Open AccessLocal Search for Optimal Global Map Generation Using Middecadal Landsat Images
Author(s) -
Morris Robert A.,
Gasch John,
Khatib Lina
Publication year - 2009
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v30i2.2233
Subject(s) - thematic mapper , thematic map , global map , solver , computer science , interface (matter) , generator (circuit theory) , remote sensing , constraint (computer aided design) , geological survey , data mining , geography , cartography , artificial intelligence , engineering , satellite imagery , geology , mechanical engineering , paleontology , power (physics) , physics , bubble , quantum mechanics , maximum bubble pressure method , parallel computing , robot , programming language
NASA and the United States Geological Survey (USGS) are collaborating to produce a global map of the Earth using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) remote sensor data from the period of 2004 through 2007. The map is composed of thousands of scene locations, and for each location there are tens of different images of varying quality to choose from. Constraints and preferences on map quality make it desirable to develop an automated solution to the map‐generation problem. This article formulates a global map‐generator problem as a constraint‐optimization problem (GMG‐COP) and describes an approach to solving it using local search. The article also describes the integration of a GMG solver into a graphical user interface for visualizing and comparing solutions, thus allowing for solutions to be generated with human participation and guidance.