The rapid growth in use of drones for scientific data capture is leading to exciting new avenues for research. Much of this growth is being driven by researchers and institutions that do not traditionally provide large scale dedicated remote sensing data management infrastructure leaving researchers to self deploy in-house solutions. At the same time the Semantic Web technology stack, and machine-to-machine API driven application development particularly have matured making everything from data publication, through cloud resource deployment, and use of remote models automatable. Within this context the LANDRS project is building APIs and foundational infrastructure to: (i) ease the burden on small science researchers capturing increasing amounts of data using drones, (ii) take full advantage of these new application technologies, and (iii) work towards practically solving the challenges involved in making drone captured data FAIR. These efforts include ontology development, supporting RDF python modules, and OpenAPI tooling to support automated drone data metadata capture.