Manganese is a designated critical mineral, being industrially utilized for producing steel and batteries, including in the production of electric vehicles (Rozelle and others, 2021). The central Appalachian Valley and Ridge hosts hundreds of manganese and iron oxide mines that served steel production until their abandonment in the mid-twentieth century (Lesure, 1957; Pegau, 1958). Many relict mines still feature accessible pits, waste rock, and unmined ore materials to varying degrees. Preliminary assessments of supergene manganese oxides in the Appalachian Mountains have revealed extensive enrichment in critical minerals and rare earth elements (REE) (Carmichael and others, 2017; Odom, 2020). The Appalachian Manganese Oxide Research Effort (AMORE) was established to: (1) characterize the locations and extents of Appalachian manganese oxide mines using artificial intelligence mapping applied to high-resolution lidar elevation models and (2) assess the geochemical nature of remnant manganese oxide ore in the context of critical minerals. Here, we present digital vector data of manganese and iron mines and prospects within the Lewisburg 30 x 60 minute quadrangle generated by a semantic segmentation deep learning artificial intelligence model. The Lewisburg 30 x 60 minute quadrangle hosts several large areas of historic iron and manganese mines on federal lands (Lesure, 1957). Previously published maps (Lesure, 1957) document mining at the excavation to large trench scale and lack the resolution to document prospect pits and small trenches smaller than ~30 square meters (~323 square feet), which are often characteristic of mine workings throughout the quadrangle. Mineralization within the quadrangle is documented in the following scenarios: (1) at the contact between the Devonian Oriskany Sandstone and limestones of the Silurian and Devonian Helderberg Group, (2) disseminated within the Silurian Rose Hill Formation, and (3) within the damage zones of faults. Model outputs identified the locations of probable prospect pits, trenches, and large excavations which were then evaluated based on geologic context and subsequently culled based on their co-occurrence with known anthropogenic and karst features. The resulting dataset contains 2,054 features ranging in size from ~16 square meters to ~183,224 square meters (~172-1,972,207 square feet), documents probable mining and prospecting features in much greater detail than in previously published resources (for example, Lesure, 1957), and is a valuable resource for future work documenting existing and abandoned mine lands on federal lands.