Multiphase procedure for landscape reconstruction and their evolution analysis. GIS modelling for areas exposed to high volcanic risk

This paper – focussed on the province of Naples, where many municipalities with a huge demographic and building density are subject to high volcanic risk owing to the presence of the Campi Flegrei (Phlegrean Fields) caldera and the Somma-Vesuvius complex – highlights the methodological-applicative steps leading to the setting up of a multiphase procedure for landscape reconstruction and their evolution analysis. From the operational point of view, the research led to the: (1) digitalisation, georeferencing and comparison of cartographies of different periods of time and recent satellite images; (2) elaboration and publication of a multilayer Story Map; (3) accurate vectorisation of the data of the buildings, for each period of time considered, and the use of kernel density in 2D and 3D; (4) application of the extrusion techniques to the physical aspects and anthropic structures; (5) production of 4D animations and film clips for each period of time considered. A procedure is thus tested made up of preparatory sequences, leading to a GIS modelling aimed at highlighting and quantifying significant problem areas and high exposure situations and at reconstructing the phases which in time have brought about an intense and widespread growth process of the artificial surfaces, considerably altering the features of the landscape and noticeably showing up the risk values. In a context characterised by land use conflicts and anomalous conditions of anthropic congestion, a diagnostic approach through images in 2D, 3D and 4D is used, with the aim to support the prevention and planning of emergencies, process damage scenarios and identify the main intervention orders, raise awareness and educate to risk, making an impact on the collective imagination through the enhancement of specific geotechnological functionalities of great didactic interest

Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds

A novel multi-scale operator for unorganized 3D point clouds is introduced. The Difference of Normals (DoN) provides a computationally efficient, multi-scale approach to processing large unorganized 3D point clouds. The application of DoN in the multi-scale filtering of two different real-world outdoor urban LIDAR scene datasets is quantitatively and qualitatively demonstrated. In both datasets the DoN operator is shown to segment large 3D point clouds into scale-salient clusters, such as cars, people, and lamp posts towards applications in semi-automatic annotation, and as a pre-processing step in automatic object recognition. The application of the operator to segmentation is evaluated on a large public dataset of outdoor LIDAR scenes with ground truth annotations.Comment: To be published in proceedings of 3DIMPVT 201

Map Calculus in GIS: a proposal and demonstration

This paper provides a new representation for fields (continuous surfaces) in Geographical Information Systems (GIS), based on the notion of spatial functions and their combinations. Following Tomlin's (1990) Map Algebra, the term 'Map Calculus' is used for this new representation. In Map Calculus, GIS layers are stored as functions, and new layers can be created by combinations of other functions. This paper explains the principles of Map Calculus and demonstrates the creation of function-based layers and their supporting management mechanism. The proposal is based on Church's (1941) Lambda Calculus and elements of functional computer languages (such as Lisp or Scheme)

Representing multifunctional cities: density and diversity in space and time

In this paper, we define measures of urban diversity, density and segregation using newdata and software systems based on GIS. These allow us to visualise the meaning of themultifunctional city. We begin with a discussion of how cities have become moresegregated in their land uses and activities during the last 200 years and how the currentfocus is on reversing this trend through limiting urban sprawl and bringing new lifeback to the inner and central city. We define various indices which show how diversityand density manifest themselves spatially. We argue that multifunctionalism is a relativeconcept, dependent upon the spatial and temporal scale that we use to think about themixing and concentration of urban land uses. We present three examples using spatiallysmoothed indicators of diversity: for a world city ? London, for a highly controlledpolycentric urban region ? Randstad Holland, and for a much more diffusely populatedsemi-urban region ? Venice-Padua-Teviso. We conclude by illustrating that urbandiversity varies as people engage in different activities associated with different landuses throughout the day, as well as through the vertical, third dimension of the city. Thisimpresses the point that we need to understand multifunctional cities in all theirdimensions of space and time

Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73

3D geological models and their hydrogeological applications : supporting urban development : a case study in Glasgow-Clyde, UK

Urban planners and developers in some parts of the United Kingdom can now access geodata in an easy-to-retrieve and understandable format. 3D attributed geological framework models and associated GIS outputs, developed by the British Geological Survey (BGS), provide a predictive tool for planning site investigations for some of the UK's largest regeneration projects in the Thames and Clyde River catchments. Using the 3D models, planners can get a 3D preview of properties of the subsurface using virtual cross-section and borehole tools in visualisation software, allowing critical decisions to be made before any expensive site investigation takes place, and potentially saving time and money. 3D models can integrate artificial and superficial deposits and bedrock geology, and can be used for recognition of major resources (such as water, thermal and sand and gravel), for example in buried valleys, groundwater modelling and assessing impacts of underground mining. A preliminary groundwater recharge and flow model for a pilot area in Glasgow has been developed using the 3D geological models as a framework. This paper focuses on the River Clyde and the Glasgow conurbation, and the BGS's Clyde Urban Super-Project (CUSP) in particular, which supports major regeneration projects in and around the City of Glasgow in the West of Scotland

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications