Wetlands for the entire state of Pennsylvania (119  ,  282  km²) were mapped using object-based image analysis (OBIA). The OBIA combined vector output from logistic statistical models informed by hydrogeologic variables (e.g., hydrology facets, soils, and climate) with LiDAR-derived terrain variables, high-resolution aerial orthoimagery, and land-cover data through a data-fusion approach. Wetland presence data and hydrogeologic features in a geographic information system were used to develop moderate-resolution (10  m²) maximum-entropy models (Maxent) both for wetlands dominated by low-lying emergent vegetation and woody vegetation, respectively. Terrain variables describing slope, landscape wetness, surface elevation, climate, precipitation, and poorly drained soils were most strongly associated with occurrences of wetlands in this modeling procedure. Maxent derived models accurately predicted validation wetlands: Area under curve of 0.91 for woody wetlands and 0.93 for emergent wetlands. High-resolution (1  m²) mapping was then performed in the OBIA by creating objects based on a compound topographic index (CTI) and leaf-off true-color orthoimagery and then classifying them using a combination of the CTI, imagery, and 10-m² statistical models. An accuracy assessment found that the OBIA correctly classified 81.5% of validation locations compared to only 59.3% for National Wetlands Inventory (NWI), although commission rates were higher for OBIA (15.6%) versus NWI (3.4%). The user’s accuracy for OBIA was 82.8% compared to 78.1% for NWI. Our work suggests advances in remote sensing techniques and a data-fusion approach may facilitate improved wetland mapping accuracy while reducing processing time relative to traditional approaches.