An introduction in the use of small unmanned aerial systems for mapping is becoming a desirable course for undergraduate students. The controllability of drone aircraft and quality cameras, even in less expensive aerial models, offers opportunities for students to learn and collect their own imagery for a variety of applications. This opportunity can be taught with an active learning approach. The challenge for instructors is to cover the three fundamentals in the remote sensing workflow -- planning, collection, and image processing -- while minimizing logistical issues associated with actual flight operation. The logistical issues (e.g. policy, legal, safety, weather) for an outdoor aerial drone learning experience are quite daunting, particularly for urban campuses with local/state restrictions on the use of drone aircraft. In this article, we separate the fundamental learning concepts in the remote sensing workflow from the flight operation and provide a learning environment for unlimited experiments. Using a synthetic indoor scaled landscape, students are given the opportunity for repeat experiments in a tightly controlled environment, saving the focus on actual flight operation for later times in the semester. The advantages of the indoor landscape are many, and also minimize the logistical issues for actual flight operation.