Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

On learning dynamics underlying the evolution of learning rules.

In order to understand the development of non-genetically encoded actions during an animal's lifespan, it is necessary to analyze the dynamics and evolution of learning rules producing behavior. Owing to the intrinsic stochastic and frequency-dependent nature of learning dynamics, these rules are often studied in evolutionary biology via agent-based computer simulations. In this paper, we show that stochastic approximation theory can help to qualitatively understand learning dynamics and formulate analytical models for the evolution of learning rules. We consider a population of individuals repeatedly interacting during their lifespan, and where the stage game faced by the individuals fluctuates according to an environmental stochastic process. Individuals adjust their behavioral actions according to learning rules belonging to the class of experience-weighted attraction learning mechanisms, which includes standard reinforcement and Bayesian learning as special cases. We use stochastic approximation theory in order to derive differential equations governing action play probabilities, which turn out to have qualitative features of mutator-selection equations. We then perform agent-based simulations to find the conditions where the deterministic approximation is closest to the original stochastic learning process for standard 2-action 2-player fluctuating games, where interaction between learning rules and preference reversal may occur. Finally, we analyze a simplified model for the evolution of learning in a producer-scrounger game, which shows that the exploration rate can interact in a non-intuitive way with other features of co-evolving learning rules. Overall, our analyses illustrate the usefulness of applying stochastic approximation theory in the study of animal learning.

Ibotenate Lesions of Hippocampus and/or Subiculum: Dissociating Components of Allocentric Spatial Learning

This study examined the effects of ibotenic acid-induced lesions of the hippocampus, subiculum and hippocampus +/- subiculum upon the capacity of rats to learn and perform a series of allocentric spatial learning tasks in an open-field water maze. The lesions were made by infusing small volumes of the neurotoxin at a total of 26 (hippocampus) or 20 (subiculum) sites intended to achieve complete target cell loss but minimal extratarget damage. The regional extent and axon-sparing nature of these lesions was evaluated using both cresyl violet and Fink - Heimer stained sections. The behavioural findings indicated that both the hippocampus and subiculum lesions caused impairment to the initial postoperative acquisition of place navigation but did not prevent eventual learning to levels of performance almost as effective as those of controls. However, overtraining of the hippocampus + subiculum lesioned rats did not result in significant place learning. Qualitative observations of the paths taken to find a hidden escape platform indicated that different strategies were deployed by hippocampal and subiculum lesioned groups. Subsequent training on a delayed matching to place task revealed a deficit in all lesioned groups across a range of sample choice intervals, but the subiculum lesioned group was less impaired than the group with the hippocampal lesion. Finally, unoperated control rats given both the initial training and overtraining were later given either a hippocampal lesion or sham surgery. The hippocampal lesioned rats were impaired during a subsequent retention/relearning phase. Together, these findings suggest that total hippocampal cell loss may cause a dual deficit: a slower rate of place learning and a separate navigational impairment. The prospect of unravelling dissociable components of allocentric spatial learning is discussed.

Faculty-librarians collaboration in an e-learning experience : resources management and training in Open University of Catalonia (UOC)

The traditional model of learning based on knowledge transfer doesn't promote the acquisition of information-related competencies and development of autonomous learning. More needs to be done to embrace learner-centred approaches, based on constructivism, collaboration and co-operation. This new learning paradigm is aligned with the European Higher Education Area (EHEA) requirements. In this sense, a learning experience based in faculty' librarian collaboration was seen as the best option for promoting student engagement and also a way to increase information-related competences in Open University of Catalonia (UOC) academic context. This case study outlines the benefits of teacher-librarian collaboration in terms of pedagogy innovation, resources management and introduction of open educational resources (OER) in virtual classrooms, Information literacy (IL) training and use of 2.0 tools in teaching. Our faculty-librarian's collaboration aims to provide an example of technology-enhanced learning and demonstrate how working together improves the quality and relevance of educational resources in UOC's virtual classrooms. Under this new approach, while teachers change their role from instructors to facilitators of the learning process and extend their reach to students, libraries acquire an important presence in the academic learning communities.

Improving Searching and Browsing Capabilities of Learning Object Repositories

Learning object repositories are a basic piece of virtual learning environments used for content management. Nevertheless, learning objects have special characteristics that make traditional solutions for content management ine ective. In particular, browsing and searching for learning objects cannot be based on the typical authoritative meta-data used for describing content, such as author, title or publicationdate, among others. We propose to build a social layer on top of a learning object repository, providing nal users with additional services fordescribing, rating and curating learning objects from a teaching perspective. All these interactions among users, services and resources can be captured and further analyzed, so both browsing and searching can be personalized according to user pro le and the educational context, helping users to nd the most valuable resources for their learning process. In this paper we propose to use reputation schemes and collaborative filtering techniques for improving the user interface of a DSpace based learning object repository.

From institutional repositories to personal collections of learning resources

Institutional digital repositories are a basic piece to provide preservation and reutilization of learning resources. However, their creation and maintenance is usually performed following a top-down approach, causing limitations in the search and reutilization of learning resources. In order to avoid this problem we propose to use web 2.0 functionalities. In this paper we present how tagging can be used to enhance the search and reusability functionalities of institutional learning repositories as well as promoting their usage. The paper also describes the evaluation process that was performed in a pilot experience involving open educational resources.

Online Learning and Teaching in Higher Education, a book review

Peer-reviewed

The FaceUOC Project : An Open Social Learning experience by the Open University of Catalonia

The possibilities and expansion of the use of Web 2.0 has opened up a world of possibilities in online learning. In spite of the integration of these tools in education major changes are required in the educational design of instructional processes.This paper presents an educational experience conducted by the Open University of Catalonia using the social network Facebook for the purpose of testing a learning model that uses a participation and collaboration methodology among users based on the use of open educational resources.- The aim of the experience is to test an Open Social Learning (OSL) model, understood to be a virtual learning environment open to the Internet community, based on the use of open resources and on a methodology focused on the participation and collaboration of users in the construction of knowledge.- The topic chosen for this experience in Facebook was 2.0 Journeys: online tools and resources. The objective of this 5 weeks course was to provide students with resources for managing the various textual, photographic, audiovisual and multimedia materials resulting from a journey.- The most important changes in the design and development of a course based on OSL are the role of the teacher, the role of the student, the type of content and the methodology:- The teacher mixes with the participants, guiding them and offering the benefit of his/her experience and knowledge.- Students learn through their participation and collaboration with a mixed group of users.- The content is open and editable under different types of license that specify the level of accessibility.- The methodology of the course was based on the creation of a learning community able to self-manage its learning process. For this a facilitator was needed and also a central activity was established for people to participate and contribute in the community.- We used an ethnographic methodology and also questionnaires to students in order to acquire results regarding the quality of this type of learning experience.- Some of the data obtained raised questions to consider for future designs of educational situations based on OSL:- Difficulties in breaking the facilitator-centred structure- Change in the time required to adapt to the system and to achieve the objectives- Lack of commitment with free courses- The trend to return to traditional ways of learning- Accreditation- This experience has taught all of us that education can happen any time and in any place but not in any way.

The use of mixed methods research in interdisciplinary educational journals

Mixed methods research is becoming increasingly important in several scientific areas. The analysis of prevalence rates is a new line of research that has emerged in mixed methods research, and this methodological approach has only been applied carefully in a handful of journals. The purpose of this article was to analyse the prevalence of mixed methods research in interdisciplinary educational journals. Moreover, the main characteristics of the mixed methods articles identified were examined. This study used a mixed methods approach to analyse these aspects. Specifically, a partially mixed sequential equal status multiple-case study design was applied with a development mixed methods purpose. Three educational journals in different disciplines were reviewed from 2005 to 2010 (Academy of Management Learning and Education, Educational Psychology Review, Journal of the Learning Sciences). The findings show differences among the journals in the prevalence rates and characteristics of the mixed methods studies

The cultural dimension in focus : promoting awareness of diversity and respect for difference in a Finland-Swedish EFL classroom

This study addresses the role of EFL education, its potential and shortcomings, and the challenges the future of EFL education will bring. It is argued that new societal demands and the limited time we have at our disposal in the classroom make it necessary to rethink goals and content and move away from the transmissionof limited sets of facts and information to helping students develop awareness and competences that can be applied in many different situations, also in a perspective of lifelong learning. The overall aim of the current study is to problematize and increase understanding of the implementation of cultural aspects in the language classroom by addressing the interrelated what, why and how of the cultural dimension within EFL education. This has been conducted by means of theoretical explorations into the area, alongside an attempt at promoting intercultural competence (IC) in a more systematic and insightful manner within my own educational praxis. The focus of the intercultural work in the classroom was on the promotion of awareness of difference and diversity, as well as respect for such difference through the ability to decenter from cultural norms and behavior that previously have been taken for granted. These are two elements that have been suggested as fundamental for other work with IC in the classroom and for the realization of important aspects of the underlying values of basic education. In the context of this study, IC comprises several interconnected components supportingeach other in a variety of ways, with the further aim being interaction with and respect for difference in general, not only concerning e.g. representatives ofcertain English-speaking communities. The methodology was informed by action research, with myself in the role of the teacher-researcher or the reflective practitioner. For the purpose of the project I was authorized to take on the EFL education for the three years of upper comprehensive school of one random class of students originally assigned to one of the language teachers of the selected Finland-Swedish school. Thus, the class of 17 students was not specifically chosen for the project, and the aims and contents chosen for the development project were placed within the framework of the ordinary curriculum. By exploring the students¿ insights concerning different English-speaking cultural groups, mainly through a set of questionnaires, it was possible to outline the work with the cultural dimension in the classroom for the following three years. Work progress was evaluated at specific stages, and the final project evaluations were conducted through individual student interviews in grade 9. The interviews were focused on possible development of students¿ insights concerning different aspects of the cultural dimension. In particular this concerned awareness of difference and diversity, including modification of stereotypes, as well as the ability to decenterin order to be better able to respect such difference. I also explored students¿ awareness and views of the activities and approaches used in class, as well asaffordances both inside and outside the EFL classroom in relation to these intended insights. A further focus area was the perceived relevance to students of different aspects of the cultural dimension. The frameworks and approaches adopted for the work in the classroom all have in common that they are based on a constructivist framework, where knowledge is constructed and reconstructed through interaction with one¿s social and cultural environment, including interaction with others. Reflective processes precede or are simultaneous with the learning of basic factual knowledge. This entails a view of learning as a progression from simple to more complex models rather than as a progression from facts to understanding and analysis. Here, the development of intercultural competence is seen asa cyclical process, or along a spiral curriculum, from simple to more complex levels through a combination of cognitive, affective and behavioral elements within a framework of experiential learning. This project has shown one possible wayforward concerning the development of intercultural competence within EFL education through a more systematic and comprehensive approach regarding linguistic and cultural aspects. The evaluation of the educational process explored in the study suggests the possibilities for work with the promotion of awareness of difference and diversity concerning some specific context that, based on students¿ prior knowledge and preconceptions, would benefit from further work. In this case, the specific context primarily concerned different aspects of both cultural and linguistic conditions in the UK. It is also suggested that many students developed the ability to decenter, described in the study as integral to being able to respect otherness. What still remains to be explored are more individualized approaches considering students¿ different levels of departure. Further work alsoneeds to be put into how to apply insights gained in these specific situations to more general contexts. It is also necessary to explore the use of the suggested approaches in a wider range of different contexts.

Organizational learning from customer interaction

In the fierce competition of today‟s business world an organization‟s capacity to learn maybe its only competitive advantage. This research aims at increasing the understanding on how organizational learning from the customer happens in technology companies. In doing so it provides a synthesized definition of organizational learning and investigates processes of organizational learning within technology companies. A qualitative research method and in-depth interviews with different sized high technology companies, as applied here, enables in-depth study of the learning processes. Research contributes to the understanding of what type of knowledge firms acquire, how new knowledge is transferred and used in a learning firm‟s routines and processes. Research findings show that SMEs and large size companies also, depending on their position in the software value chain, consider different knowledge types as most important and that they use different learning methods to acquire knowledge from their customers.

A bridge between engineering and language learning : automation and German in an online course

Artikkeli luettavissa osassa: Part 2. - ISBN 9789522163172(PDF). - Liitteenä työpaperi

Reflexive model of research-based theatre – processing innovation at the crossroads of theatre, reflection and practice-based innovation activities

Contemporary organisations have to embrace the notion of doing ‘more with less’. This challenges knowledge production within companies and public organisations, forcing them to reorganise their structures and rethink what knowledge production actually means in the context of innovation and how knowledge is actually produced among various professional groups within the organisation in their everyday actions. Innovations are vital for organisational survival, and ‘ordinary’ employees and customers are central but too-often ignored producers of knowledge for contemporary organisations. Broader levels of participation and reflexive practices are needed. This dissertation discusses the missing links between innovation research conducted in the context of industrial management, arts, and culture; applied drama and theatre practices (specifically post-Boalian approaches); and learning – especially organising reflection – in organisational settings. This dissertation (1) explores and extends the role of research-based theatre to organising reflection and reflexive practices in the context of practice-based innovation, (2) develops a reflexive model of RBT for investigating and developing practice-based organisational process innovations in order to contribute to the development of a tool for innovation management and analysis, and (3) operationalises this model within private- and publicsector organisations. The proposed novel reflexive model of research-based theatre for investigating and developing practice-based organisational process innovations extends existing methods and offers a different way of organising reflection and reflexive practices in the context of general innovation management. The model was developed through five participatory action research processes conducted in four different organisations. The results provide learning steps – a reflection path – for understanding complex organisational life, people, and relations amid renewal and change actions. The proposed model provides a new approach to organising and cultivating reflexivity in practice-based innovation activities via research-based theatre. The results can be utilised as a guideline when processing practice-based innovation within private or public organisations. The model helps innovation managers to construct, together with their employees, temporary communities where they can learn together through reflecting on their own and each others’ experiences and to break down assumptions related to their own perspectives. The results include recommendations for practical development steps applicable in various organisations with regard to (i) application of research-based theatre and (ii) related general innovation management. The dissertation thus contributes to the development of novel learning approaches in knowledge production. Keywords: practice-based innovation, research-based theatre, learning, reflection, mode 2b knowledge production