GeoPAT: Spatial and Temporal Pattern Analysis Using a Similarity Approach

Tutorial Description:

This tutorial focuses on spatial analysis of categorical and temporal data using a similarity approach. It introduces the Geospatial Pattern Analysis Toolbox (GeoPAT). GeoPAT is a collection of modules for performing pattern-based geospatial analysis on categorical images and other spatial datasets,  including spatial time series. GeoPAT has also been successfully used to analyze medical images and periodic signals. This package was developed for NASA as part of a NASA project. The GeoPAT modules implement common spatial algorithms such as query/search, overlay, clustering, and segmentation. Participants will become familiar with the use of a similarity approach to pattern-based spatial analysis. The tutorial will guide them through common processing paths and show them how to apply such an approach to different datasets. 

Prof. dr hab.  Paweł Netzel 

Dr hab. Paweł Netzel prof. URK Pawel Netzel is a mathematician, climatologist, spatial analyst and software developer. He received his M.Sc. in Applied Mathematics. His Ph.D. thesis focused on the application of acoustic radar (SODAR) in climatology. Paweł Netzel has participated in a number of projects. Some of them were based on Free and Open Source Software. He is a founding member of OSGeo Chapter Poland. Since 2015 he is a member of IEEE, since 2016 - a member of IEEE Geoscience and Remote Sensing Society (GRSS) and International Association for Mathematical Geosciences (IAMG). His areas of interest are spatial analysis, big data, computer programming, mathematical modeling, machine learning and artificial intelligence.