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2 Abstract. Recent technological advances in geospatial data gathering have created massive data sets with better spatial and temporal resolution than ever. These large spatiotemporal data sets have motivated a challenge for Geoinformatics: how to model changes and design good quality software. Many existing spatiotemporal data models represent how objects(More)
This paper describes a software architecture for development of geographic databases that uses object-relational DBMS, like PostgreSQL and Oracle Spatial, sharing a common programming interface. This work is part of TerraLib, an open source software allowing a collaborative environment and use for the development of multiple GIS tools.
— Although KML files can be used to describe journeys, there is not a standard way to represent them as trajectories of moving objects for further analysis. In the KML schema, there is not a predefined tag to describe a moving object trajectory. Each software or mobile device that generates KML files with moving object trajectories uses its own structure(More)
Observations are our means to assess spatiotemporal phenomena in the real world. They are basic units for spatiotemporal data representation and are distributed by data providers using different formats and standards. In this work, we propose an approach to discover, access and integrate spatiotemporal observations from different kinds of data sources using(More)
This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent different types of spatiotemporal data, such as trajectories, time series, remote sensing and,(More)
This work presents a generic programming interface, or API (Application Programming Interface), for spatial operations in geographical database developed in the TerraLib environment-a base library for construction of geographical applications with integrated architecture. This API provides operations on geographical data stored in relational DBMS (RDBMS)(More)
Earth observation satellites produce petabytes of geospatial data. To manage large data sets, researchers need stable and efficient solutions that support their analytical tasks. Since the technology for big data handling is evolving rapidly, researchers find it hard to keep up with the new developments. To lower this burden, we argue that researchers(More)
The recent technological advances in geospatial data collection have created massive data sets with better spatial and temporal resolution than ever. To properly deal with these data sets, geographical information systems (GIS) must evolve to represent, access, analyze and visualize big spatiotemporal data in an efficient and integrated way. In this paper,(More)
Mobile devices, such as smartphones and tablets, are useful tools for in situ collecting information about spatial locations. In this paper, we describe the architecture of a mobile application for geographical data gathering and validation in fieldwork. This application is being developed based on well-established standards in order to assure spatial data(More)
Mobile devices, such as smartphones and tablets, are useful tools for in situ gathering information about spatial locations. Specialists need mobile geographical data acquisition and validation systems to be used in fieldwork and in places where there is limited or any network connectivity available. This paper presents an ongoing work on designing and(More)