The Experts below are selected from a list of 231 Experts worldwide ranked by ideXlab platform
João Alexandrino - One of the best experts on this subject based on the ideXlab platform.
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Modeling a spatially restricted distribution in the Neotropics: How the size of Calibration Area affects the performance of five presence-only methods
Ecological Modelling, 2010Co-Authors: João Gabriel Ribeiro Giovanelli, Marinez Ferreira De Siqueira, Célio F. B. Haddad, João AlexandrinoAbstract:Abstract We here examine species distribution models for a Neotropical anuran restricted to ombrophilous Areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two Calibration Areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution Areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted Area around 10 6 km 2 larger than predicted by other SDMs. With increased Calibration Area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased Calibration Area, while MAXENT models were more consistent across Calibration Areas . MAXENT was the only method that retrieved consistent predictions across Calibration Areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms.
Richard A. Phillips - One of the best experts on this subject based on the ideXlab platform.
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Poor Transferability of Species Distribution Models for a Pelagic Predator, the Grey Petrel, Indicates Contrasting Habitat Preferences across Ocean Basins
PloS one, 2015Co-Authors: Leigh G. Torres, Philip Sutton, David R. Thompson, Karine Delord, Henri Weimerskirch, Paul M. Sagar, Erica Sommer, Ben J. Dilley, Peter G. Ryan, Richard A. PhillipsAbstract:Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model Calibration Area (interpolation), but few studies have assessed SDM transferability to novel Areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model Calibration Areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside Calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.
Marc A. Goossens - One of the best experts on this subject based on the ideXlab platform.
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Integrated analysis of Landsat TM, airborne magnetic, and radiometric data, as an exploration tool for granite-related mineralization, Salamanca province, Western Spain
Nonrenewable Resources, 1993Co-Authors: Marc A. GoossensAbstract:For successful application of remote sensing and data integration in regional mineral exploration, it is crucial that, prior to regional application, relations between geological setting, mineralizing processes, related anomalies, and available remote sensing data are thoroughly studied in a Calibration Area with known mineralization that is representative for the region of interest. Recognizing features that are diagnostic for mineralization and its setting using Landsat Thematic Mapper (TM) and airborne geophysical data is complicated for the following reasons: (1) The basic relation between geology and the original data is often obscured by complex data manipulation, obstructing the understanding of the final output. (2) The risk of removing potentially important information during the data interpretation is considerable. (3) The decision criteria with respect to the correctness of classifications are often arbitrary. (4) Error and quality assessments are often subjective and not reproducible under different circumstances. A data integration study is presented for a 20-km×20-km Calibration Area in Spain with known granite-related mineralization. The contact metamorphic setting is diagnostic for most mineral deposits. Therefore, TM and airborne data are used to map features predictive for contact metamorphic rocks. A concept of spatial reasoning is presented that reduces the damage caused by these problems: (1) The data manipulation is very straightforward and aimed at recognizing specific features known to be diagnostic for the setting in which mineralization is likely to occur. (2) Every step in the data manipulation is described in a quantitative way. (3) The nature of the pixels surrounding a classified pixel is considered when deciding whether the classification is correct. (4) Weights are assigned to the degree of uncertainty within an interpretation. (5) Integrating the interpreted and weighed data into a probability map highlights zones that are confirmed by all data sets, and thus are most reliably classified. This concept enables users of a careful and systematic analysis of the multiple spatial data sets to detect diagnostic features that are predictive for granite-related mineralization in this region.
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Integrated analysis of Landsat TM, airborne magnetic, and radiometric data, as an exploration tool for granite-related mineralization, Salamanca province, Western Spain
Nonrenewable Resources, 1993Co-Authors: Marc A. GoossensAbstract:For successful application of remote sensing and data integration in regional mineral exploration, it is crucial that, prior to regional application, relations between geological setting, mineralizing processes, related anomalies, and available remote sensing data are thoroughly studied in a Calibration Area with known mineralization that is representative for the region of interest. Recognizing features that are diagnostic for mineralization and its setting using Landsat Thematic Mapper (TM) and airborne geophysical data is complicated for the following reasons: (1) The basic relation between geology and the original data is often obscured by complex data manipulation, obstructing the understanding of the final output. (2) The risk of removing potentially important information during the data interpretation is considerable. (3) The decision criteria with respect to the correctness of classifications are often arbitrary. (4) Error and quality assessments are often subjective and not reproducible under different circumstances.
João Gabriel Ribeiro Giovanelli - One of the best experts on this subject based on the ideXlab platform.
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Modeling a spatially restricted distribution in the Neotropics: How the size of Calibration Area affects the performance of five presence-only methods
Ecological Modelling, 2010Co-Authors: João Gabriel Ribeiro Giovanelli, Marinez Ferreira De Siqueira, Célio F. B. Haddad, João AlexandrinoAbstract:Abstract We here examine species distribution models for a Neotropical anuran restricted to ombrophilous Areas in the Brazilian Atlantic Forest hotspot. We extend the known occurrence for the treefrog Hypsiboas bischoffi (Anura: Hylidae) through GPS field surveys and use five modeling methods (BIOCLIM, DOMAIN, OM-GARP, SVM, and MAXENT) and selected bioclimatic and topographic variables to model the species distribution. Models were first trained using two Calibration Areas: the Brazilian Atlantic Forest (BAF) and the whole of South America (SA). All modeling methods showed good levels of predictive power and accuracy with mean AUC ranging from 0.77 (BIOCLIM/BAF) to 0.99 (MAXENT/SA). MAXENT and SVM were the most accurate presence-only methods among those tested here. All but the SVM models calibrated with SA predicted larger distribution Areas when compared to models calibrated in BAF. OM-GARP dramatically overpredicted the species distribution for the model calibrated in SA, with a predicted Area around 10 6 km 2 larger than predicted by other SDMs. With increased Calibration Area (and environmental space), OM-GARP predictions followed changes in the environmental space associated with the increased Calibration Area, while MAXENT models were more consistent across Calibration Areas . MAXENT was the only method that retrieved consistent predictions across Calibration Areas, while allowing for some overprediction, a result that may be relevant for modeling the distribution of other spatially restricted organisms.
Marc Pollefeys - One of the best experts on this subject based on the ideXlab platform.
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Leveraging Image-based Localization for Infrastructure-based Calibration of a Multi-camera Rig
Journal of Field Robotics, 2014Co-Authors: Lionel Heng, Paul Furgale, Marc PollefeysAbstract:Most existing Calibration methods for multi-camera rigs are computationally expensive, use installations of known fiducial markers, and require expert supervision. We propose an alternative approach called infrastructure-based Calibration that is efficient, requires no modification of the infrastructure or Calibration Area, and is completely unsupervised. In infrastructure-based Calibration, we use a map of a chosen Calibration Area and leverage image-based localization to calibrate an arbitrary multi-camera rig in near real-time. Due to the use of a map, before we can apply infrastructure-based Calibration, we have to run a survey phase once to generate a map of the Calibration Area. In this survey phase, we use a survey vehicle equipped with a multi-camera rig and a calibrated odometry system, and self-Calibration based on simultaneous localization and mapping to build the map that is based on natural features. The use of the calibrated odometry system ensures that the metric scale of the map is accurate. Our infrastructure-based Calibration method does not assume an overlapping field of view between any two cameras, and it does not require an initial guess of any extrinsic parameter. Through extensive field tests on various ground vehicles in a variety of environments, we demonstrate the accuracy and repeatability of the infrastructure-based Calibration method for Calibration of a multi-camera rig. The code for our infrastructure-based Calibration method is publicly available as part of the CamOdoCal library at https://github.com/hengli/camodocal.
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ICRA - Infrastructure-based Calibration of a multi-camera rig
2014 IEEE International Conference on Robotics and Automation (ICRA), 2014Co-Authors: Lionel Heng, Paul Furgale, Mathias Bürki, Gim Hee Lee, Roland Siegwart, Marc PollefeysAbstract:The online reCalibration of multi-sensor systems is a fundamental problem that must be solved before complex automated systems are deployed in situations such as automated driving. In such situations, accurate knowledge of Calibration parameters is critical for the safe operation of automated systems. However, most existing Calibration methods for multisensor systems are computationally expensive, use installations of known fiducial patterns, and require expert supervision. We propose an alternative approach called infrastructure-based Calibration that is efficient, requires no modification of the infrastructure, and is completely unsupervised. In a survey phase, a computationally expensive simultaneous localization and mapping (SLAM) method is used to build a highly accurate map of a Calibration Area. Once the map is built, many other vehicles are able to use it for Calibration as if it were a known fiducial pattern. We demonstrate the effectiveness of this method to calibrate the extrinsic parameters of a multi-camera system. The method does not assume that the cameras have an overlapping field of view and it does not require an initial guess. As the camera rig moves through the previously mapped Area, we match features between each set of synchronized camera images and the map. Subsequently, we find the camera poses and inlier 2D-3D correspondences. From the camera poses, we obtain an initial estimate of the camera extrinsics and rig poses, and optimize these extrinsics and rig poses via non-linear refinement. The Calibration code is publicly available as a standalone C++ package.
