This book provides readers with an insight into the development of a novel method for regridding gridded spatial data, an operation required to perform the map overlay operation and apply map algebra when processing spatial data. It introduces the necessary concepts from spatial data processing and fuzzy rulebase systems and describes the issues experienced when using current regridding algorithms. The main focus of the book is on describing the different modifications needed to make the problem compatible with fuzzy rulebases. It offers a number of examples of out-of-the box thinking to handle aspects such as rulebase construction, defuzzification, spatial data comparison, etc. At first, the emphasis is put on the newly developed method, and additional datasets containing information on the underlying spatial distribution of the data are identified. After this, an artificial intelligent system (in the form of a fuzzy inference system) is constructed using this knowledge and then applied on the input data to perform the regridding. The book offers an example of how an apparently simple problem can pose many different challenges, even when trying to solve it with existing soft computing technologies. The workflow and solutions to solve these challenges are universal and may therefore be broadly applied into other contexts.
Artificial Intelligent Methods for Handling Spatial Data
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$184.00
Description
This book provides readers with an insight into the development of a novel method for regridding gridded spatial data, an operation required to perform the map overlay operation and apply map algebra when processing spatial data. It introduces the necessary concepts from spatial data processing and fuzzy rulebase systems and describes the issues experienced when using current regridding algorithms. The main focus of the book is on describing the different modifications needed to make the problem compatible with fuzzy rulebases. It offers a number of examples of out-of-the box thinking to handle aspects such as rulebase construction, defuzzification, spatial data comparison, etc. At first, the emphasis is put on the newly developed method, and additional datasets containing information on the underlying spatial distribution of the data are identified. After this, an artificial intelligent system (in the form of a fuzzy inference system) is constructed using this knowledge and then applied on the input data to perform the regridding. The book offers an example of how an apparently simple problem can pose many different challenges, even when trying to solve it with existing soft computing technologies. The workflow and solutions to solve these challenges are universal and may therefore be broadly applied into other contexts.
This volume is based on the reviewed and edited proceedings of the International Symposium on Spatial Data Handling 2012, held in Bonn. The 15th SDH brought together scholars and professionals from...
Since the first symposium in 1984 the International Symposia on Spatial Data Handling (SDH) has become a major resource for recent advances in GIS research. The International Symposium on Spatial...
The International Symposium on Spatial Data Handling is the premier research forum for Geographic Information Science. The Symposium is particularly strong in respect to identifying significant new ...
This book contains a selection of papers from the 16th International Symposium on Spatial Data Handling (SDH), the premier long-running forum in geographical information science. This collection...
Spatial information is pervaded by uncertainty. Indeed, geographical data is often obtained by an imperfect interpretation of remote sensing images, while people attach ill-defined or ambiguous...
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