Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of “volunteer mappers”. Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protection

Risk assessment of foot and mouth disease in the border between Brazil and Paraguay : a geographical approach

La fièvre aphteuse (FA) est l'une des maladies infectieuses les plus importantes qui affectent les animaux biongulés. Le Brésil est libre avec vaccination depuis 2001, mais en 2005, une épidémie est survenue à la frontière entre le Brésil et le Paraguay. Identifier les exploitations agricoles ou des espaces géographiques qui sont le plus à risque de fièvre aphteuse en particulier dans les régions frontalières est l'un des principaux objectifs du service vétérinaire officiel du Brésil et aussi d'autres pays d'Amérique du Sud. Les indicateurs utilisés par le gouvernement brésilien pour indentifier les zones à risque de fièvre aphteuse prennent en considération essentiellement des informations au niveau du troupeau (structure du troupeau, la présence de jeunes animaux, rapport vache / veau, etc.). Dans ce contexte, l'objectif principal de notre recherche est d'élaborer un cadre pour l'évaluation des risques de fièvre aphteuse à la frontière entre le Brésil et le Paraguay prenant en compte les aspects géographiques liés aux systèmes de production. Afin d'atteindre cet objectif, l'étude a été divisée en trois articles. Le premier article dresse un aperçu concernant les pratiques d'hygiène et de contrôle de la FA dans cette zone particulière. Quatre-vingt-sept agriculteurs ont été interrogés sur cinq thèmes principaux: la caractérisation des agriculteurs, les indicateurs sanitaires, la vaccination de la fièvre aphteuse, la circulation des personnes et des animaux ainsi que l'opinion des agriculteurs sur les risques d'introduction de la FA. Les résultats montrent que les agriculteurs sont conscients de leur rôle dans le combat contre la fièvre aphteuse. Il montre également que les agriculteurs, surtout les petits, ont besoin d'être mieux soutenus. Ils n'ont toujours pas de contrôle sanitaire formel. Ils ont besoin de formation et d’un constant soutien. Même si cette région a le même statut sanitaire que le reste du Mato Grosso do Sul, qui est libre de fièvre aphteuse avec vaccination, le contrôle et les différentes mesures sanitaires doivent se poursuivre. Le deuxième article explore la possibilité d'utiliser la télédétection pour cartographier et pour surveiller les zones de pâturage afin d’établir des modèles localisés de prédiction de densité de bétail. Un modèle statistique afin de prédire le nombre de bovins en fonction de la superficie de pâturage déclarée par les agriculteurs, a été réalisé sur la base de données officielle concernant l'élevage de la zone d‘étude. Finalement, ce modèle a été appliqué aux zones de pâturage détectées par classification orientée objet pour prédire la densité bovine. Les résultats indiquent que la méthodologie utilisée pour estimer la densité du bétail peut être utilisée dans des régions où l'information sur l'emplacement et la densité de ferme d'élevage est inexistante. Dans le troisième article, nous avons testé l'approche à majorité floue d’analyse multicritère de décision basée sur un système d’information geographique (SIG - AMC) afin de déterminer les zones à risque d'introduction de la FA. Deux scénarios ont été comparés, le premier basé sur la ferme (où l'information officielle est disponible) et le second basé sur la télédétection (où seulement l'information géographique est disponible). Les cartes obtenues mettent en évidence la forte hétérogénéité spatiale du risque d'introduction de la FA. Une corrélation positive a été observée entre les scénarios basés sur la ferme et les scénarios basés sur la télédétection. Cette étude fournit un cadre alternatif pour détecter les zones à risque de FA et de cette manière pour renforcer les mesures sanitaires brésiliennes. Il a également un grand potentiel pour être extrapolé à d'autres régions ayant des caractéristiques similaires mais où des informations au niveau du troupeau sont rares, ou inexistantes, comme d'autres régions reculées du Brésil ou d'autres pays d'Amérique du Sud Mots-clés : régions frontalières, analyse multicritère à la décision, télédétection, analyse de risques de fièvre aphteuseFoot and mouth disease (FMD) is one of the most important infectious diseases that can affect cloven hoofed animals. Brazil is free with vaccination since 2001, but in 2005 an outbreak occurred in the border between Brazil and Paraguay. Identifying farms or geographic spaces that are more at risk of FMD, especially in border regions, is one of the main goals of official veterinary service from Brazil and other South American countries. Indicators used by the Brazilian government to indentify FMD risk areas takes into consideration basic information at herd level. For these reasons, the principal objective of this research was to elaborate a framework for FMD risk assessment in the frontier between Brazil and Paraguay that takes in account geographic aspects associated with production systems information. In order to accomplish this objective, the study was divided in three articles. The first article draws an overview regarding sanitary practices and FMD control in this particular zone. Eighty seven farmers were interviewed regarding five main subjects: farmers’ characterization, sanitary indicators, FMD disease vaccination, people and animal movements and farmer’s opinions about FMD risks of introduction. The results show that farmers are conscious of their roles in FMD control. It also shows that among small farmers there is a need to be better assisted. Such farmers lack formal sanitary controls and they need constant training and support. Even if this region has the same sanitary status as the rest of Mato Grosso do Sul State (which is FMD free with vaccination), differentiated sanitary measures and control should continue. The second article explores the potential use of remote sensing to map and monitor pasture areas and to establish models for predicting cattle density and location. A statistical model to predict numbers of cattle in function of declared pasture area by the farmers was produced on the basis of Brazilian official livestock databases for the studied area. Finally, this model was applied to the pasture areas detected by oriented based classification to predict cattle density. The results indicate that the methodology used for estimating cattle density has the potential to be applied in regions where no information about farm location and cattle density exists. In the third article the fuzzy majority approach for GIS based multicriteria decision analysis (GIS – MCDA) was tested to determine risk areas of FMD introduction. Two main scenarios were compared: a farm-based one (where official information is available) and a remote sensing-based one (where only geographic information is available). Resulting maps highlighted a strong spatial heterogeneity in the risk of FMD introduction. A positive correlation was observed between farm-based scenarios and remote sensing-based scenarios. This study provides an alternative framework to detect areas of higher risk of FMD and by this way reinforce Brazilian sanitary measures. It also has great potential to be extrapolated for other regions with similar characteristics but where information at herd level are sparse or inexistent such as remote regions of Brazil and other South American countries. Key-words: border regions, multicriteria decision analysis, remote sensing, FMD risk assessment

Multisensor Fusion Remote Sensing Technology For Assessing Multitemporal Responses In Ecohydrological Systems

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government