Image data handling in spatial databases

The recent advances in database technology have enabled the development of a new generation of spatial databases, where the DBMS is able to manage spatial and non-spatial data types together. Most spatial databases can deal with vector geometries (e.g., polygons, lines and points), but have limited facilities for handling image data. However, the widespread availability of high-resolution remote sensing images has improved considerably the application of images to environmental monitoring and urban management. Therefore, it is increasingly important to build databases capable of dealing with images together with other spatial and non-spatial data types. With this motivation, this paper describes a solution for efficient handling of large image data sets in a standard object-relational database management system. By means of adequate indexing, compression and retrieval techniques, satisfactory performances can be achieved using a standard DBMS, even for very large satellite images. This work is part of the development of the TerraLib library, which aims to provide a comprehensive environment for the development of GIS applications

Image Data Handling In Spatial Databases

The recent advances in database technology have enabled the development of a new generation of spatial databases, where the DBMS is able to manage spatial and non-spatial data types together. Most spatial databases can deal with vector geometries (e.g., polygons, lines and points), but have limited facilities for handling image data. However, the widespread availability of high-resolution remote sensing images has improved considerably the application of images to environmental monitoring and urban management. Therefore, it is increasingly important to build databases capable of dealing with images together with other spatial and non-spatial data types. With this motivation, this paper describes a solution for efficient handling of large image data sets in a standard object-relational database management system. By means of adequate indexing, compression and retrieval techniques, satisfactory performances can be achieved using a standard DBMS, even for very large satellite images

TerraML: a Language to Support Spatial Dynamic Modeling

Abstract. Spatial Dynamic Modeling simulates spatio-temporal processes in which a location on the Earth’s surface changes due to some external driving force. This paper introduces TerraML, a dynamic modeling language to be used in environmental applications. TerraML supports both discrete and continuous change processes and generalized neighborhood to accommodate non-local actions.

TerraMAP: Uma Álgebra de Mapas Genérica Baseada em Predicados Espaciais

Este artigo descreve o projeto de um conjunto geral de operações de álgebra de mapas. Esta proposta generaliza a álgebra de mapas de Tomlin por incorporar os predicados espaciais topológicos e direcionais. O artigo também descreve a linguagem TerraMAP, que disponibiliza um conjunto geral de operações para álgebra de mapas. Nós argumentamos que TerraMAP é uma linguagem de álgebra de mapas mais geral e completa do que outras propostas da literatura de geoinformação

Visualização de imagens multiespectrais em sistemas gráficos

Pages: 513-51

Towards a generalized map algebra: principles and data types

Abstract. Map Algebra is a collection of functions for handling continuous spatial data, which allows modeling of different problems and getting new information from the existing data. There is an established set of map algebra functions in the GIS literature, originally proposed by Dana Tomlin. However, the question whether his proposal is complete is still an open problem in GIScience. This paper describes the design of a map algebra that generalizes Tomlin’s map algebra by incorporating topological and directional spatial predicates. Our proposal enables operations that are not directly expressible by Tomlin’s proposal. One of the important results of our paper is to show that Tomlin’s Map Algebra can be defined as an application of topological predicates to coverages. This paper points to a convergence between these two approaches and shows that it is possible to develop a foundational theory for GIScience where topological predicates are the heart of both object-based algebras and field-based algebras. 1