Hasil untuk "Maps"

R. Axelrod

F. Guzzetti, A. Mondini, M. Cardinali et al.

Landslides are present in all continents, and play an important role in the evolution of landscapes. They also represent a serious hazard in many areas of the world. Despite their importance, we estimate that landslide maps cover less than 1% of the slopes in the landmasses, and systematic information on the type, abundance, and distribution of landslides is lacking. Preparing landslide maps is important to document the extent of landslide phenomena in a region, to investigate the distribution, types, pattern, recurrence and statistics of slope failures, to determine landslide susceptibility, hazard, vulnerability and risk, and to study the evolution of landscapes dominated by mass-wasting processes. Conventional methods for the production of landslide maps rely chiefly on the visual interpretation of stereoscopic aerial photography, aided by field surveys. These methods are time consuming and resource intensive. New and emerging techniques based on satellite, airborne, and terrestrial remote sensing technologies, promise to facilitate the production of landslide maps, reducing the time and resources required for their compilation and systematic update. In this work, we first outline the principles for landslide mapping, and we review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories. Next, we examine recent and new technologies for landslide mapping, considering (i) the exploitation of very-high resolution digital elevation models to analyze surface morphology, (ii) the visual interpretation and semi-automatic analysis of different types of satellite images, including panchromatic, multispectral, and synthetic aperture radar images, and (iii) tools that facilitate landslide field mapping. Next, we discuss the advantages and the limitations of the new remote sensing data and technology for the production of geomorphological, event, seasonal, and multi-temporal inventory maps. We conclude by arguing that the new tools will help to improve the quality of landslide maps, with positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations.

J. Poland, P. Brown, M. Sorrells et al.

Advancements in next-generation sequencing technology have enabled whole genome re-sequencing in many species providing unprecedented discovery and characterization of molecular polymorphisms. There are limitations, however, to next-generation sequencing approaches for species with large complex genomes such as barley and wheat. Genotyping-by-sequencing (GBS) has been developed as a tool for association studies and genomics-assisted breeding in a range of species including those with complex genomes. GBS uses restriction enzymes for targeted complexity reduction followed by multiplex sequencing to produce high-quality polymorphism data at a relatively low per sample cost. Here we present a GBS approach for species that currently lack a reference genome sequence. We developed a novel two-enzyme GBS protocol and genotyped bi-parental barley and wheat populations to develop a genetically anchored reference map of identified SNPs and tags. We were able to map over 34,000 SNPs and 240,000 tags onto the Oregon Wolfe Barley reference map, and 20,000 SNPs and 367,000 tags on the Synthetic W9784 × Opata85 (SynOpDH) wheat reference map. To further evaluate GBS in wheat, we also constructed a de novo genetic map using only SNP markers from the GBS data. The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence. Development of the sequenced reference genome(s) will in turn increase the utility of GBS data enabling physical mapping of genes and haplotype imputation of missing data. Finally, as a result of low per-sample costs, GBS will have broad application in genomics-assisted plant breeding programs.

O. Venter, E. Sanderson, Ainhoa Magrach et al.

Remotely-sensed and bottom-up survey information were compiled on eight variables measuring the direct and indirect human pressures on the environment globally in 1993 and 2009. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. A validation analysis using scored pressures from 3114×1 km2 random sample plots revealed strong agreement with the Human Footprint maps. We anticipate that the Human Footprint maps will find a range of uses as proxies for human disturbance of natural systems. The updated maps should provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. Design Type(s) data integration objective Measurement Type(s) anthropogenic environmental process Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) Earth • anthropogenic terrestrial biome • agricultural feature • road • railway • transport feature Design Type(s) data integration objective Measurement Type(s) anthropogenic environmental process Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) Earth • anthropogenic terrestrial biome • agricultural feature • road • railway • transport feature Machine-accessible metadata file describing the reported data (ISA-Tab format)

T. Kohonen

J. W. Van, Ooijen Wageningen, Kyazma B V

Man-Duen Choi

S. Tanksley, S. McCouch

J. Novak

P. Bernardis, P. Ade, J. Bock et al.

The blackbody radiation left over from the Big Bang has been transformed by the expansion of the Universe into the nearly isotropic 2.73 K cosmic microwave background. Tiny inhomogeneities in the early Universe left their imprint on the microwave background in the form of small anisotropies in its temperature. These anisotropies contain information about basic cosmological parameters, particularly the total energy density and curvature of the Universe. Here we report the first images of resolved structure in the microwave background anisotropies over a significant part of the sky. Maps at four frequencies clearly distinguish the microwave background from foreground emission. We compute the angular power spectrum of the microwave background, and find a peak at Legendre multipole lpeak = (197 ± 6), with an amplitude ΔT200 = (69 ± 8) µK. This is consistent with that expected for cold dark matter models in a flat (euclidean) Universe, as favoured by standard inflationary models.

D. Buonomano, M. Merzenich

G. Smoot, C. Bennett, A. Kogut et al.

Results of the first year of data from the differential microwave radiometers on the Cosmic Background Explorer are presented. Statistically significant structure that is well described as scale-invariant fluctuations with a Gaussian distribution is shown. The rms sky variation, smoothed to a total 10-deg FWHM Gaussian, is 30 +/-5 micro-K for Galactic latitude greater than 20-deg data with the dipole anisotropy removed. The rms cosmic quadrupole amplitude is 13 +/-4 micro-K. The angular autocorrelation of the signal in each radiometer channel and cross-correlation between channels are consistent and give a primordial fluctuation power-law spectrum with index of 1.1 +/-0.5, and an rms-quadrupole-normalized amplitude of 16 +/-4 micro-K. These features are in accord with the Harrison-Zel'dovich spectrum predicted by models of inflationary cosmology. 29 refs.

V. Paulsen

D. Liebschner, P. Afonine, N. Moriarty et al.

Residual OMIT maps can be improved by the selective exclusion of bulk solvent from the OMIT region.

G. Caudullo, E. Welk, J. San-Miguel-Ayanz

A novel chorological data compilation for the main European tree and shrub species is presented. This dataset was produced by combining numerous and heterogeneous data collected from 20th century atlas monographs providing complete species distribution maps, and from more recent national to regional atlases, occurrence geodatabases and scientific literature. The dataset is composed of numerous species distribution maps available in geographical information system (GIS) format, created by compiling, evaluating and synthesizing data of all collected sources. The geometry of the individual datasets describes contiguous large areas of occupancy of each species as polygons and fragmented or isolated occurrences as points. Since this geodatabase is intended to provide a synthetic continental-scale overview of the species ranges, the maps represent the species’ general chorology and the presence/absence information should not be considered absolute in terms of geolocation. Errors and imprecisions arising from the interpretation and digitalization processes are likely to occur, especially in those areas where detailed information is scarce. As new information sources become available, these will be used to address current data gaps, implement corrections and updates of the chorology dataset as well as expanding it to comprise additional species.

Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim et al.

Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g. 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene. We also propose a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression. Experimental results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar dataset show significant performance improvement over prior work. The MapNet project webpage is https://goo.gl/mRB3Au.

Binh Yang, Ming Liang, R. Urtasun

In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design a single stage detector that extracts geometric and semantic features from the HD maps. As maps might not be available everywhere, we also propose a map prediction module that estimates the map on the fly from raw LiDAR data. We conduct extensive experiments on KITTI as well as a large-scale 3D detection benchmark containing 1 million frames, and show that the proposed map-aware detector consistently outperforms the state-of-the-art in both mapped and un-mapped scenarios. Importantly the whole framework runs at 20 frames per second.

T. Terwilliger, S. Ludtke, Randy J. Read et al.

A density-modification procedure for improving maps from single-particle electron cryogenic microscopy (cryo-EM) is presented. The theoretical basis of the method is identical to that of maximum-likelihood density modification, previously used to improve maps from macromolecular X-ray crystallography. Key differences from applications in crystallography are that the errors in Fourier coefficients are largely in the phases in crystallography but in both phases and amplitudes in cryo-EM, and that half-maps with independent errors are available in cryo-EM. These differences lead to a distinct approach for combination of information from starting maps with information obtained in the density-modification process. The density-modification procedure was applied to a set of 104 datasets and improved map-model correlation and increased the visibility of details in many of the maps. The procedure requires two unmasked half-maps and a sequence file or other source of information on the volume of the macromolecule that has been imaged. A method based on maximum likelihood density modification, adapted from X-ray crystallography, improves cryo-EM maps.

G. Pintilie, Kaiming Zhang, Z. Su et al.

Cryogenic electron microscopy (cryo-EM) maps are now at the point where resolvability of individual atoms can be achieved. However, resolvability is not necessarily uniform throughout the map. We introduce a quantitative parameter to characterize the resolvability of individual atoms in cryo-EM maps, the map Q -score. Q -scores can be calculated for atoms in proteins, nucleic acids, water, ligands and other solvent atoms, using models fitted to or derived from cryo-EM maps. Q -scores can also be averaged to represent larger features such as entire residues and nucleotides. Averaged over entire models, Q -scores correlate very well with the estimated resolution of cryo-EM maps for both protein and RNA. Assuming the models they are calculated from are well fitted to the map, Q -scores can be used as a measure of resolvability in cryo-EM maps at various scales, from entire macromolecules down to individual atoms. Q -score analysis of multiple cryo-EM maps of the same proteins derived from different laboratories confirms the reproducibility of structural features from side chains down to water and ion atoms. Q -scores provide a quantitative metric for resolvability in cryo-EM maps, and can be used at the atom, residue or macromolecule scale.

Tianhang Zheng, Changyou Chen, Junsong Yuan et al.

3D point-cloud recognition with PointNet and its variants has received remarkable progress. A missing ingredient, however, is the ability to automatically evaluate point-wise importance w.r.t.\! classification performance, which is usually reflected by a saliency map. A saliency map is an important tool as it allows one to perform further processes on point-cloud data. In this paper, we propose a novel way of characterizing critical points and segments to build point-cloud saliency maps. Our method assigns each point a score reflecting its contribution to the model-recognition loss. The saliency map explicitly explains which points are the key for model recognition. Furthermore, aggregations of highly-scored points indicate important segments/subsets in a point-cloud. Our motivation for constructing a saliency map is by point dropping, which is a non-differentiable operator. To overcome this issue, we approximate point-dropping with a differentiable procedure of shifting points towards the cloud centroid. Consequently, each saliency score can be efficiently measured by the corresponding gradient of the loss w.r.t the point under the spherical coordinates. Extensive evaluations on several state-of-the-art point-cloud recognition models, including PointNet, PointNet++ and DGCNN, demonstrate the veracity and generality of our proposed saliency map. Code for experiments is released on \url{https://github.com/tianzheng4/PointCloud-Saliency-Maps}