• Publications
  • Influence
TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications
TLDR
This chapter describes TerraLib, an open source GIS software library. Expand
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TerraLib: Technology in Support of GIS Innovation
TLDR
This paper outlines the rationale for the initial version of TerraLib, an open source software library aimed at providing a rich and powerful environment for the development of GIScience research. Expand
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Big earth observation data analytics: matching requirements to system architectures
TLDR
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. Expand
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Fields as a Generic Data Type for Big Spatial Data
TLDR
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. Expand
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TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping
TLDR
We developed TerraBrasilis, a spatial data analytics infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. Expand
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Web Services for Big Earth Observation data
TLDR
In this paper, we investigate what are the design decisions needed to implement web services for big Earth observation (EO) data. Expand
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A mobile application for geographical data gathering and validation in fieldwork (invited talk)
TLDR
We describe the architecture of a mobile application for geographical data gathering and validation in fieldwork. Expand
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Using dynamic geospatial ontologies to support information extraction from big Earth observation data sets
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Geographical information engineering in the 21st century
TLDR
This paper discusses the challenges facing GIS designers in the 21st century. Expand
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Big data streaming for remote sensing time series analytics using MapReduce
TLDR
We propose a combination of streaming and map-reduce for time series analysis of time series data. Expand
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