Lyimo, Neema NicodemusLuo, FangCheng, QiminPeng, Hao2023-08-092023-08-092020http://www.suaire.sua.ac.tz/handle/123456789/5585Journal ArticleQuality assessment of training samples collected from hetero- geneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncer- tainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geo- graphic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre- classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to de- velop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification.enUrban areaLandsat ImageryData SetsArticle