Web services enabled architecture coupling data and functional resources

Web services are the backbone of WISDOM system, an information and visualisation system supporting decision makers in the fields of water related management processes based on open source technologies. They enable the distributed and loosely coupled, component based architecture of the system. In cooperating OGC compliant web services for data access, visualisation and data processing the system is extendible to external data resources and other proprietary software solutions. The base idea behind the designed and prototypically implemented WISDOM techniques is the orchestration of decoupled web resources representing data sets and functionality to model more complex business processes quite easily. The system covers most aspects of administrative business processes including spatial and non-spatial data ingestion and dissemination, necessary data processing and visualisation techniques. In combination with a semantics enabled data management WISDOM system is capable to produce value added information products to water management related tasks autonomously. These compound data and processing resource chains are implemented to facilitate certain identified business processes in regional administration. Clients like data and information explorers supporting manual interaction as human machine interfaces or automated data access of value adding operations accomplish, respectively trigger these integrative chains. As an example the same data and processing infrastructure is used to visualise data in map clients as WMS or access data as WCS, resp. WFS for further processing which can furthermore trigger additional actions like feeding reports or requesting auxiliary data

Automated classification of CORINE land use classes using object recognition techniques

Informationen über die Landbedeckung und die mit der anthropogenen Komponente verbundenen Landnutzung sind elementare Bestandteile für viele Bereiche der Politik, Wirtschaft und Wissenschaft. Darunter fallen beispielsweise die Strukurentwicklungsprogramme der EU, die Schadensregulierung im Versicherungswesen und die Modellierung von Stoffkreisläufen. CORINE Land Cover (CLC) wurde infolge eines erweiterten Bedarfs an einem europaweit harmonisierten Datensatz der Landoberfläche erstellt. Das CORINE Projekt weist für diese Arbeit eine hohe Relevanz durch die regelmäßigen Aktualisierungen von 10 Jahren, dem Einsatz der Daten in vielen europäischen und nationalen Institutionen und der guten Dokumentation der CORINE Nomenklatur auf. Die Erstellung der Daten basiert auf der computergestützten manuellen Interpretation, da automatische Verfahren durch die Komplexität der Aufgabenstellung und Thematik nicht in der Lage waren, den menschlichen Interpreten zu ersetzen. Diese Arbeit stellt eine Methodik vor, um CORINE Land Cover aus optischen Fernerkundungsdaten für eine kommende Aktualisierung abzuleiten. Hierzu dienen die Daten von CLC 1990 und der Fernerkundungsdatensatz Image 2000 als Grundlage, sowie die CLC 2000 Klassifikation als Referenz. Die entwickelte und in dem Softwarepaket gnosis implementierte Methodik wendet die objektorientierte Klassifikation in Kombination mit Theorien aus der menschlichen Bildwahrnehmung an. In diesen Theorien wird die Bildwahrnehmung als informationstechnischer Prozess gesehen, der den Klassifikationsprozess in die drei folgenden Subprozesse unterteilt: Bildsegmentierung, Merkmalsgenerierung und Klassenzuweisung. Die Bildsegmentierung generiert aus den untersten Bildprimitiven (Pixeln) bedeutungsvolle Bildsegmente. Diesen Bildsegmenten wird eine Anzahl von bildinvarianten Merkmalen aus den Fernerkundungsdaten für die Bestimmung der CLC Klasse zugewiesen. Dabei liegt die wichtigste Information in der Ableitung der Landbedeckung durch den überwachten Stützvektor-Klassifikator. Die Landoberfläche wird hierzu in zehn Basisklassen untergliedert, um weiteren Merkmalen einen semantischen Unterbau zu geben. Zur Bestimmung der anthropogenen Komponente von ausgewählten Landnutzungsklassen, wie beispielsweise Ackerland und Grünland, wird der phänologische Verlauf der Vegetation durch die Parameter temporale Variabilität und temporale Intensität beschrieben. Neben dem jahreszeitlichen Verlauf der Vegetation können Nachbarschaftsbeziehungen untersucht werden, um weitere anthropogene Klassen und heterogen aufgebaute Sammelklassen beschreiben zu können. Der Versiegelungsgrad als Beispiel für eine Reihe von unscharfen Merkmalen dient der weiteren Differenzierung der verschiedenen Siedlungsklassen aus CORINE LC. Mit Hilfe dieser Merkmale werden die CLC Klassen in abstrakter Form im Klassenkatalog (a-priori Wissensbasis) als Protoklassen beschrieben. Die eigentliche Objekterkennung basiert auf der Repräsentation der CORINE Objekte durch ihre einzelnen Bestandteile und vergleicht die gefundenen Strukturen mit der Wissensbasis. Semantisch homogen aufgebaute Klassen, wie Wälder und Siedlungen oder Protoklassen mit eindeutigen Merkmalen, beispielsweise zur Bestimmung von Grünland durch die Phänologie, können durch den bottom–up Ansatz identifiziert werden. Das übergeordnete CLC Objekt kann direkt aus den Bestandteilen zusammengebaut und einer Klasse zugewiesen werden. Semantisch heterogene Klassen, wie zum Beispiel bestimmte Sammelklassen (Komplexe Parzellenstrukur), können durch ihre Bestandteile validiert werden, indem die Bestandteile eines existierenden CLC Objektes mit der Wissensbasis auf Konsistenz untersucht werden (top–down Ansatz). Eine a-priori Datengrundlage ist für die Erkennung dieser Klassen essentiell. Die Untersuchung der drei Testgebiete (Frankfurt, Berlin, Oldenburg) zeigte, dass von der CORINE LC Nomenklatur 13 Klassen identifiziert und weiteren 14 Klassen validiert werden können. Zehn Klassen können durch diese Methodik aufgrund fehlender Merkmale oder Zusatzdaten nicht klassifiziert werden. Die Gesamtgenauigkeit der automatisierten Klassifikation für die Testgebiete beträgt zwischen 70% und 80% für die umgesetzten Klassen. Betrachtet man davon einzelne Klassen, wie Siedlungs-, Wald- oder Wasserklassen, wird aufgrund der verwendeten Merkmale eine Klassifikationsgenauigkeit von über 90% erreicht. Ein möglicher Einsatz der entwickelten Software gnosis liegt in der Unterstützung einer kommenden CORINE Aktualisierung durch die Prozessierung der identifizierbaren Klassen. Diese CLC Klassen müssen vom Interpreten nicht mehr überprüft werden. Für bestimmte CLC Klassen aus dem Top-down Ansatz wird der Interpret die letzte Entscheidung aus einer Auswahl von Klassen treffen müssen. Weiterhin können die berechneten Merkmale, wie die temporalen Eigenschaften und der Versiegelungsgrad dem Bearbeiter als Entscheidungsgrundlage zur Verfügung gestellt werden. Der Einsatz dieser neu entwickelten Methode führt zu einer Optimierung des bestehenden Aufnahmeverfahrens durch die Integration von semi-automatisierten Prozessen.Land cover and land use classifications provide significant information for politics, economy and science. CORINE Land Cover (CLC) represents a harmonised Pan-European land cover dataset utilised by many European and national institutions. The mapping product comprising 44 classes of land cover and land use, is well documented. At the same time it is periodically updated in intervals of 10 years. Mainly due to the complexity of the CORINE nomenclature, generating and updating of this product has ever since been solely based on computer–aided manual image interpretation. To this date, manual interpretation being the backbone of CORINE actualisation has not been replaced by computer aided approaches. As a consequence, this study aims at developing a semi-automated methodology to derive CORINE Land Cover from optical remotely sensed data. The methodology presented, is based upon the former CLC 1990 classification and the Landsat ETM+ based Image 2000 while reference and validation is realised utilising the CLC 2000 data set. Implementation of the presented approach is realised by the software package gnosis combining object oriented classification paradigms with theories related to human image perception. Human image perception itself is known to be a process of information engineering including three sub-processes as follows: image segmentation, feature generation, and class assignment. With regard to image segmentation, meaningful image segments are generated based upon the most simple image primitives, the pixels. Resulting image segments consist of a wide range of invariant image features describing actual CLC classes. However, precise knowledge about land cover is the uttermost important information for any further processing steps presented in this work. Therefore, ten baseline land cover classes are extracted from multi spectral image 2000 data sets using a novel supervised classification approach of support vector machines. In order to estimate the anthropogenic impact affecting some CORINE classes, the phenological characteristics are analysed and processed. Thus temporal parameters like temporal variability and temporal intensity are used for the delineation of pastures and arable land. Conjointly with these vegetation features, neighbourhood analysis is used to derive functionality or heterogeneity of complex classes. At last, additional error reduction and further specification is addressed by the extraction of fuzzy features. Based on these features sets, CLC classes are represented abstractly stored within a class catalogue i.e. an a-priori knowledge base. Class assignment itself is based on the representation of CORINE objects by its integral parts. In the following this sub-process, representing the final step of image perception, is used to compare the extracted structures with the prototypical classes of the knowledge base. On one hand homogeneous classes, consisting of a single land cover type of baseline classes like forests and pastures, are identified with a bottom–up approach. This is based on the assumption that any superior CLC object is composed of and therefore directly linked to its components and consecutively assigned to a specific CLC class. On the other hand heterogeneous classes, consisting of multiple cover types like complex cultivation patterns, can be validated by comparing its components to the knowledge base, i.e. a top–down approach. However, the a-priori geometry provided by a former classification is essential for this type of object recognition. The analysis of test sites located in the vicinities of Frankfurt, Berlin, and Oldenburg indicates that 13 CLC classes can be identified automatically while a second set of 14 CLC classes can be validated. On the contrary, ten classes can not be acquired by the presented approach due to the lack of required features or missing ancillary information. Thus the overall accuracy of the automated classification of the test sites ranges between 70% and 80 %. In addition it increases to more than 90% as observation is limited to classes with intrinsic prototypical description and distinct features like settlement, forest, and water. As a result of this thesis the software package gnosis will provide a fundamental service and support for the forthcoming CORINE update. By means of the software, 13 identifiable classes can be processed automatically based on the bottom–up approach. In regards to the set of 14 CLC classes derived from the top–down approach, distinct class definition will rely on the trained interpreters selecting from a given set of potential classes. This decision making process can also be facilitated by existing feature sets describing temporal characteristics and impervious cover fraction. As a consequence both automated and semi-automated processes presented in this thesis can be considered a good advancement of the existing compilation and updating procedures of the CORINE land cover project

Automatisierte Klassifikation von Landnutzung durch Objekterkennung am Beispiel von CORINE Land Cover

Land cover and land use classifications provide significant information for politics, economy and science. CORINE Land Cover (CLC) represents a harmonised Pan-European land cover dataset utilised by many European and national institutions. The mapping product comprising 44 classes of land cover and land use, is well documented. At the same time it is periodically updated in intervals of 10 years. Mainly due to the complexity of the CORINE nomenclature, generating and updating of this product has ever since been solely based on computer–aided manual image interpretation. To this date, manual interpretation being the backbone of CORINE actualisation has not been replaced by computer aided approaches. As a consequence, this study aims at developing a semi-automated methodology to derive CORINE Land Cover from optical remotely sensed data. The methodology presented, is based upon the former CLC 1990 classification and the Landsat ETM+ based Image 2000 while reference and validation is realised utilising the CLC 2000 data set. Implementation of the presented approach is realised by the software package gnosis combining object oriented classification paradigms with theories related to human image perception. Human image perception itself is known to be a process of information engineering including three sub-processes as follows: image segmentation, feature generation, and class assignment. With regard to image segmentation, meaningful image segments are generated based upon the most simple image primitives, the pixels. Resulting image segments consist of a wide range of invariant image features describing actual CLC classes. However, precise knowledge about land cover is the uttermost important information for any further processing steps presented in this work. Therefore, ten baseline land cover classes are extracted from multi spectral image 2000 data sets using a novel supervised classification approach of support vector machines. In order to estimate the anthropogenic impact affecting some CORINE classes, the phenological characteristics are analysed and processed. Thus temporal parameters like temporal variability and temporal intensity are used for the delineation of pastures and arable land. Conjointly with these vegetation features, neighbourhood analysis is used to derive functionality or heterogeneity of complex classes. At last, additional error reduction and further specification is addressed by the extraction of fuzzy features. Based on these features sets, CLC classes are represented abstractly stored within a class catalogue i.e. an a-priori knowledge base. Class assignment itself is based on the representation of CORINE objects by its integral parts. In the following this sub-process, representing the final step of image perception, is used to compare the extracted structures with the prototypical classes of the knowledge base. On one hand homogeneous classes, consisting of a single land cover type of baseline classes like forests and pastures, are identified with a bottom–up approach. This is based on the assumption that any superior CLC object is composed of and therefore directly linked to its components and consecutively assigned to a specific CLC class. On the other hand heterogeneous classes, consisting of multiple cover types like complex cultivation patterns, can be validated by comparing its components to the knowledge base, i.e. a top–down approach. However, the a-priori geometry provided by a former classification is essential for this type of object recognition. The analysis of test sites located in the vicinities of Frankfurt, Berlin, and Oldenburg indicates that 13 CLC classes can be identified automatically while a second set of 14 CLC classes can be validated. On the contrary, ten classes can not be acquired by the presented approach due to the lack of required features or missing ancillary information. Thus the overall accuracy of the automated classification of the test sites ranges between 70% and 80 %. In addition it increases to more than 90% as observation is limited to classes with intrinsic prototypical description and distinct features like settlement, forest, and water. As a result of this thesis the software package gnosis will provide a fundamental service and support for the forthcoming CORINE update. By means of the software, 13 identifiable classes can be processed automatically based on the bottom–up approach. In regards to the set of 14 CLC classes derived from the top–down approach, distinct class definition will rely on the trained interpreters selecting from a given set of potential classes. This decision making process can also be facilitated by existing feature sets describing temporal characteristics and impervious cover fraction. As a consequence both automated and semi-automated processes presented in this thesis can be considered a good advancement of the existing compilation and updating procedures of the CORINE land cover project

RESTful WISDOM (Water-related Information System for the sustainable development of the Mekong Delta)

ABSTRACT: This web-based GIS supports decision makers and non-expert users in the field of water resources management. A vast amount of data covering different water-related topics is hosted by the system and can be visualised for further analysis. The data products stem from the fields of remote sensing, hydrological modelling, vulnerability studies, statistical data from year books, etc. As a consequence, one complexity lies in the task of making these different data types from different disciplines efficiently accessible to non GIS experts for certain user driven questions. Thus, every piece of data within WISDOM system is seen as a resource, which is registered in a spatial and thematic reference scheme. To make these resources available, RESTful services are implemented, with special focus on semantically meaningful naming. By giving entry points the user can successively browse the resources which are bundled to data collections supporting different tasks or questions. How these collections are bundled together is defined by the reference scheme and the questions the user asks (by clicking a special link). Resources can be linked to new resources in a standardised way and are addressed the same way. The complexity of combining two different resources to one new resource is hidden from the user. The RESTful style of the system architecture helps to make data easily accessible for non-expert users and system processes and lays the basis for an easy-to-use decision support systems

Web services enabled architecture coupling data and processing resources

ABSTRACT: Web services are the backbone of WISDOM system, an information and visualisation system supporting decision makers in the fields of water related management processes based on open source technologies. They enable the distributed and loosely coupled, component based architecture of the system. In cooperating OGC compliant web services for data access, visualisation and data processing the system is extendible to external data resources and other proprietary software solutions. The base idea behind the designed and prototypically implemented WISDOM techniques is the orchestration of decoupled web resources representing data sets and functionality to model more complex business processes quite easily. The system covers most aspects of administrative business processes including spatial and non-spatial data ingestion and dissemination, necessary data processing and visualisation techniques. In combination with a semantics enabled data management WISDOM system is capable of producing value added information products to water management related tasks autonomously. These compound data and processing resource chains are implemented to facilitate certain identified business processes in regional administration. Clients like data and information explorers supporting manual interaction as human machine interfaces or automated data access of value adding operations accomplish, respectively trigger these integrative chains. As an example the same data and processing infrastructure is used to visualise data in map clients as WMS or access data as WCS, resp. WFS for further processing which can furthermore trigger additional actions like feeding reports or requesting auxiliary data

Data processing using Web Processing Service orchestration within a Spatial Data Infrastructure

Standardised web services are the backbone of the ELVIS system, the EnvironmentaL Visualisation and Information System supporting Vietnamese stake holders in the fields of water related management processes based on open source technologies. They support distributed and loosely coupled, component based architecture of the system. In cooperating OGC compliant web services for data access, visualisation and data processing the system is extendible to external data resources and other proprietary software solutions. The base idea behind the architecture is the orchestration of decoupled web resources representing data sets and functionality to model more complex business processes. The system covers most aspects of administrative business processes including spatial and non-spatial data ingestion and dissemination, necessary data processing and visualisation techniques. In combination with a semantics enabled data management ELVIS is capable to produce value added information products to water management related tasks

TWOPAC – A new approach for automated classification of satellite imagery

ABSTRACT: Land cover classification from satellite imagery provides base data for planning activities in several fields like integrated water resources management, land management, etc. The WISDOM project (www.wisdom.caf.dlr.de) aims for the implementation of a water-related information system to support planning activities within Vietnamese institutions. Reliable and reproducible land cover and land use maps are one of the main products which are provided through the WISDOM information system. Common image classification techniques often include high degree in manual operator interaction, for e.g. data preparation and sampling over a variety of software tools. Our approach aims to reduce these manual sequential processing steps. Increasing needs for automation of classification procedures result from the requirement of processing large amounts of data – either to cover large areas or to handle time series data. The introduced approach TWOPAC – Twinned object- and pixel-based automated classification chain – realizes pixel- and objectbased supervised classification of multi-sensor and multi-resolution satellite imagery. It basically supports management and processing of sample data as also the classification of earth observation data in either vector or raster form. The classification utilizes a large number of pixel- and object samples stored to a database allowing for multiple usages of those for training and validating of classifiers. With the C5.0, Maximum Likelihood Estimation, and Supported Vector Machines TWOPAC is currently supporting different modular classification methods. The software realizes OGC conform Web Processing Services which decreases the need for special commercial image classification software. The automated modular classification process chain is tested for several data sets from study areas in the Mekong Delta, and classifier stability and classification accuracy are analyzed. The method is considered to retrieve very good accuracy for stable and comparable classification results