Loss of Control In-flight (LOC-I) remains a leading cause of fatal accidents in commercial aviation, with aerodynamic stall identified as a frequent precursor. Effective Upset Prevention and Recovery Training (UPRT) requires representative Flight Simulation Training Devices (FSTDs), which in turn depend on accurate stall models. Current models are typically derived through in-flight system identification, but rely solely on global aircraft states and thus depend on assumptions about the onset and progression of flow separation. This study introduces a methodology to derive quantitative flow-separation maps from in-flight recordings of tufts and flow cones on the PH-LAB research aircraft during stall maneuvers.
High-speed video was analyzed through a dedicated image-processing pipeline to classify tuft states and reconstruct separation patterns. The resulting maps provide direct, spatially resolved evidence of stall onset and progression. This allows for a direct validation of existing Kirchhoff-inspired formulations, while highlighting their shortcomings in capturing spanwise variations and local effects of separation. Furthermore, synchronization with flight-test data allows for correlation of tuft measurements with global aircraft states and supports the development of a logistic model to describe local separation behavior. Beyond validating existing models, the proposed approach establishes a robust experimental framework for integrating flow visualization into stall model identification, with direct implications for the improvement of stall models and, ultimately, flight safety.