The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
Gunter Bloschl - One of the best experts on this subject based on the ideXlab platform.
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rtop an r package for interpolation of data with a variable Spatial Support with an example from river networks
Computers & Geosciences, 2014Co-Authors: J O Skoien, Gunter Bloschl, Gregor Laaha, Edzer Pebesma, Juraj Parajka, Alberto ViglioneAbstract:Abstract Geostatistical methods have been applied only to a limited extent for Spatial interpolation in applications where the observations have an irregular Support, such as runoff characteristics along a river network and population health data. Several studies have shown the potential of such methods, but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skoien et al. (2006) , we will here present the package rtop , which has been implemented in the statistical environment R ( R Core Team, 2013 ). Taking advantage of the existing methods in R for analysis of Spatial objects ( Bivand et al., 2013 ), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point Spatial Support. The package also offers integration with the intamap package for automatic interpolation and the possibility to run rtop through a Web Service.
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catchments as space time filters a joint spatio temporal geostatistical analysis of runoff and precipitation
Hydrology and Earth System Sciences, 2006Co-Authors: Jon Olav Skoien, Gunter BloschlAbstract:Abstract. In this paper catchments are conceptualised as linear space-time filters. Catchment area A is interpreted as the Spatial Support and the catchment response time T is interpreted as the temporal Support of the runoff measurements. These two Supports are related by T~Aκ which embodies the space-time connections of the rainfall-runoff process from a geostatistical perspective. To test the framework, spatio-temporal variograms are estimated from about 30 years of quarter hourly precipitation and runoff data from about 500 catchments in Austria. In a first step, spatio-temporal variogram models are fitted to the sample variograms for three catchment size classes independently. In a second step, variograms are fitted to all three catchment size classes jointly by estimating the parameters of a point/instantaneous spatio-temporal variogram model and aggregating (regularising) it to the Spatial and temporal scales of the catchments. The exponential, Cressie-Huang and product-sum variogram models give good fits to the sample variograms of runoff with dimensionless errors ranging from 0.02 to 0.03, and the model parameters are plausible. This indicates that the first order effects of the spatio-temporal variability of runoff are indeed captured by conceptualising catchments as linear space-time filters. The scaling exponent κ is found to vary between 0.3 and 0.4 for different variogram models.
Sanjay Chawla - One of the best experts on this subject based on the ideXlab platform.
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Mining spatio-temporal patterns in object mobility databases
Data Mining and Knowledge Discovery, 2008Co-Authors: Florian Verhein, Sanjay ChawlaAbstract:With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions . The latter consists of sources , sinks and thoroughfares . These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define Spatial Support to effectively deal with the problem of different sized regions. We provide an efficient algorithm— STAR-Miner —to find these patterns by exploiting several pruning properties.
Florian Verhein - One of the best experts on this subject based on the ideXlab platform.
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Mining spatio-temporal patterns in object mobility databases
Data Mining and Knowledge Discovery, 2008Co-Authors: Florian Verhein, Sanjay ChawlaAbstract:With the increasing use of wireless communication devices and the ability to track people and objects cheaply and easily, the amount of spatio-temporal data is growing substantially. Many of these applications cannot easily locate the exact position of objects, but they can determine the region in which each object is contained. Furthermore, the regions are fixed and may vary greatly in size. Examples include mobile/cell phone networks, RFID tag readers and satellite tracking. This demands techniques to mine such data. These techniques must also correct for the bias produced by different sized regions. We provide a comprehensive definition of Spatio-Temporal Association Rules (STARs) that describe how objects move between regions over time. We also present other patterns that are useful for mobility data; stationary regions and high traffic regions . The latter consists of sources , sinks and thoroughfares . These patterns describe important temporal characteristics of regions and we show that they can be considered as special STARs. We define Spatial Support to effectively deal with the problem of different sized regions. We provide an efficient algorithm— STAR-Miner —to find these patterns by exploiting several pruning properties.
Amy Braverman - One of the best experts on this subject based on the ideXlab platform.
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Spatial statistical data fusion for remote sensing applications
Journal of the American Statistical Association, 2012Co-Authors: Hai Nguyen, Noel A Cressie, Amy BravermanAbstract:Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models, and it is crucial to accurately estimate aerosol concentrations from remote sensing instruments so as to minimize errors “downstream” in climate models. Currently, space-based observations of aerosols are available from two remote sensing instruments on board NASA's Terra spacecraft: the Multiangle Imaging SpectroRadiometer (MISR), and the MODerate-resolution Imaging Spectrometer (MODIS). These two instruments have complementary coverage, Spatial Support, and retrieval characteristics, making it advantageous to combine information from b...
Alexei A Efros - One of the best experts on this subject based on the ideXlab platform.
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recovering occlusion boundaries from a single image
International Conference on Computer Vision, 2007Co-Authors: Derek Hoiem, Alexei A Efros, Andrew Neil Stein, Martial HebertAbstract:Occlusion reasoning, necessary for tasks such as navigation and object search, is an important aspect of everyday life and a fundamental problem in computer vision. We believe that the amazing ability of humans to reason about occlusions from one image is based on an intrinsically 3D interpretation. In this paper, our goal is to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. Our approach is to learn to identify and label occlusion boundaries using the traditional edge and region cues together with 3D surface and depth cues. Since some of these cues require good Spatial Support (i.e., a segmentation), we gradually create larger regions and use them to improve inference over the boundaries. Our experiments demonstrate the power of a scene-based approach to occlusion reasoning.
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recovering surface layout from an image
International Journal of Computer Vision, 2007Co-Authors: Derek Hoiem, Alexei A Efros, Martial HebertAbstract:Humans have an amazing ability to instantly grasp the overall 3D structure of a scene--ground orientation, relative positions of major landmarks, etc.--even from a single image. This ability is completely missing in most popular recognition algorithms, which pretend that the world is flat and/or view it through a patch-sized peephole. Yet it seems very likely that having a grasp of this "surface layout" of a scene should be of great assistance for many tasks, including recognition, navigation, and novel view synthesis. In this paper, we take the first step towards constructing the surface layout, a labeling of the image intogeometric classes. Our main insight is to learn appearance-based models of these geometric classes, which coarsely describe the 3D scene orientation of each image region. Our multiple segmentation framework provides robust Spatial Support, allowing a wide variety of cues (e.g., color, texture, and perspective) to contribute to the confidence in each geometric label. In experiments on a large set of outdoor images, we evaluate the impact of the individual cues and design choices in our algorithm. We further demonstrate the applicability of our method to indoor images, describe potential applications, and discuss extensions to a more complete notion of surface layout.
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improving Spatial Support for objects via multiple segmentations
British Machine Vision Conference, 2007Co-Authors: Tomasz Malisiewicz, Alexei A EfrosAbstract:Sliding window scanning is the dominant paradigm in object recognition research today. But while much success has been reported in detecting several rectangular-shaped object classes (i.e. faces, cars, pedestrians), results have been much less impressive for more general types of objects. Several researchers have advocated the use of image segmentation as a way to get a better Spatial Support for objects. In this paper, our aim is to address this issue by studying the following two questions: 1) how important is good Spatial Support for recognition? 2) can segmentation provide better Spatial Support for objects? To answer the first, we compare recognition performance using ground-truth segmentation vs. bounding boxes. To answer the second, we use the multiple segmentation approach to evaluate how close can real segments approach the ground-truth for real objects, and at what cost. Our results demonstrate the importance of finding the right Spatial Support for objects, and the feasibility of doing so without excessive computational burden.
