CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

Sensitivities of 1-km Forecasts of 24 May 2011 Tornadic Supercells to Microphysics Parameterizations

On 24 May 2011, Oklahoma experienced an outbreak of tornadoes, including one rated EF5 on the enhanced Fujita (EF) scale and two rated EF4. The extensive observation network in this area makes this an ideal case to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. Additionally, the real-time configuration of the 1-km ARPS, which assimilated increments produced by 3DVAR with cloud analysis using incremental analysis updating (IAU), had success providing a good baseline forecast. ARPS forecasts of 0–2 h are verified using observation-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of the storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate that the multimoment microphysics schemes tend to produce better forecasts of tornadic supercells. However, some of the forecasts from the single-moment microphysics schemes perform as well as or better than the forecasts from the multimoment microphysics schemes.

A new metric for climate models that includes field and spatial dependencies using Gaussian Markov Random Fields

A new metric for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new metric uses Gaussian Markov Random Fields for estimating field and space dependencies within a first order grid point neighborhood structure. We illustrate the ability of Gaussian Markov Random Fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new metric to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.

Malleable Environments and the Pursuit of Spatial Justice in the Bronx

A design team in the Hunts Point neighborhood of the South Bronx used methodologies of performance and collaborative, location-based storytelling to contend with the effects of urban spatial injustice in the community. Ideation via a series of participatory performances led to creation of a mobile cinema application as the starting point for public, location-based cinema walks. The application accepts user-generated content, acting as a new form of generative monument to the neighborhood as it evolves. The project exemplifies how installing situated technologies for an embodied form of participation can help translate local concerns to outside audiences, in this case using a metaphorical, locative media platform to discuss the evolving nature of environmental discrimination, over-incarceration, and urban spatial justice in New York City.