Ensemble Learning for Semantic Segmentation of Ancient Maya Architectures

Deep learning methods hold great promise for the automatic analysis of large-scale remote sensing data in archaeological research. Here, we present a robust approach to locating ancient Maya architectures (buildings, aguadas, and platforms) based on integrated segmentation of satellite imagery and aerial laser scanning data. Deep learning models with different architectures and loss functions were trained and combined to form an ensemble for pixel-wise classification. We applied both training data augmentation as well as test-time augmentation and performed morphological cleaning in the postprocessing phase. Our approach was evaluated in the context of the “Discover the mysteries of the Maya: An Integrated Image Segmentation Challenge” at ECML PKDD 2021 and achieved one of the best results with an average IoU of 0.8183.