Across Appalachia, Unarmed Aircraft System (UAS) programs are revealing what land managers could never see from the ground. They are identifying hidden mine fires, unstable slopes, invasive species, and early signs of ecological recovery. While many people associate UAS with wildfire mapping or law enforcement, one of their most important uses is happening quietly on former coal mines and legacy energy infrastructure. In these places, UASs are transforming how we understand and restore some of the region's most challenging landscapes.

For the Office of Surface Mining Reclamation and Enforcement (OSM), this transformation began more than a decade ago. The Appalachian Region Technical Support Division (AR TSD) began its UAS program in 2011, using surplus military aircraft. In 2012, the program expanded through a partnership with the U.S. Geological Survey to determine whether aerial data could make mine investigations safer, more efficient, and how it can best support reclamation science.

Today, OSM Director Lanny Erdos said years of dedicated work have resulted in the efficient use of UAS technology to facilitate data-driven reclamation. "The program, using the latest UAS technology, brings to bear a variety of resources in greatly informing our reclamation work," said Erdos, who noted his agency currently uses the Skydio X10, which carries a wide-view camera, a narrow-view camera, and a thermal sensor that identifies temperature variations across landscapes. "Years of experience with these tools have prepared the agency to team up with partners like the Pittsburgh Botanic Garden to demonstrate the real-world impact of drones on the key work we do."

This progress connects directly to the bureau's broader scientific mission. Since 2005, the National Applied Science Program has supported research dedicated to developing new reclamation science and technology for both the coal industry and for public protection and environmental health in coal communities. During that time, the program has invested in more than one hundred projects led by universities, state agencies, and nonprofit partners. Many of the tools that are in use today, including modern UAS-based workflows, are the result of this steady investment in practical scientific solutions. Building on these long-running efforts, a recent Applied Science project explored how modern UAS tools and AI could be used to tackle a persistent reclamation challenge.

In 2022, a research team at West Virginia University launched a project titled: Machine Learning and UAS for Managing Autumn Olive on Reclaimed Surface Mines to explore how UAS and machine -learning could help land managers address Autumn Olive, an invasive shrub that spreads quickly across reclaimed coal lands in Appalachia. The team used high-resolution color aerial imagery to train a computer deep-learning models to identify the plant at different growth stages. This approach gives land managers a faster way to locate new infestations and focus restoration work where it will have the greatest impact. This project demonstrates how partnering machine-learning models with UAS flights can result in powerful tools that can be used for early detection and management of invasive species.

To understand how well the model worked outside its original study area, AR TSD conducted a test flight at the Pittsburgh Botanic Garden. The Garden offered an ideal setting to evaluate the model because portions of the garden had recently been reclaimed and restored with modern reclamation techniques, such as the Forestry Reclamation Approach, . The site offered a mixture of restored terrain and diverse vegetation, allowing the team to see how well the model performed in a different setting. Successful detection of Autumn Olive in this environment will help land managers assess the progress of reclamation and plan more targeted management strategies.

Bureau staff conducted a drone flight using the Skydio X10 on Oct. 22, 2025, at the Gardens. Although the purpose of the flight was to maintain pilot proficiency, the imagery collected over a recently restored area also served to support the evaluation of the Applied Science machine-learning model. The data will help determine whether the model can successfully identify Autumn Olive in locations beyond the original research site. As the analysis continues, the team aims to show how the combination of UAS and machine-learning can support stronger invasive species management and healthier restored mine lands throughout the region.

What began as early experimentation with surplus equipment has grown into a powerful scientific capability. UASs are now helping to advance reclamation, protect communities, and guide stewardship of the region's legacy energy landscapes. As technology continues to improve, the story of drone-based mine land science is only at the beginning. Want to see how this work comes together behind the scenes? Explore these resources: