The Experts below are selected from a list of 18363 Experts worldwide ranked by ideXlab platform
Cem Unsalan - One of the best experts on this subject based on the ideXlab platform.
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Statistical, Structural, Hybrid, and Graph Theoretical Features to Measure Land Development
IEEE Geoscience and Remote Sensing Letters, 2009Co-Authors: Cem UnsalanAbstract:Extracting information on a developing region from its sequential satellite images has many benefits. Therefore, in a previous study, we introduced graph theoretical and conditional statistical features to measure Land Development in a predefined region. There, we only used the grayscale information from the satellite image at hand. Here, we extend that work by introducing novel statistical, hybrid, and graph theoretical features using multispectral information. We also introduce novel structural features based on three different structure extraction methods. We test our new features on a diverse data set and report their performances in measuring Land Development.
James H Lambert - One of the best experts on this subject based on the ideXlab platform.
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decision analysis and risk models for Land Development affecting infrastructure systems
Risk Analysis, 2012Co-Authors: Shital A Thekdi, James H LambertAbstract:Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential Land Development. Land Development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of Land Development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of Land Development and Land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, Land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of Land Development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential Land Development. Language: en
Shital A Thekdi - One of the best experts on this subject based on the ideXlab platform.
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decision analysis and risk models for Land Development affecting infrastructure systems
Risk Analysis, 2012Co-Authors: Shital A Thekdi, James H LambertAbstract:Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential Land Development. Land Development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of Land Development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of Land Development and Land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, Land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of Land Development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential Land Development. Language: en
Glenn E Moglen - One of the best experts on this subject based on the ideXlab platform.
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a multiobjective optimization approach to smart growth in Land Development
Socio-economic Planning Sciences, 2006Co-Authors: Steven A Gabriel, Jose A Faria, Glenn E MoglenAbstract:Abstract In this paper we apply a multiobjective optimization model of Smart Growth to Land Development. The term Smart Growth is meant to describe Development strategies—that do not promote urban sprawl. However, the term is somewhat open to interpretation. The multiobjective aspects arise when considering the conflicting interests of the various stakeholders involved in Land Development decisions: the government planner, the environmentalist, the conservationist, and the Land developer. We present a formulation—employing linear and convex quadratic objective functions subject to polyhedral and binary constraints for the stakeholders. The resulting optimization problems are convex, quadratic mixed integer programs that are NP-complete. We report numerical results with this model for Montgomery County, MaryLand, and present them using a geographic information system (GIS).
Cemr Unsalan - One of the best experts on this subject based on the ideXlab platform.
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Measuring Land Development in Urban Regions using Graph Theoretical Features
2007 3rd International Conference on Recent Advances in Space Technologies, 2007Co-Authors: Cemr UnsalanAbstract:Inferring Land use from satellite images is studied extensively. In previous studies, the focus was on classifying large regions due to the resolution of available satellite images. Nowadays, very high resolution satellite imagery (Ikonos and Quickbird) allow researchers to focus on more complex Land use problems such as monitoring Development in urban regions. Solutions to these complex problems may improve life standards of city residents. To this end, we focus on automatically monitoring construction zones using their very high resolution panchromatic satellite images through time. To monitor Land Development, we obtain sequential images of a selected region. Then, we extract features from each image in the sequence. Comparing values of these features, we expect to measure the degree of Land Development through time. Here, we introduce four new graph theoretical features to the existing ones. We test all our existing and new features to measure Land Development in 19 different urban construction zones. Our test set consists of Ikonos satellite images of these regions imaged in separate times.
