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Data Fusion of LiDAR and Aerial Imagery to Map the Campus of Florida Atlantic University
Title
Data Fusion of LiDAR and Aerial Imagery to Map the Campus of Florida Atlantic University.
Creator
Gamboa, Nicole, Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
Reliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud ...
Show moreReliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud (elevation and intensity) data and aerial imagery. These were classified by Random Forest, k-Nearest Neighbor and Support Vector Machines classifiers. Shadow features were reclassified hierarchically in order to create a complete map. The Random Forest classifier used with the fused data set gave the highest overall accuracy at 82.3%, and a Kappa value at 0.77. When combined with the results from the shadow reclassification, the overall accuracy increased to 86.3% and the Kappa value improved to 0.82.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004595, http://purl.flvc.org/fau/fd/FA00004595
Subject Headings
Spatial analysis (Statistics), Geographic information systems., Cartography--Remote sensing., Thematic maps., Geospatial data--Mathematical models., Criminal justice, Administration of., African Americans, Violence against.
Format
Document (PDF)