Ricardo Cartaxo Modesto de Souza

Spring: Integrating remote sensing and gis by object-oriented data modelling

Abstract This work discusses the design and implementation of SPRING, a geographical information system designed to support environmental projects over large spatial data base. SPRING is based on an object-oriented data model, which caters for the diversity of data sources and formats, and combines the ideas of “fields” and “objects”. The system includes functions for image processing, geographical analysis and digital terrain modelling, integrated with a spatial data base environment. The paper describes the project objectives, the object-oriented data model, the LEGAL spatial language and system functionality.

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.

Remote-sensing image mining: detecting agents of land-use change in tropical forest areas

Land remote‐sensing images are the primary means of assessing land change. There have been major land changes in the planet in the last decades, especially in tropical forest areas. Identifying the agents of deforestation is important for establishing public policies that can help preserve the environment. This paper proposes a method for detecting the agents of land change in remote‐sensing image databases. We associate each land‐change pattern, detected in a remote‐sensing image, to one of the agents of change. The proposed method uses a decision‐tree classifier to describe shapes found in land‐use maps extracted from remote‐sensing images and then associates these shape descriptions to the different types of social agents involved in land‐use change. We support our proposal with two case studies for detecting land‐change agents in Amazonia, using the remote‐sensing image database of the Brazilian National Institute for Space Research (INPE).

Mining patterns of change in remote sensing image databases

Remote sensing image databases are the fastest growing archives of spatial information. However, we still have a limited capacity for extracting information from large remote sensing image databases. There are currently very few techniques for image data mining and information extraction in large image data sets, and thus we are failing to exploit our large remote sensing data archives. This paper proposes a methodology to provide guidance for mining remote sensing image databases. The basic idea is to use domain concepts to build generic description of patterns in remote sensing images, and then use structural approaches to identify such patterns in images. We illustrate our proposal with a case study for detecting land use patterns in Amazonia from INPEs remote sensing image database.

Free and Open Source GIS: Will There Ever Be a Geo-Linux?

This paper examines the constraints that limit the large-scale adoption of open source GIS. Although the open source GIS community has already achieved relevant results, their products have a small market share. There is equivalent to Linux and Apache in the open source GIS scene. We try to explain why this happens, by considering some factors that control the evolution and adoption of open source software. Our view is that the community effort is split in many different systems, not allowing a dominant solution to come forth. Thus, none of the current open source GIS has the potential to be a disruptive technology. Then, we consider a future scenario where most public geospatial data will be available as open access policy. This scenario is becoming more probable given recent data policy decision in Europe, USA and other countries. In this scenario, there is a major chance for a disruptive open source GIS to appear.