Self-organised clustering for road extraction in classified imagery

The extraction of road networks from digital imagery is a fundamental image analysis operation. Common problems encountered in automated road extraction include high sensitivity to typical scene clutter in high-resolution imagery, and inefficiency to meaningfully exploit multispectral imagery (MSI). With a ground sample distance (GSD) of less than 2 m per pixel, roads can be broadly described as elongated regions. We propose an approach of elongated region-based analysis for 2D road extraction from high-resolution imagery, which is suitable for MSI, and is insensitive to conventional edge definition. A self-organising road map (SORM) algorithm is presented, inspired from a specialised variation of Kohonen's self-organising map (SOM) neural network algorithm. A spectrally classified high-resolution image is assumed to be the input for our analysis. Our approach proceeds by performing spatial cluster analysis as a mid-level processing technique. This allows us to improve tolerance to road clutter in high-resolution images, and to minimise the effect on road extraction of common classification errors. This approach is designed in consideration of the emerging trend towards high-resolution multispectral sensors. Preliminary results demonstrate robust road extraction ability due to the non-local approach, when presented with noisy input.     

Semantically Enriching an Open Source Sensor Observation Service Implementation for Accessing Heterogeneous Environmental Data Sources

Many kinds of environmental data are nowadays publicly available, but spread over the web. This article discusses using the Sensor Observation Service (SOS) standard of the Open Geospatial Consortium (OGC) as a common interface for providing data from heterogeneous sources which can be integrated to a user tailored environmental information system. In order to allow for providing user‐tailored and problem‐specific information the adjusted SOS is augmented by a semantic layer which maps the environmental information to ontology concepts. The necessary information fusion from different domains and data types lead to several specific requirements for the SOS. Addressing these requirements we have implemented a SOS which still conforms to the OGC SOS 1.0.0 standard specification. The developed SOS has been integrated in a publicly available demonstrator of our personalized environmental information system. Additionally this article discusses future consequences for the SOS, caused by the recently published SOS 2.0 specification.     

Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter

The analysis of social media content for the extraction of geospatial information and event‐related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two‐step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real‐world wildfire event as a representative application case study.     

Antenna-coupled arrays of NbSi micro-bolometers

Microbolometers are at present the most sensitive detectors for mm and sub-mm Astronomy. They are in use in most of the present instruments in that bandwidth. We have developed filled arrays of NbSi-based planar antenna coupled microbolometers. The fabrication details are given, together with characterization of the NbSi thermometers and optical results. The optical performances are potentially good for ground-based mm-wave astronomy applications, while the overall detectors performances are limited by low-frequency excess noise in the thermometric NbSi high-impedance sensors (Anderson insulator).     

Geosocial gauge: a system prototype for knowledge discovery from social media

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.     

Harvesting ambient geospatial information from social media feeds

Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people’s references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information.     

Climate change projections for Greek viticulture as simulated by a regional climate model

Theoretical and Applied Climatology - Viticulture represents an important economic activity for Greek agriculture. Winegrapes are cultivated in many areas covering the whole Greek territory, due to...     

Luminescence spectroscopy and microscopy applied to study gem materials: a case study of C centre containing diamonds

The methods of luminescence spectroscopy and microscopy are widely used for the analysis of gem materials. This paper gives an overview of the most important applications of the analysis of laser and UV excited luminescence by spectroscopy and visually by microscopy with emphasis on diamond, and specifically natural type Ib diamond, little studied so far. Luminescence based techniques are paramount to the gemmological analysis of diamond, in order to determine whether it is natural, treated or synthetic. The great sensitivity of luminescence helps detect some emitting centres that are undetectable by any other analytical method. Hence, especially for diamond, luminescence is an enabling technology, as illustrated by its pioneering use of imagery for the separation of natural and synthetic diamond, and of spectroscopy for the detection of High Pressure–High Temperature treatment. For all other gemstones the applications are at the moment less numerous, but nevertheless they remain highly important. They provide quickly information on the identification of a gem material, and its treatment. Besides the study of broad band emissions caused by various colour centres, the typical PL-causing trace elements (amongst others) are chromium, manganese, uranium and rare earth elements. In pearls the study of broad band luminescence can be useful, and particularly the study of pink to red porphyrin luminescence in pearls from certain species such as Pinctada and Pteria and others can help identify the pearl-producing mollusc, or if a pearl has been dyed or not. Type Ib diamonds are representative of the importance and complexity of the analysis of luminescence by microscopy and spectroscopy. They show a wide range of sometimes very complex emissions that result in luminescence colours from green to yellow to orange or red. These emissions show generally very inhomogeneous distribution. They are caused by a range of defects, however only a few of them are well characterized.     

A Comparative Analysis of Traveling Salesman Solutions from Geographic Information Systems

The Traveling Salesman Problem is one of the most prominent problems in combinatorial optimization, and is regularly employed in a wide variety of applications. The objective of this article is to demonstrate the extent of sub‐optimality produced by Traveling Salesman solution procedures implemented in the context of Geographic Information Systems and to discuss the consequences that such solutions have for practice. Toward that end, an analysis is made of Traveling Salesman solutions from implementations in four Geographic Information System packages. These implementations are tested against the optimal solution for a range of problem sizes. Computational results are presented in the context of a school bus routing application. This analysis concludes that no Traveling Salesman implementation in GIS is likely to find the optimal solution when problems exceed 10 stops. In contrast, optimal solutions can be generated with desktop linear programming software for up to 25 cities. Moreover, one GIS implementation consistently found solutions that were closer to optimal than its competitors. This research strongly suggests that for applications with fewer than 25 stops, the use of an optimal solution procedure is advised, and that GIS implementations can benefit from the integration of more robust optimization techniques.