Classification accuracy assessments are arguably the most important act of the classification process – without it, the results are an uncertain collection of opinions. It is the measurement of error, both thematic and spatial, that provides confidence in the quality of the data, its relative strengths and weaknesses across classes, its locational reliability, and allows scientists a means to communicate its value and limitations for particular applications not only to one another, but to customers and policy makers. Working towards a greater understanding of how bias can differentially impact modern classification methods such as deep CNNs is key to advancing the state of this field, and ensuring that we are properly assessing and mitigating bias across the wider computer vision and remote sensing community.
Approve for public release, 20-069.
|Discussant||Kevin Magee NGA||15||12:00 AM|
|Presenter||Nathan Trombley*, Oak Ridge Institute for Science and Education, Leveraging Multiple Methodologies For High-Resolution Mapping of Data-Limited Subpopulations: A Case Study Using ORNL’s UrbanPop to Strengthen LandScan USA||15||12:00 AM|
|Presenter||LASYA VENIGALLA*, University of Texas, Dallas, Fang Qiu, Professor and Head of the Department, Geospatial Information Sciences, University of Texas, Dallas, Extracting Urban Features using Neuro-Fuzzy Classifier||15||12:00 AM|
|Presenter||Haile K Tadesse, ORISE/Environmental Protection Agency (EPA), Durham, Maction K. Komwa*, George Mason University, John J Qu, George Mason University, Asmelash Berhane, Mekelle University, Mekelle, Ethiopia, Alonso A Aguirre, George Mason University, Colin Flynn, George Mason University, Land Cover Change Detection Analysis Using Thematic Mapper Data and GIS in Ethiopia||15||12:00 AM|
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