Karine Reis Ferreira

TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications

This chapter describes TerraLib, an open source GIS software library. The design goal for TerraLib is to support large-scale applications using socio-economic and environmental data. TerraLib supports coding of geographical applications using spatial databases, and stores data in different database management systems including MySQL and PostgreSQL. Its vector data model is upwards compliant with Open Geospatial Consortium (OGC) standards. It handles spatio-temporal data types (events, moving objects, cell spaces, modifiable objects) and allows spatial, temporal, and attribute queries on the database. TerraLib supports dynamic modeling in generalized cell spaces, has a direct runtime link with the R programming language for statistical analysis, and handles large image data sets. The library is developed in C++, and has programming interfaces in Java and Visual Basic. Using TerraLib, the Brazilian National Institute for Space Research (INPE) developed the TerraView open source GIS, which provides functions for data conversion, display, exploratory spatial data analysis, and spatial and non-spatial queries. Another noteworthy application is TerraAmazon, Brazil’s national database for monitoring deforestation in the Amazon rainforest, which manages more than 2 million complex polygons and 60 gigabytes of remote sensing images.

Fields as a Generic Data Type for Big Spatial Data

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, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.

Big earth observation data analytics: matching requirements to system architectures

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 should not have to convert their algorithms to specialised environments. Imposing a new API to researchers is counterproductive and slows down progress on big data analytics. This paper assesses the cost of research-friendliness, in a case where the researcher has developed an algorithm in the R language and wants to use the same code for big data analytics. We take an algorithm for remote sensing time series analysis on compare it use on map/reduce and on array database architectures. While the performance of the algorithm for big data sets is similar, organising image data for processing in Hadoop is more complicated and time-consuming than handling images in SciDB. Therefore, the combination of the array database SciDB and the R language offers an adequate support for researchers working on big Earth observation data analytics.

A mobile application for geographical data gathering and validation in fieldwork (invited talk)

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 interoperability between existing Spatial Data Infrastructures (SDI) and mobile systems.

Moving Objects and KML Files

Although KML files can be used to describe journeys, there is not a standard way to represent them as moving object trajectories for further analysis. In the KML schema, there is not a predefined element to describe a moving object trajectory. Each software or mobile device that generates KML files with trajectories uses its own structure for representing them. Therefore, this work proposes an interoperable way to extract moving object trajectories from any KML file, based on the processing of an additional metadata file. This metadata file is an XML that must be compliant with an XML schema proposed in this paper. This proposal has been implemented in a geographical software library as a proof of concept.

An Algorithm to Discover Partners in Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many researches on moving object trajectory data mining. In this paper, we propose an efficient method to discover partners in moving object trajectories. Such method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. We present two case studies using the proposed algorithm.

Clustering Methods to Asses Land Cover Samples of MODIS Vegetation Indexes Time Series

MODIS vegetation indexes time series have been widely used to build land cover change maps on large scales. In this scope, to obtain good quality maps using supervised classification methods, it is crucial to select representative training samples of land cover change classes. In this paper, we evaluate two clustering methods, Hierarchical and Self-Organizing Map (SOM), to assess land cover samples of MODIS vegetation indexes time series. As we show, these techniques are suitable tools for assisting users to select representative land cover change samples from MODIS vegetation indexes time series. We present the accuracy of both methods for a case study in Ipiranga do Norte municipality in Mato Grosso state, Brazil.