Effects of reverse geocoding on OpenStreetMap tag quality assessment

OpenStreetMap (OSM), a widely-used open-source geographic information system platform, provides a vast geographic dataset in which users contribute both geometric information (nodes, ways, and relations) and semantic information (tags). This method of voluntary contributions is governed by the collective effort of the users. It is widely acknowledged that the quantity of tag information is substantial, but its quality is often poor. Researchers are therefore trying to assess the quality of the tags and enhance the data through various integration experiments. This article investigates the validity of the tags for geographical objects in metropolitan areas using municipal data and a reverse geocoding technique. The proposed method evaluates the data quality and the matching process carried out by reverse geocoding, using municipal points of interest as a reference. The accuracy of the tag and address information and road network centrality metrics were assessed for the OSM objects that were matched to the locations of interest. The tags were found to match the points of interest with an accuracy of 88%. Furthermore, the tag values were categorized and analyzed based on their similarity. It is concluded that in metropolitan settings where centers of interest are closely located, the accuracy of tags and addresses tends to decrease.     

A Composite Semisupervised SVM for Classification of Hyperspectral Images

This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels.     

Recent Retreat of Wilkins Ice Shelf Reveals New Insights in Ice Shelf Breakup Mechanisms

The disintegration of various ice shelves on the Antarctic Peninsula has demonstrated their vulnerability and impacts on tributary glaciers. A satellite image of Wilkins Ice Shelf (WIS) from July of 2007 reveals the formation of a large new double fracture, accompanied by numerous small fractures. We show that bending stresses induced by buoyancy forces were responsible for fracture formation. On February 28-29, 2008, an area of about 425 km² broke up at a narrow connection of the WIS to one of its confining islands. In contrast to Larsen B Ice Shelf, melt ponds that drain into crevasses played no role in this breakup process. A further breakup of 160 km² in the same area occurred on May 30-31, 2008 and documented that breakup can occur during austral winter. Radar images reveal a frozen surface, which demonstrates that in this breakup, surface melt water did not play a role. We conclude that ice shelves with strong thickness contrasts carry potential for disintegration. The fact that the WIS experienced two breakup events under two widely contrasting surface conditions (one during the melt season and one during winter) reveals that there may be several reasons for the disintegration of ice shelves that operate under differing circumstances.     

On the use of iterative re-weighting least-squares and outlier detection for empirically modelling rates of vertical displacement

The proper identification and removal of outliers in the combination of rates of vertical displacements derived from GPS, tide gauges/satellite altimetry, and GRACE observations is presented. Outlier detection is a necessary pre-screening procedure in order to ensure reliable estimates of stochastic properties of the observations in the combined least-squares adjustment (via rescaling of covariance matrices) and to ensure that the final vertical motion model is not corrupted and/or distorted by erroneous data. Results from this study indicate that typical data snooping methods are inadequate in dealing with these heterogeneous data sets and their stochastic properties. Using simulated vertical displacement rates, it is demonstrated that a large variety of outliers (random scattered and adjacent, as well as jointly influential) can be dealt with if an iterative re-weighting least-squares adjustment is combined with a robust median estimator. Moreover, robust estimators are efficient in areas weakly constrained by the data, where even high quality observations may appear to be erroneous if their estimates are largely influenced by outliers. Four combined models for the vertical motion in the region of the Great Lakes are presented. The computed vertical displacements vary between  − 2 mm/year (subsidence) along the southern shores and 3 mm/year (uplift) along the northern shores. The derived models provide reliable empirical constraints and error bounds for postglacial rebound models in the region.     

Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision. Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction. Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of the proposed approach.     

Classical globally reflected gravity field determination in modern locally oriented multiscale framework

The purpose of this paper is the canonical connection of classical global gravity field determination following the concept of Stokes (Trans Camb Philos Soc 8:672–712, 1849), Bruns (Die Figur der Erde, Publikation Königl. Preussisch. Geodätisches Institut, P. Stankiewicz Buchdruckerei, Berlin, 1878), and Neumann (Vorlesungen über die Theorie des Potentials und der Kugelfunktionen. Teubner, Leipzig, pp 135–154, 1887) on the one hand and modern locally oriented multiscale computation by use of adaptive locally supported wavelets on the other hand. The essential tools are regularization methods of the Green, Neumann, and Stokes integral representations. The multiscale approximation is guaranteed simply as linear difference scheme by use of Green, Neumann, and Stokes wavelets. As an application, gravity anomalies caused by plumes are investigated for the Hawaiian and Iceland areas.     

The IGS VTEC maps: a reliable source of ionospheric information since 1998

The International GNSS Service (IGS) Working Group on Ionosphere was created in 1998. Since then, the Scientific community behind IGS, in particular CODE, ESA, JPL and UPC, have been continuosly contributing to reliable IGS combined vertical total electron content (VTEC) maps in both rapid and final schedules. The details on how these products are being generated, performance numbers, proposed improvement as far as VTEC evolution trends during near one Solar Cycle, are summarized in this paper. The confirmation of (1) the good performance of the IGS combined VTEC maps, and (2) the characteristic VTEC variability periods, are two main results of this work.     

CPLat: first operational experimental processing center for SIRGAS in Argentina

The SIRGAS permanent GPS network which is in fact the IGS network densification for the American continent, consists today of more than 200 stations covering the continent and islands. It is currently processed by the IGS RNAAC SIR centre at Deutsches Geodätisches Forschungsinstitut producing weekly free solutions relying on IGS final orbits and EOP that contribute to the ITRF through IGS. By August 2006, the SIRGAS Working Group I had accepted five proposals for experimental processing centers within the region that would collaborate with IGS RNAAC SIR. One of them, Centro de Procesamiento La Plata (CPLat) in Argentina, began processing 60 stations on October 2006. By January 2007 CPLat reached operational capability, delivering weekly free solution SINEX files, with an internal consistency of 1.5 mm average for the horizontal components, and 3 mm in the vertical. Comparisons with IGS global and IGS RNAAC SIR weekly solutions were taken as external consistency indications, showing average RMS residuals of 1.8, 2.4 and 5 mm for the north, east, and vertical component, respectively. Analysis and comparison of adjusted solution time series from CPLat and other processing centers has proved to be highly valuable for solution QC, namely detection and identification of station anomalous behavior or modelling problems. These procedures will ensure the maintenance of the performance specifications for CPLat solutions. Action is being taken in order to guarantee the continuity of this effort beyond the experimental phase.     

Mapping of suspended sediments using site specific seasonal algorithms

This article reports a preliminary work in which two site specific seasonal algorithms have been proposed for estimating the suspended sediments concentration (SSC) from the digital numbers recorded on Indian Remote sensing Satellite, IRS-P4 Ocean Colour Monitor (OCM) sensor. For estimation of SSC, the proposed algorithms utilize dark pixel deduction atmospheric correction technique. The computations are performed with respect to north east monsoon phase situations of Palk Strait coastal stretch. The algorithms performance was satisfactory during the north east monsoon period. Although the results obtained cannot be generalized, we suggest that the authority of proposed algorithms can be extended to other seasons with the addition of more temporal experimental validation data sets and with numeric constants adjusted to present existing conditions. (As this area was severely affected by Tsunami, it may have dissimilar conditions at present).