Minimum spanning trees for valley and ridge characterization in digital elevation maps

Texture synthesis employs neighbourhood matching to generate appropriate new content. Terrain synthesis has the added constraint that new content must be geographically plausible. The profile recognition and polygon breaking algorithm (PPA) [Chang et al. 1998] provides a robust mechanism for characterizing terrain as systems of valley and ridge lines in digital elevation maps. We exploit this to create a terrain characterization metric that is robust, efficient to compute and is sensitive to terrain properties.

Terrain regions are characterized as a minimum spanning tree derived from a graph created from the sample points of the elevation map which are encoded as weights in the edges of the graph. This formulation allows us to provide a single consistent feature definition that is sensitive to the pattern of ridges and valleys in the terrain Alternative formulations of these weights provide richer characteristicmeasures and we provide examples of alternate definitions based on curvature and contour measures.

We show that the measure is robust, with a significant portion derived directly from information local to the terrain sample. Global terrain characteristics introduce the issue of over- and underconnected valley/ridge lines when working with sub-regions. This is addressed by providing two graph construction strategies, which respectively provide an upper bound on connectivity as a single spanning tree, and a lower bound as a forest of trees.

Efficient minimum spanning tree algorithms are adapted to the context of terrain data and are shown to provide substantially better performance than previous PPA implementations. In particular, these are able to characterize valley and ridge behaviour at every point even in large elevation maps, providing a measure sensitive to terrain features at all scales.

The resulting graph based formulation provides an efficient and elegant algorithm for characterizing terrain features. The measure can be calculated efficiently, is robust under changes of neighbourhood position, size and resolution and the hybrid measure is sensitive to terrain features both locally and globally.

Extended depth from focus reconstruction using NIH ImageJ plugins: Quality and resolution of elevation maps

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

LiDAR elevation data of Yukon Coast and Herschel Island in 2012 and 2013

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Lidar Elevation Data Download App

See Project Homepage for more details.

Learned ultra-wideband RADAR sensor model for augmented LIDAR-based traversability mapping in vegetated environments

In vegetated environments, reliable obstacle detection remains a challenge for state-of-the-art methods, which are usually based on geometrical representations of the environment built from LIDAR and/or visual data. In many cases, in practice field robots could safely traverse through vegetation, thereby avoiding costly detours. However, it is often mistakenly interpreted as an obstacle. Classifying vegetation is insufficient since there might be an obstacle hidden behind or within it. Some Ultra-wide band (UWB) radars can penetrate through vegetation to help distinguish actual obstacles from obstacle-free vegetation. However, these sensors provide noisy and low-accuracy data. Therefore, in this work we address the problem of reliable traversability estimation in vegetation by augmenting LIDAR-based traversability mapping with UWB radar data. A sensor model is learned from experimental data using a support vector machine to convert the radar data into occupancy probabilities. These are then fused with LIDAR-based traversability data. The resulting augmented traversability maps capture the fine resolution of LIDAR-based maps but clear safely traversable foliage from being interpreted as obstacle. We validate the approach experimentally using sensors mounted on two different mobile robots, navigating in two different environments.

The use of elevation map histograms for topographic heterogeneity analysis

Surface defects are extremely important in mechanical characterization of several different materials. Therefore, the analysis of surface finishing is essential for a further simulation of surface mechanical properties in a customized project in materials science and technology. One of the methods commonly employed for such purpose is the statistical mapping of different sample surface regions using the depth from focus technique. The analysis is usually performed directly from the elevation maps which are obtained from the digital image processing. In this paper, the possibility of quantifying the surface heterogeneity of Silicon Carbide porous ceramics by elevation map histograms is presented. The advantage of this technique is that it allows the qualitative or quantitative verification of all surface image fields that cannot be done by using the Surface Plot plugin of image J™ platform commonly used in digital image processing. © 2012 Springer Science+Business Media, LLC.

Cutler Bay Township: Sea Level Rise 1990 - 2160 Resulting from Global Climate Change

Draft Document. Presentation of maps produced from digital LIDAR elevation grids and contoured at 1 ft. levels illustration sea level rise for the Cutler Bay Township in Miami-Dade County.

Fractal characterization of brittle fracture in ceramics under mode I stress loading

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fractal behavior throughout stretch zone of 15-5PH steel under elastic-plastic loading conditions

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

3-D reconstruction by extended depth-of-field in failure analysis - Case study II: Fractal analysis of interlaminar fracture in carbon/epoxy composites

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

MRT letter: Extended depth from focus reconstruction method for stretch zone measurement in 15-5PH steel

The stretch zone width (SZW) data for 15-5PH steel CTOD specimens fractured at -150 degrees C to + 23 degrees C temperature were measured based on focused images and 3D maps obtained by extended depth-of-field reconstruction from light microscopy (LM) image stacks. This LM-based method, with a larger lateral resolution, seems to be as effective for quantitative analysis of SZW as scanning electron microscopy (SEM) or confocal scanning laser microscopy (CSLM), permitting to clearly identify stretch zone boundaries. Despite the worst sharpness of focused images, a robust linear correlation was established to fracture toughness (KC) and SZW data for the 15-5PH steel tested specimens, measured at their center region. The method is an alternative to evaluate the boundaries of stretched zones, at a lower cost of implementation and training, since topographic data from elevation maps can be associated with reconstructed image, which summarizes the original contrast and brightness information. Finally, the extended depth-of-field method is presented here as a valuable tool for failure analysis, as a cheaper alternative to investigate rough surfaces or fracture, compared to scanning electron or confocal light microscopes. Microsc. Res. Tech. 75:11551158, 2012. (C) 2012 Wiley Periodicals, Inc.

Correlative light-electron fractography for fatigue striations characterization in metallic alloys

The correlative light-electron fractography technique combines correlative microscopy concepts to the extended depth-from-focus reconstruction method, associating the reliable topographic information of 3-D maps from light microscopy ordered Z-stacks to the finest lateral resolution and large focus depth from scanning electron microscopy. Fatigue striations spacing analysis can be precisely measured, by correcting the mean surface tilting with the knowledge of local elevation data from elevation maps. This new technique aims to improve the accuracy of quantitative fractography in fatigue fracture investigations. Microsc. Res. Tech. 76:909-913, 2013. © 2013 Wiley Periodicals, Inc.