Former field systems (FFS) are the most widespread traces of ancient activities in present European landscapes and represent significant perturbations to ecosystems. Through its ability to penetrate forest canopies and detect microlandforms, Airborne Laser Scanning data reveal archaeological relics over large areas, from periods older than the first available Historical Topographic Maps. Mapping these traces from ALS-derived data (e.g. Digital Elevation Model (DEM)) thus allows for a determination of a new temporal baseline in order to evaluate the effects of a longer history on current patterns of biodiversity. Here, we evaluate the ability of traditional machine learning (Random Forest-RF) and deep learning (Fully Connected Networks-FCN) models to detect Medieval Terraced slopes and Ridges and Furrows (RaF) from an ALS-derived DEM in the southern Vosges (1462 km2). We used a combination of Local Binary Patterns and topographical metrics to measure properties of FFS and to train detection models. We then assessed the relative performance of each model semantically and spatially. Our results demonstrated the high suitability of our approach for reproducing major trends in the landscape with a high level of similarity between the predicted and reference spatial patterns (Structural Similarity Index - SSIM 0.75). RF outperformed FCN for Terraced Slopes, whilst minimizing the false positive rate. FCN slightly outperformed RF for the RaF dataset but showed promising abilities to survey unseen data with a low sensitivity to annotation errors. We suggest that this approach has the potential to offer new spatio-temporal possibilities in Historical Ecology studies as a means of automatically detecting past archaeological ecosystems from a landscape to a regional scale.