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The functionality of Generic Mapping Tools (GMT) to process and visualize geospatial information is crucial to the development of the advanced cartographic method. This paper presents modelling and spatial analysis of the marine geological data using GMT shell scripting. GMT demonstrated effective cartographic solutions for visualization of the georeferenced data. The particular feature of GMT consists in its scripting modular approach that enables to use machine learning to explore reliable georeferenced data. Here, the study applies a sequential shell scripting to devise GMT modules for depicting marine geological data on the Mariana Trench. The data cover bathymetry, geophysics, tectonics and geology. The first method makes use of the 'nearneighbor' GMT module for grid contour modelling using Nearest Neighbor algorithm. This form of modelling classifies the geospatial data based on a similarity. The second method presents surface modelling from the initial XYZ-ASCII dataset by a combination of the 'blockmean' and 'surface' modules. The third method includes the use of the modules 'grdimage', 'psbasemap' and 'grdcontour' for plotting. Compared to GIS methods in which data are processed in a menu, GMT presents the console-based approach which automates cartographic data processing. The results present seven new maps and explanations of scripts.A combination of visual approaches applied using a color fill and various textures to represent data, which is effective in allowing readers to assess geophysical setting. The study demonstrated the effectiveness of GMT in geodata visualization.

International audience; The paper presents the use of scripting cartographic techniques for the visualization of topographic and geomorphological maps by R and GMT. The thematic maps intend to analyze the region of Peru with specific focus on its geomorphology: slope, aspect, elevation and hillshade performed in R libraries ‘raster’ and ‘tmap’. The integrated free materials include several datasets embedded in R library ‘OpenStreetMap’: Stamen, ESRI World Imagery, Bing Maps, National Park Service. The research shows a particular example of the use of open source datasets and free tools available for distance-based online education and research, which becomes an actual tendency in modern geographic research.; El artículo presenta el uso de técnicas cartográficas de scripting para la visualización de mapas topográficos y geomorfológicos por R y GMT. Los mapas temáticos pretenden analizar la región del Perú con un enfoque específico en su geomorfología: pendiente, aspecto, elevación y sombreado realizado en las bibliotecas R «raster» y «tmap». Los materiales integrados incluyen varios conjuntos de datos integrados en la biblioteca R "OpenStreetMap": Stamen, ESRI World Imagery, Bing Maps, National Park Service. La investigación muestra un ejemplo particular del uso de conjuntos de datos de código abierto y herramientas gratuitas disponibles para la educación e investigación en línea a distancia, que se convierte en una tendencia real en la investigación geográfica moderna.

International audience; We propose a novel method to handle thin structures in Image-Based Rendering (IBR), and specifically structures supportedby simple geometric shapes such as planes, cylinders, etc. These structures, e.g. railings, fences, oven grills etc, are present inmany man-made environments and are extremely challenging for multi-view 3D reconstruction, representing a major limitationof existing IBR methods. Our key insight is to exploit multi-view information. After a handful of user clicks to specify thesupporting geometry, we compute multi-view and multi-layer alpha mattes to extract the thin structures. We use two multi-viewterms in a graph-cut segmentation, the first based on multi-view foreground color prediction and the second ensuring multiviewconsistency of labels. Occlusion of the background can challenge reprojection error calculation and we use multiviewmedian images and variance, with multiple layers of thin structures. Our end-to-end solution uses the multi-layer segmentationto create per-view mattes and the median colors and variance to create a clean background. We introduce a new multi-pass IBRalgorithm based on depth-peeling to allow free-viewpoint navigation of multi-layer semi-transparent thin structures. Our resultsshow significant improvement in rendering quality for thin structures compared to previous image-based rendering solutions.

International audience; The study area is located in Central Europe and focusing on Mecsek Hills, a low mountain range in the south western part of Hungary. The region of central Europe includes complex mis of elements from mediterranean and continental climate, since the area is located in transitional zone of sub-atlantic and sub-mediterranean climate types. Mecsek Hills is a unique region of the Hungarian environment. Its central European location specifies distribution of diverse landscape types, formed under conditions of transitional climatic settings, mixed from sub-atlantic to sub-mediterranean. The distribution of ecosystems in the region illustrate adaptation of soil properties to actual climate conditions. As determined by the present-day climatic and geomorphological settings, soils in the Mecsek region are rich in nutrients, and landscapes are characterized by various mixed soil types. Brown forest soils predominate on hilltops of Mecsek, while alluvial soils on floodplains. According to the underlying soil types, the landscapes are characterized by mixed vegetation types. The most typical is Pannonian mixed forests of temperate broadleaf and mixed forests, common in Euro-Siberian region. There are also forests of Turkey and hornbeam oak in the catchment area of Mecsek region. The dominating vegetation coverage types on the slopes of Mecsek Hills include beech forests, ravine forests and oaks. Some regions of the Mecsek Hills include unique biogeographic areas with endemic species, not founded elsewhere in Carpathian Basin. The overall climate change, especially the global temperature increase, controls balance of heat and water budget on the Hungarian landscapes, and has considerable impact on the agricultural landscapes. In the past decades there were changes in climate, detected in the Carpathian Basin region, which illustrate general fluctuations in the climatic settings in Hungary and change of overall average air temperatures. Combination of the satellite images with GIS techniques is a key method for land patterns identification and classification of ecosystems. Research methodology is based on the GIS spatial analysis tools and classification of Landsat TM image, which was used for visualizing landscapes. An ILWIS GIS was applied to perform spatial analysis and mapping. The research algorithm is clustering, which classifies pixels with similar value of Digital Numbers (DNs) to thematic classes. As a result, spatial analysis on distribution of land cover patterns was performed. Data processing include image pre-processing, enhancement, classification, spatial analysis and interpretation. A land cover map was created by classifying study area into land categories. The core method used in the current work for the interpretation of imagery is clustering algorithm. This method is based on the remote sensing general principle that each unique pixel on a multichannel image has spectral signature defined by the reflectance of its DN in each spectral band. The DNs of pixels create unique signatures for various objects, distinguishable from other objects. Multispectral cluster classification was applied for the Landsat TM image, by extracting information about values of the pixels DNs, analyzing their spectral signatures.Current work demonstrated analysis of climate factors affecting ecological settings of Mecsek Hulls, and local landscapes visualized by means of ILWIS GIS and remote sensing data (Landsat TM). The results consist in recognized distribution of land use patterns. ILWIS GIS is a convenient open source GIS, useful for spatial analysis and land use monitoring. Clustering method is useful for ecological mapping, since it enables objective identification of the land types in regions characterized by high land heterogeneity and complex structure, such as agricultural fields mixed with natural land cover types. The experience of Landsat TM imagery processing by means of ILWIS GIS, described in the current work, is a contribution towards agricultural mapping.

This article presents the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring networks at a scale of millions of nodes and tens of millions of edges. ZAME presents an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from the most summary to the most detailed views. Several components work together to make this performance possible and the results meaningful. A ``pyramid'' of aggregated views paged on demand to OpenGL GPU shader programs supports smooth multiscale browsing in huge datasets. Efficient matrix ordering algorithms group related elements to make the views meaningful. Using ZAME, we can explore the entire French Wikipedia, over 500,000 articles and 6,000,000 links, with interactive performance on standard consumer-level computer hardware.