Extraction of Land Cover Units from Land Cover Components Based on Geometric Rules

Land cover units are aggregations of land cover components that are obtained by using criteria of homogeneity and proximity of basic components. For example, residential urban settlements can be defined as aggregations of single buildings, neighboring green spaces, paved surfaces and small roads, which are separated by more prominent land cover components, such as main roads or rivers. Land cover components belong to standard classes typically obtained by an automated classification process applied to aerial or satellite images, such as buildings, constructed areas, bare soil, water, vegetation, and the like. Land cover units belong to more general classes, obtained by a combination of land cover components, such as residential areas, industrial areas, road networks, river systems, and agricultural units. In this paper, we describe an approach based on the application of geometric rules and semantic constraints to extract land cover units from land cover components. We use spatial operators to extract composite land cover units from land cover databases, where spatial operators are taken from standards of the Open Geospatial Consortium. Expert knowledge needs to be translated into specific automatic procedures, called complex object definitions or CODs. Finally, we build a prototype system, where the user can choose among a set of available CODs to build a sequence of actions that lead to the discovery of knowledge. We discuss several study cases, such as the recognition of urban settlements, agricultural land units, and road networks

3D Cadastre visualization and dissemination: Most recent progresses and future directions

The 3D Cadastre has been investigated from many viewpoints (including legal, organizational and technical). However, to date little research has focused specifically on visualizationrelated aspects despite the value-added of the third dimension. The paper first proposes an overview of progress made in the last five years in 3D cadastral visualization. The authors then summarize discussions at the 2014 3D Cadastre workshop regarding future research and development on the topic. This synthesis is complemented by a broad review of the most recent advances in 3D visualization beyond the 3D cadastral domain, with the goal of providing a number of important directions for further work, allowing researchers, developers and users to consolidate their respective activities, and encouraging collaboration

extraction of land cover units from land cover components based on geometric rules

Land cover units are aggregations of land cover components that are obtained by using criteria of homogeneity and proximity of basic components. For example, residential urban settlements can be defined as aggregations of single buildings, neighboring green spaces, paved surfaces and small roads, which are separated by more prominent land cover components, such as main roads or rivers. Land cover components belong to standard classes typically obtained by an automated classification process applied to aerial or satellite images, such as buildings, constructed areas, bare soil, water, vegetation, and the like. Land cover units belong to more general classes, obtained by a combination of land cover components, such as residential areas, industrial areas, road networks, river systems, and agricultural units. In this paper, we describe an approach based on the application of geometric rules and semantic constraints to extract land cover units from land cover components. We use spatial operators to extract composite land cover units from land cover databases, where spatial operators are taken from standards of the Open Geospatial Consortium. Expert knowledge needs to be translated into specific automatic procedures, called complex object definitions or CODs. Finally, we build a prototype system, where the user can choose among a set of available CODs to build a sequence of actions that lead to the discovery of knowledge. We discuss several study cases, such as the recognition of urban settlements, agricultural land units, and road networks

A Conceptual Framework for Modelling Spatial Relations

Various approaches lie behind the modelling of spatial relations, which is a heterogeneous and interdisciplinary field. In this paper, we introduce a conceptual framework to describe the characteristics of various models and how they relate each other. A first categorization is made among three representation levels: geometric, computational, and user. At the geometric level, spatial objects can be seen as point-sets and relations can be formally defined at the mathematical level. At the computational level, objects are represented as data types and relations are computed via spatial operators. At the user level, objects and relations belong to a context-dependent user ontology. Another way of providing a categorization is following the underlying geometric space that describes the relations: we distinguish among topologic, projective, and metric relations. Then, we consider the cardinality of spatial relations, which is defined as the number of objects that participate in the relation. Another issue is the granularity at which the relation is described, ranging from general descriptions to very detailed ones. We also consider the dimension of the various geometric objects and the embedding space as a fundamental way of categorizing relations

3D Cadastres Best Practices, Chapter 5: Visualization and New Opportunities

This paper proposes a discussion on opportunities offered by 3D visualization to improve the understanding and the analysis of cadastre data. It first introduce the rationale of having 3D visualization functionalities in the context of cadastre applications. Second the publication outline some basic concepts in 3D visualization. This section specially addresses the visualization pipeline as a driven classification schema to understand the steps leading to 3D visualization. In this section is also presented a brief review of current 3D standards and technologies. Next is proposed a summary of progress made in the last years in 3D cadastral visualization. For instance, user’s requirement, data and semiotics, and platforms are highlighted as main actions performed in the development of 3D cadastre visualization. This review could be perceived as an attempt to structure and emphasise the best practices in the domain of 3D cadastre visualization and as an inventory of issues that still need to be tackled. Finally, by providing a review on advances and trends in 3D visualization, the paper initiates a discussion and a critical analysis on the benefit of applying these new developments to cadastre domain. This final section discusses about enhancing 3D techniques as dynamic transparency and cutaway, 3D generalization, 3D visibility model, 3D annotation, 3D data and web platform, augmented reality, immersive virtual environment, 3D gaming, interaction techniques and time

Reasoning with Mixed Qualitative-Quantitative Representations of Spatial Knowledge

Drastic transformations in human settlements are caused by extreme events. As a consequence, descriptions of an environment struck by an extreme event, based on spatial data collected before the event, become suddenly unreliable. On the other hand, time critical actions taken for responding to extreme events require up-to-date spatial information. Traditional methods for spatial data collection are not able to provide updated information rapidly enough, calling for the development of new data collection methods. Reports provided by actors involved in the response operations can be considered as an alternative source of spatial information. Indeed, reports often convey spatial descriptions of the environment. The extraction of spatial descriptions from such reports can serve a fundamental role to update existing information which is usually maintained within, and by means of, Geographic Information Systems. However, spatial information conveyed by human reports has qualitative characteristics, that strongly differ from the quantitative nature of spatial information stored in Geographic Information Systems. Methodologies for integrating qualitative and quantitative spatial information are required in order to exploit human reports for updating existing descriptions of spatial knowledge. Although a significant amount of research has been carried on how to represent and reason on qualitative data and qualitative information, relatively little work exists on developing techniques to combine the different methodologies. The work presented in this thesis extends previous works by introducing a hybrid reasoning system--able to deal with mixed qualitative-quantitative representations of spatial knowledge--combining techniques developed separately for qualitative spatial reasoning and quantitative data analysis. The system produces descriptions of the spatial extent of those entities that have been modified by the event (such as collapsed buildings), or that were not existing before the event (such as fire or ash clouds). Furthermore, qualitative descriptions are produced for all entities in the environment. The former descriptions allow for overlaying on a map the information interpreted from human reports, while the latter triggers warning messages to people involved in decision making operations. Three main system functionalities are investigated in this work: The first allows for translating qualitative information into quantitative descriptions. The second aims at translating quantitative information into qualitative relations. Finally, the third allows for performing inference operations with information given partly qualitatively and partly quantitatively for boosting the spatial knowledge the system is able to produce

Reasoning with Mixed Qualitative-Quantitative Representations of Spatial Knowledge

Drastic transformations in human settlements are caused by extreme events. As a consequence, descriptions of an environment struck by an extreme event, based on spatial data collected before the event, become suddenly unreliable. On the other hand, time critical actions taken for responding to extreme events require up-to-date spatial information. Traditional methods for spatial data collection are not able to provide updated information rapidly enough, calling for the development of new data collection methods. Reports provided by actors involved in the response operations can be considered as an alternative source of spatial information. Indeed, reports often convey spatial descriptions of the environment. The extraction of spatial descriptions from such reports can serve a fundamental role to update existing information which is usually maintained within, and by means of, Geographic Information Systems. However, spatial information conveyed by human reports has qualitative characteristics, that strongly differ from the quantitative nature of spatial information stored in Geographic Information Systems. Methodologies for integrating qualitative and quantitative spatial information are required in order to exploit human reports for updating existing descriptions of spatial knowledge. Although a significant amount of research has been carried on how to represent and reason on qualitative data and qualitative information, relatively little work exists on developing techniques to combine the different methodologies. The work presented in this thesis extends previous works by introducing a hybrid reasoning system--able to deal with mixed qualitative-quantitative representations of spatial knowledge--combining techniques developed separately for qualitative spatial reasoning and quantitative data analysis. The system produces descriptions of the spatial extent of those entities that have been modified by the event (such as collapsed buildings), or that were not existing before the event (such as fire or ash clouds). Furthermore, qualitative descriptions are produced for all entities in the environment. The former descriptions allow for overlaying on a map the information interpreted from human reports, while the latter triggers warning messages to people involved in decision making operations. Three main system functionalities are investigated in this work: The first allows for translating qualitative information into quantitative descriptions. The second aims at translating quantitative information into qualitative relations. Finally, the third allows for performing inference operations with information given partly qualitatively and partly quantitatively for boosting the spatial knowledge the system is able to produce