multiresolution convolutional network, called FuseNet.
The network matches the resolution of the panchromatic and multispectral bands in a very high resolution VHR image using convolutional layers with corresponding downsampling and upsampling. I compared FuseNet with the use of separate processing steps for image fusion, such as pansharpening and resampling through interpolation. Results show quantitative improvements on the accuracy of the land cover classification results when using FuseNet.
The second objective addresses label regularization in the context of urban land cover classification. I propose a novel end-to-end classification model integrating image fusion, feature extraction, and label regularization within a recurrent multiresolution convolutional network, called ReuseNet.
ReuseNet extends FuseNet by incorporating contextual label information via recurrent connections. This extension is similar to a standard post-classification label regularization step. I designed experiments on land cover classification where I compared ReuseNet with using separate processing steps for both image fusion and map regularization. Experimental results show both quantitative and qualitative improvements on the classified land cover maps produced by ReuseNet.
The third objective addresses predicting abstracted urban land use classes.
Remote Sensing and GIS
Features extracted from land cover maps are helpful on performing land use classification. Such prior information can be incorporated in the design of a deep learning based land use classifier by applying a multitask learning setup, i.Quantification of model uncertainties in groundwater drought simulations under climate change.Auditing homework solutions
Groundwater drought can be described as a temporary decrease in groundwater availability over a significant period of time. This PhD 1 addresses the influencing factors on groundwater drought, 2 quantifies the groundwater level prediction uncertainty in climate change impact studies using an ensemble of representative concentration pathways, global climate models, multiple alternative conceptual groundwater flow models and groundwater abstraction scenarios and 3 develops and applies an Integrated fully Bayesian Multi-model Uncertainty Estimation Framework IBMUEF to quantify the model uncertainty originating from errors in the model conceptualization, the input data, the parameter values and measurements.
Investigating the effect of different climatic and anthropogenic factors on groundwater drought provides essential information for sustainable planning and management of ground water resources.
In the first part of this study, a multi-step methodology is proposed to understand the influencing factors on groundwater drought. The results show that the evapotranspiration and rainfall deficits are determining meteorological drought, which show a direct relation with groundwater recharge deficits. Land-cover change has a small effect on groundwater recharge but does not seem to be the main cause of groundwater-level decline depletion in the study area.
The groundwater depth and groundwater-level deficit drought is continuously increasing with little correlation to meteorological drought or recharge anomalies. Overexploitation of groundwater for irrigation seems to be the main cause of groundwater-level decline in the study area. Future changes in climate and anthropogenic factors pose additional uncertainties to the supply and management of ground water resources.
In this study, groundwater level prediction uncertainty in climate change impact studies were quantified using 15 alternative conceptual models, 22 climate model runs for representative concentration pathways 4. The results confirm that the groundwater level predictions are strongly dependent on the conceptual model structure.
A significant decrease in groundwater levels is noticed for all groundwater abstraction scenarios. If the current groundwater abstraction trend continues, the groundwater level would decline about 5 to 6 times faster for the future period compared to the baseline period — The difference in groundwater abstraction scenarios was identified as the dominant source of uncertainty in groundwater level simulation in north-western Bangladesh.
The uncertainty due to model conceptualization was also found to be quite significant and higher than that arising from the recharge scenario uncertainty, including the greenhouse gas scenario, climate model and stochastic climate uncertainty contributions.
In the last part of this study, we present a flexible Integrated Bayesian Multi-model Uncertainty Estimation Framework IBMUEF to explicitly quantify the uncertainty originating from errors in the model conceptualization, the input data, the parameter values and measurement. Groundwater recharge and groundwater abstraction multipliers are introduced to quantify the uncertainty of the spatially distributed input data of the groundwater model. A novel generalized formal likelihood function is also included for groundwater modelling based on a heteroscedastic error model to extend the applicability of our IBMUEF in situations where residual errors are heteroscedastic.
Four alternative conceptual models representing different geological information have been used in this multi-model framework. The proposed framework has been applied for an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results of our study confirm that input uncertainty and model conceptualization have a considerable effect on the model predictions and parameter distributions.
The use of groundwater recharge and abstraction multipliers leads to physically more realistic results and improve the reliability and accuracy of the model prediction. We demonstrated that our approach is able to simultaneously quantify the uncertainty originating from model input, parameter and measurement error of a fully distributed groundwater flow model.
Our results also confirm that heteroscedasticity is present in the groundwater level error and its consideration along with model parameter uncertainty improves the accuracy of the uncertainty band. Additionally, the approach described serves as a new way to optimize the spatially distributed recharge and abstraction data under uncertain input conditions. It is found that a single conceptual model is unable to represent all the hydrogeologic processes of the system, hence consideration of alternatives conceptual models along with model input uncertainty is mandatory to have a reliable model prediction and confident parameters sets.
We conclude that an explicit consideration of conceptual model structural uncertainty along with model input, parameter and measurement uncertainty using the IBMUEF framework improves the accuracy and reliability of the model prediction and related uncertainty bounds. In the era of high anthropogenic pressures on the quality of waters, a reliable water management strategy is essential.
Mathematical models play an essential role in providing the information needed to take efficient measures to improve the water quality status of rivers. The current water quality simulators can be categorized into two broad categories. The first category consists of detailed water quality simulators that represent the physical reality in a detailed way. Water quality models developed using this types of simulators are characterized by long calculation times, and often require intensive input data.
As a consequence of the excessive calculation time, they are not suitable for water quality management applications that require multiple simulations. In the second category are water quality simulators that represent the physical reality in a simplified way. Water quality models developed using this types of simulators have very short calculation times.
They are therefore suitable for water quality applications that require multiple model runs and long term simulations.I ordered a research proposal in the research area of Wireless Communications and it was as very good as I can catch it. My friend suggested this place and it delivers what I expect.
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Thank you so much!!!Jump to main content. Remote sensing will be used in data collection, and candidates will also have the opportunity to conduct fieldwork as well. Apply by 1 February The position is available from 1 Maybut no later than 1 August Title: PhD study on Megafauna effects on vegetation dynamics in key rewilding areas.
The PhD project will contribute to this broader objective through assessments of landscape-scale vegetation responses to reintroductions of megafauna in major rewilding areas including both established and young ecosystems. The PhD study will include fieldwork to collect ground data on vegetation structure in major rewilding sites in the US YellowstoneSouth Africa and Europe the Netherlands.
Qualifications and specific competences: Applicants to the PhD position should hold a master in Ecology or equivalent. General quantitative skills is required. Experience with remote sensing and macroecology is an advantage. Information and attachments: Please be aware that you must have all relevant appendices, attachments, addresses for referees, etc. Next to some of the information fields you will find a number. Please note that we reserve the right to remove scientific papers, large reports, theses and the like.
Instead you can indicate a URL where the information is available. All information in the application must be in English or Danish, preferably English. A certified English translation is required for documents written in languages other than English or one of the Scandinavian languages i.
Norwegian, Swedish or Danish languages. As a minimum all applications must include pdf-files only, max.
PhD Projects in Remote Sensing
Application procedures Information and attachments: Please be aware that you must have all relevant appendices, attachments, addresses for referees, etc. If the original documents are not in English or one of the Scandinavian languages i.
Norwegian, Swedish or Danish then copies of the original documents as well as a certified English translation must be attached. For technical reasons, you must upload a project description. When - as here - you apply for a specific project, please simply copy the project description above, and upload it as a PDF in the application. If you wish to, you can indicate an URL where further information can be found. Share this content. Come chat about more early career opportunities in our group forum!
Continue the discussion… Early Career.Michael Schmitt Reconstruction of urban surface models from multi-aspect and multi-baseline interferometric SAR. Felipe G. Bosch-Lluis On the design of microwave radiometers with digital beamforming and polarization synthesis for earth observation Monerris Experimental estimation of soil emissivity and its application to soil moisture retrieval in the SMOS mission Prakoso, K.
Tropical forest mapping using polarimetric and interferometric SAR data Tran, T. Including spatial information in clustering of multi-channel images, Sgrenzaroli, M. Jordi Portell i de Mora Payload data handling, telemetry and data compression systems for Gaia. Pradip K. Mukhopadhyay Three-dimensional Borehole Radar Imaging. Richard T. Jasper M. Camps Aplication of Interferometric Radiometry to Earth Observation Note: in case of missing fonts, to properly view the pdf, please contact the author directly.
Modelling and filtering related to sar data. Application au suivi des glaciers alpins. PhD Theses. Camps Aplication of Interferometric Radiometry to Earth Observation Note: in case of missing fonts, to properly view the pdf, please contact the author directly N.It will be interesting to see how the Panthers utilize their dual-threat rookie. Will Cam stop running and check-down more.
How will the carries be split between McCaffery and Jonathan Stewart. The kid looked good in the preseason, but he was far from spectacular. Cook is a more dynamic playmaker than McCaffery, and his role at this point is more certain. The opportunities will be there for the ex-Seminole to make an immediate impact on an offense lacking impact players.
When Blake Bortles and Chad Henne are the quarterbacks, running the ball should be a priority. But these are the Jags. Trusting them to do the smart thing is risky. His upright style results in a lot of hits and that leads to nagging injuries.
Watson has ability, but as long as Tom Savage is ahead of him on the depth chart, the former Clemson Tiger should be avoided. Hue Jackson deserves credit for biting the bullet and naming Kizer the Week 1 starter. The question is does Kizer possess the maturity to handle the job. Cleveland has zero to lose by leaving him in there to work through growing pains.
Maybe he steals the starting job by the end of September. Or maybe he remains stuck in a committee all season. Not worth the headache. The chances he aggravates it at some point down the road are good. Spencer Ware is done for the season and neither Charcandrick West or CJ Spiller are threats to steal the job from Hunt. Get him at this price while you can, because his value will never be higher.
This Betting Preview is Sponsored by MyBookie. The 2017 NFL season is finally here.
Below, ESPN's Mike Clay and Phil Steele offer their overall assessments of each team's strengths and weaknesses heading into the season, along with best bets. The lower the number, the easier the slate. Each team's projected wins are shown, as well as, how many games Clay expects them to be favored in this season. They let Rex Ryan go and brought in Sean McDermott, who had been the defensive coordinator with Carolina and Philadelphia.
They have their fourth offensive coordinator in four years: Rick Dennison, who comes over from Denver.How to write your PhD thesis (without going insane)
The defense switches from a 3-4 to a 4-3. Buffalo was middle of the road in the NFL for yardage on offense and defense, ranking No. They were not far from a winning record last year at 7-9 with four net close losses, but their seven wins came versus six teams with a losing record and a New England team down to its third-string quarterback. In the past four years, the Bills are dead even at 31-31-2 ATS.
Super Bowl LII odds: The Bills are 20-1 to win the division, 100-1 to win the AFC and 200-1 to win the Super Bowl. Those are big odds, but I have no interest in any of them. While Carolina, Cincinnati, New Orleans and the LA Chargers all had losing seasons last year, I think they are all playoff-caliber teams.
The Bills are favored in seven games by the early lines at CG Technology.
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