In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.
","abstract_html":"<p>In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.</p>","abstract_has_math":false,"creators":["Watts-Willis, Tristan A."],"institution":null,"degree_name":"Master of Science in Engineering (M.S.Eng.)","degree_level":"Thesis - Pacific Access Restricted","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":["Elizabeth Basha"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T05:36:00Z","subjects":["Robotics","Artificial intelligence","Applied sciences","Automated classifier","Classification","Efficiency","Feature selection","Model selection","Unmanned aerial vehicle","Engineering"],"languages":[],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9781369757897"],"render_values":[{"text":"9781369757897","href":null,"code":true}]}]},"links":{"outbound_url":"https://scholarlycommons.pacific.edu/uop_etds/224","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Elizabeth Basha"]},{"key":"dc:creator","label":"Author","values":["Watts-Willis, Tristan A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-29T09:05:10Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Pacific Access Restricted"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Engineering (M.S.Eng.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Robotics","Artificial intelligence","Applied sciences","Automated classifier","Classification","Efficiency","Feature selection","Model selection","Unmanned aerial vehicle","Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["9781369757897","https://scholarlycommons.pacific.edu/uop_etds/224"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.
"]},{"key":"dc:source","label":"Dc Source","values":["80"]},{"key":"dc:title","label":"Title","values":["Autonomous model selection for surface classification via unmanned aerial vehicle"]}]}],"canonical_facts":{"dc:contributor":["Elizabeth Basha"],"dc:creator":["Watts-Willis, Tristan A."],"dc:date.available":["2018-06-29T09:05:10Z"],"dc:description.abstract":["In the pursuit of research in remote areas, robots may be employed to deploy sensor networks. These robots need a method of classifying a surface to determine if it is a suitable installation site. Developing surface classification models manually requires significant time and detracts from the goal of automating systems. We create a system that automatically collects the data using an Unmanned Aerial Vehicle (UAV), extracts features, trains a large number of classifiers, selects the best classifier, and programs the UAV with that classifier. We design this system with user configurable parameters for choosing a high accuracy, efficient classifier. In support of this system, we also develop an algorithm for evaluating the effectiveness of individual features as indicators of the variable of interest. Motivating our work is a prior project that manually developed a surface classifier using an accelerometer; we replicate those results with our new automated system and improve on those results, providing a four-surface classifier with a 75% classification rate and a hard/soft classifier with a 100% classification rate. We further verify our system through a field experiment that collects and classifies new data, proving its end-to-end functionality. The general form of our system provides a valuable tool for automation of classifier creation and is released as an open-source tool.
"],"dc:identifier":["9781369757897","https://scholarlycommons.pacific.edu/uop_etds/224"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:source":["80"],"dc:subject":["Robotics","Artificial intelligence","Applied sciences","Automated classifier","Classification","Efficiency","Feature selection","Model selection","Unmanned aerial vehicle","Engineering"],"dc:title":["Autonomous model selection for surface classification via unmanned aerial vehicle"],"thesis:degree_discipline":["Engineering"],"thesis:degree_level":["Thesis - Pacific Access Restricted"],"thesis:degree_name":["Master of Science in Engineering (M.S.Eng.)"]},"updated_at":"2026-07-24T05:36:00Z"}