After a heyday in the 1970s and 1980s, probability sampling became much less visible in archaeological literature as it came under assault from the post-processual critique and the widespread adoption of “full-coverage survey.” After 1990, published discussion of probability sampling rarely strayed from sample-size issues in analyses of artifacts along with plant and animal remains, and most textbooks and archaeological training limited sampling to regional survey and did little to equip new generations of archaeologists with this critical aspect of research design. A review of the last 20 years of archaeological literature indicates a need for deeper and broader archaeological training in sampling; more precise usage of terms such as “sample”; use of randomization as a control in experimental design; and more attention to cluster sampling, stratified sampling, and nonspatial sampling in both training and research.

After acquiring 91 km2 of lidar data from the Zacapu region, West Mexico, we confronted a series of issues that most archaeologists using this technology face. These include the large volume of data available, the limited training of potential “analysts,” the difficult development of a collective mapping tool and protocol, and the reliability of desk-based interpretation of archaeological features. In this article, we present an initiative conducted in 2015 and 2017 as an attempt to answer these methodological and pedagogical issues. We developed a web mapping platform to collectively interpret archaeological features using lidar-derived imagery and to train volunteer students to participate in this desk-based web mapping within a crowdsourcing framework. After evaluating the results of this initiative, we discuss the potential and limitations of this method for both lidar-based research and future training.