GeoDMA—Geographic Data Mining Analyst

Abstract

Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in one year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote databases. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.

Improvements in Sample Selection Methods for Image Classification

“…In our proposal, the user may refine training samples and discover rules to be used in subsequent steps of the entire image analysis. The results presented in this article will be implemented in the GeoDMA system [11], and the current tests were performed using the TerraLib 5.0 library [12] for image processing.…”

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

“…Within this step, the linking and pruning functions are run recursively until no more frequent itemsets are generated. The Linking Function generates the candidate (m + 1)-itemsets from the m-itemsets by step-by-step linking without scanning the database, while the Pruning Function removes the false (m + 1)-itemsets according to Equation (4).…”

“…Its relationships with marine environments constitute a complex interrelated system [1], e.g., during a warm phase of the ENSO, a positive sea surface temperature (SST) and a negative sea level anomaly co-occur in the eastern tropical Pacific Ocean [2], and in a cool phase, an abnormal increase in dry conditions occurs over the Pacific Ocean, SSTs drop in the central Pacific Ocean, and SST gradients increase in the east-west Pacific Ocean, which indirectly dominate primary productivity and sea surface chlorophyll-a concentrations in the Pacific Ocean [3]. In recent decades, raster-formatted datasets derived from remote sensing images, reanalysis products and numerical simulations have provided an important source of data and offer new opportunities to improve our understanding of these relationships on a large scale [4,5].…”

A distância como variável em modelos configuracionais no estudo da distribuição de atividades econômicas urbanas

“…Ferramentas de mineração de dados também são recursos potenciais para aumentar o poder de análise de dados em estudos urbanos (Korting et al, 2013), uma vez que tais técnicas são capazes de rapidamente selecionar os atributos mais representativos de cada classe de objetos e gerar regras de classificação simples. Além disso, a mineração de dados pode lidar com grandes bases de dados complexos e ser adequada para enfrentar dinâmicas multidimensionais, como o crescimento urbano (e.g., Pinzón & Souza, 2016).…”