Land use/ land cover change and its impact on soil erosion process in Begnas Tal Rupa Tal watershed using geospatial tools, Kaski district, Nepal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Capacity building in the use of geospatial tools for natural resource management in Tajikistan: Final Report

The Swiss Swiss Consultant Trust Fund (CTF) support covered the period from July to December 2007 and comprised four main tasks: (1) Analysis of historic land degradation trends in the four watersheds of Zerafshan, Surkhob, Toirsu, and Vanj; (2) Translation of standard CDE GIS training materials into Russian and Tajik to enable local government staff and other specialists to use geospatial data and tools; (3) Demonstration of geospatial tools that show land degradation trends associated with land use and vegetative cover data in the project areas, (4) Preliminary training of government staff in using appropriate data, including existing information, global datasets, inexpensive satellite imagery and other datasets and webbased visualization tools like spatial data viewers, etc. The project allowed building of local awareness of, and skills in, up-to-date, inexpensive, easy-to-use GIS technologies, data sources, and applications relevant to natural resource management and especially to sustainable land management. In addition to supporting the implementation of the World Bank technical assistance activity to build capacity in the use of geospatial tools for natural resource management, the Swiss CTF support also aimed at complementing the Bank supervision work on the ongoing Community Agriculture and Watershed Management Project (CAWMP).

Capacity building in the use of geospatial tools for natural resource management in Tajikistan: Inception report BNWPP project

The Centre for Development and Environment (CDE) has been contracted by the World Bank Group to conduct a program on capacity development in use of geospatial tools for natural resource management in Tajikistan. The program aimed to help improving natural resource management by fostering the use of geospatial tools among governmental and non-governmental institutions in Tajikistan. For this purpose a database including a Geographic Information System (GIS) has been prepared, which combines spatial data on various sectors for case study analysis related to the Community Agriculture and Watershed Management Project (CAWMP). The inception report is based on the findings resulting from the Swiss Consultant Trust Fund (CTF) financed project, specifically on the experiences from the awareness creation and training workshop conducted in Dushanbe in November 2007 and the analysis of historical land degradation trends carried out for the four CAWMP watersheds. Furthermore, also recommendations from the inception mission of CDE to Tajikistan (5-20 August 2007) and the inception report for the Swiss CTF support were considered. The inception report for the BNWPP project (The Bank-Netherlands Water Partnership Program) discusses the following project relevant issues: (1) Preliminary list of additional data layers, types of data analysis, and audiences to be covered by BNWPP grant (2) Assessing skills and equipment already available within Tajikistan, and implications for training program and specific equipment procurement plans (3) Updated detailed schedule and plans for all activities to be financed by BNWPP grant, and (4) Proposed list of contents for the final report and web-based presentations.

Capacity Building in the Use of Geospatial Tools for Natural Resource Management in Tajikistan: Inception report

The sustainable management of natural resources is a key issue for sustainable development of a poor, mountainous country such as Tajikistan. In order to strengthen its agricultural and infrastructural development efforts and alleviate poverty in rural areas, spatial information and analysis are of crucial importance to improve priority setting and decision making efficiency. However, poor access to geospatial data and tools, and limited capacity in their use has greatly constrained the ability of governmental institutions to effectively assess, plan, and monitor natural resources management. The Centre for Development and Environment (CDE) has thus been mandated by the World Bank Group to provide adequate technical support to the Community Agriculture and Watershed Management Project (CAWMP). This support consists of a spatial database on soil degradation trends in 4 watersheds, capacity development in and awareness creation about geographic information technology and a spatial data exchange hub for natural resources management in Tajikistan. CDE’s support has started in July 2007 and will last until December 2007 with a possible extension in 2008.

Spatial metrics and Landsat data for urban landuse change detection: case of Addis Ababa, Ethiopia.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.

m-SportGIS

Information technologies (ITs), and sports resources and services aid the potential to transform governmental organizations, and play an important role in contributing to sustainable communities development, respectively. Spatial data is a crucial source to support sports planning and management. Low-cost mobile geospatial tools bring productive and accurate data collection, and their use combining a handy and customized graphical user interface (GUI) (forms, mapping, media support) is still in an early stage. Recognizing the benefits — efficiency, effectiveness, proximity to citizens — that Mozambican Minister of Youth and Sports (MJD) can achieve with information resulted from the employment of a low-cost data collection platform, this project presents the development of a mobile mapping application (app) — m-SportGIS — under Open Source (OS) technologies and a customized evolutionary software methodology. The app development embraced the combination of mobile web technologies and Application Programming Interfaces (APIs) (e.g. Sencha Touch (ST), Apache Cordova, OpenLayers) to deploy a native-to-the-device (Android operating system) product, taking advantage of device’s capabilities (e.g. File system, Geolocation, Camera). In addition to an integrated Web Map Service (WMS), was created a local and customized Tile Map Service (TMS) to serve up cached data, regarding the IT infrastructures limitations in several Mozambican regions. m-SportGIS is currently being exploited by Mozambican Government staff to inventory all kind of sports facilities, which resulted and stored data feeds a WebGIS platform to manage Mozambican sports resources.

GeoTools-Android : herramientas geográficas para Android

Este trabajo pretende esbozar el estado del arte en cuanto a herramientas geoespaciales desarrolladas para el sistema operativo Android, encontrado en muchos dispositivos "smartphone" actuales. Se centra en los navegadores de mapas, como aplicación más representativa de este tipo de herramientas. Se destaca el papel de la empresa Google Inc. como desarrollador de la plataforma y principal proveedor web de información geográfica, y se exploran algunas alternativas.

BCN­Routing : càlcul web de rutes amb pgRouting, OpenStreetMap i OpenLayers

En aquest projecte s'ha desenvolupat una interfície web per calcular rutes a la ciutat de Barcelona. Les rutes calculades són a peu, entre un punt d'origen qualsevol i un punt d'interès turístic de la ciutat com a destí. Per això s'han extret les dades dels carrers de Barcelona d'OpenStreetMap i s'han insertat a una base de dades postgreSQL/postGIS, juntament amb una capa vectorial de punts d'interès turístic que s'ha creat amb el SIG d'escriptori qGIS. El càlcul de les rutes amb les dades de la base de dades s'ha realitzat amb l'extensió pgRouting, i la interfície web per seleccionar els punts d'origen i destí, mostrar els mapes, i mostrar les rutes resultat, s'ha desenvolupat utilitzant la llibreria OpenLayers.

Spatial ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

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.

Visualisation of quality information for geospatial and remote sensing data:providing the GIS community with the decision support tools for geospatial dataset quality evaluation

The evaluation of geospatial data quality and trustworthiness presents a major challenge to geospatial data users when making a dataset selection decision. The research presented here therefore focused on defining and developing a GEO label – a decision support mechanism to assist data users in efficient and effective geospatial dataset selection on the basis of quality, trustworthiness and fitness for use. This thesis thus presents six phases of research and development conducted to: (a) identify the informational aspects upon which users rely when assessing geospatial dataset quality and trustworthiness; (2) elicit initial user views on the GEO label role in supporting dataset comparison and selection; (3) evaluate prototype label visualisations; (4) develop a Web service to support GEO label generation; (5) develop a prototype GEO label-based dataset discovery and intercomparison decision support tool; and (6) evaluate the prototype tool in a controlled human-subject study. The results of the studies revealed, and subsequently confirmed, eight geospatial data informational aspects that were considered important by users when evaluating geospatial dataset quality and trustworthiness, namely: producer information, producer comments, lineage information, compliance with standards, quantitative quality information, user feedback, expert reviews, and citations information. Following an iterative user-centred design (UCD) approach, it was established that the GEO label should visually summarise availability and allow interrogation of these key informational aspects. A Web service was developed to support generation of dynamic GEO label representations and integrated into a number of real-world GIS applications. The service was also utilised in the development of the GEO LINC tool – a GEO label-based dataset discovery and intercomparison decision support tool. The results of the final evaluation study indicated that (a) the GEO label effectively communicates the availability of dataset quality and trustworthiness information and (b) GEO LINC successfully facilitates ‘at a glance’ dataset intercomparison and fitness for purpose-based dataset selection.

Urban change monitoring using GIS and remote sensing tools in Kathmandu valley (Nepal)

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science.

A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.