Assessing the lidar revolution in the Maya lowlands: A geographic approach to understanding feature classification accuracy. (April 2023) - Garrison, Thomas G; Thompson, Amy E; Krause, Samantha; Eshleman, Sara; Fernandez-Diaz, Juan C; Baldwin, J Dennis; Cambranes, Rafael
It has been well over a decade since lidar-based research began in earnest in the Maya Lowlands of southern Mexico, Guatemala, Belize, and Honduras. Most investigations have an archaeological focus, with a few integrating studies of the ancient Maya with analyses of local ecology and land-use. A review of frequently cited publications reveals a lack of consistency in assessing the accuracy of archaeological feature classifications in lidar data with variables such as sensor type, class definitions, and ground-truthing methods differentially affecting assessment results across the Lowlands. In general, area-based ground-truthing approaches to classifications of full waveform lidar data present the most comprehensive accuracy assessments. New assessment data from the Buenavista Valley of north-central Guatemala are presented to compare against existing studies and to demonstrate how a geographic approach (a comprehensive, landscape-scale study of features over space and time) to classification error assessment can enhance understanding of classification accuracy. Results show that meaningful comparisons of archaeological features across lidar datasets cannot be considered reliable without more uniform and detailed presentations of accuracy assessment methods, analyses, and results. The article concludes with recommendations for how such collaborations might proceed.