991 resultados para Land classification
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IDALS stands for Iowa Department of Agriculture and Land Stewardship. IDALS’s mission is to provide leadership for all aspects of agriculture in Iowa, ensure consumer protection and promote the responsible use of our natural resources. DSC stands for the Division of Soil Conservation and is the division within IDALS responsible for state leadership in the protection and management of soil, water and mineral resources. Learn more about IDALS at www.iowaagriculture.gov
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A river can wander through the land in any way it pleases. People, however, face a few legal restrictions when following rivers, lakes, and streams through privately owned land. Care must be taken not to trespass on private property.
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Entre el 14 i el 18 de març de 1998 es va celebrar a Barcelona la conferència Earth’s Changing Land sota la tutela dels programes internacionals Global Change in Terrestrial Ecosystems (GCTE) i Land Use and Land Cover Change (LUCC). L’objectiu principal de la trobada era presentar les darreres aportacions científiques sobre els efectes presents i previsibles del canvi global sobre els ecosistemes terrestres i la societat. Al mateix temps, es volia afavorir l’establiment de ponts de diàleg entre els professionals implicats en el canvi global
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For most people, highway engineering, design and right of way acquisition are not of immediate concern. However, when you own or rent property that will be affected by highway construction, you begin to consider road building from a different and personal viewpoint Right of way is the land on which highways are built. The amount of land needed depends on the engineering standards that must be met for the type of highway that will be built or improved. This booklet will acquaint property owners, tenants and the public with the procedures the Iowa Department of Transportation follows in acquiring right of way for a highway. It is not a source of technical definitions or legal advice. Further, it is not intended to establish a legal standard.
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The principal objective of the knot theory is to provide a simple way of classifying and ordering all the knot types. Here, we propose a natural classification of knots based on their intrinsic position in the knot space that is defined by the set of knots to which a given knot can be converted by individual intersegmental passages. In addition, we characterize various knots using a set of simple quantum numbers that can be determined upon inspection of minimal crossing diagram of a knot. These numbers include: crossing number; average three-dimensional writhe; number of topological domains; and the average relaxation value
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Review of the Abandoned Mined Land Reclamation program administered by the Department of Agriculture and Land Stewardship for the period July 1, 2003 through June 30, 2008
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Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées
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Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées
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Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées
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Report on the Iowa Department of Agriculture and Land Stewardship for the year ended June 30, 2008
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Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.
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In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.
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The DRG classification provides a useful tool for the evaluation of hospital care. Indicators such as readmissions and mortality rates adjusted for the hospital Casemix could be adopted in Switzerland at the price of minor additions to the hospital discharge record. The additional information required to build patients histories and to identify the deaths occurring after hospital discharge is detailed.