<p> This dissertation lies at the intersection of two current trends in technology, both of which are increasing with the spread of high speed internet: location-based services (geospatial technologies) and Free and Open Source Software (FOSS). Chapter one explores the nature of geospatial technology knowledge diffusion by investigating the spread of OSGeo-Live, a collection of pre-existing geospatial FOSS packaged specifically for demonstration and education purposes. The analysis examines the distribution of downloaders, contributors and translators then assesses the potential barriers to knowledge diffusion including economic, technical and socio-cultural factors. Chapter two describes a new online tool built with FOSS designed to bring traditional cartographic knowledge to a new medium, the internet, and a new audience, web developers and other non-traditional internet-based cartographers. The tool allows users to compare several base map options simultaneously, providing guidance on how to assess the appropriateness of a base map for a particular map composition. Chapter three uses geospatial FOSS software to conduct verification of a habitat fragmentation model with GPS tracking data. By demonstrating a common type of analysis for natural resource scientists, the methods provide an example for traditional types of geospatial analysis emphasizing repeatability, shareability, and usability by field science practitioners not just geospatial specialists. These studies represent an initial foray into understanding geospatial technology diffusion and modern geospatial technologies, specifically with geospatial free and open source software.</p>

<p> Recent approaches to crater detection have been inspired by face detection's use of gray-scale texture features. Using gray-scale texture features for supervised machine learning crater detection algorithms provides better classification of craters in planetary images than previous methods. When using Haar features it is typical to generate thousands of numerical values from each candidate crater image. This magnitude of image features to extract and consider can spell disaster when the application is an entire planetary surface. One solution is to reduce the number of features extracted and considered in order to increase accuracy as well as speed. Feature subset selection provides the operational classifiers with a concise and denoised set of features by reducing irrelevant and redundant features. Feature subset selection is known to be NP-hard. To provide an efficient suboptimal solution, four genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. Inspired by analysis regarding the relationship between subset size and accuracy, a squeezing algorithm is presented to shrink the genetic algorithm's chromosome cardinality during the genetic iterations. A significant increase in the classification performance of a Bayesian classifier in crater detection using image texture features is observed.</p>