A preliminary study of the morphology and spatial distribution of funerary elements in Oman

The archaeological landscapes of northern Oman host thousands of funerary monuments of different periods and morphologies, forming one of the densest and least explored burial regions of the Arabian Peninsula. Within the framework of LAA&AAS (Laboratorio di Archeologia Analitica e Sistemi Artificiali Adattivi) and MASPAG (Missione Archeologica della Sapienza nella Penisola Arabica e nel Golfo), a multidisciplinary project supported by Sapienza University of Rome and the Italian Ministry of Foreign Affairs, we developed a reproducible geo-AI workflow to classify and analyse funerary structures based on remote-sensing and spatial-context information.

The first dataset, encompassing 185 tombs mapped in the Southwestern Cemetery near the village of Muslimat, in the region of Wadi al-Maʿawil (ca. 70 Km southwest of Muscat) was used to test a machine-learning pipeline designed to discriminate between morphological classes (“tombs” vs “non-tombs”, and within-type subclasses) from high-resolution satellite imagery and derived spatial metrics. Two Random Forest models were compared: a geometry-only baseline using shape descriptors (area, compactness, circularity, elongation), and an extended model incorporating spatial-context features such as kernel density, nearest-neighbour distances, Moran’s I local autocorrelation and cluster membership. The integration of these contextual descriptors increased overall accuracy from 59 % to 76 %, improving model reliability and reducing false positives in morphologically ambiguous contexts. The workflow includes systematic feature importance analysis and confusion-matrix evaluation to assess interpretability and class-imbalance effects.

Beyond the single-site test case, this approach aims to address a broader spatiotemporal challenge: learning and transferring morphological–contextual patterns across different archaeological regions. During 2025 field campaign (20 October – 20 December 2025), more than 500 new tombs were surveyed and georeferenced in the area of the Western Cemetery, expanding the available dataset and enabling large-scale testing of model scalability and transferability. This new phase will assess whether models trained in Wadi al-Maʿawil can generalize to nearby valleys with comparable geomorphological and cultural settings, supporting semi-automated mapping and predictive modelling of funerary features.

The presented pipeline, implemented in an open-source environment (Python, QGIS, and scikit-learn), is designed for reproducibility and transparent parameter tracking. All processing steps—from data preparation and feature extraction to model training and evaluation—are logged and versioned, facilitating cross-project reuse. The workflow thus bridges archaeological and geospatial domains, demonstrating how spatially aware machine learning can improve the detection, classification, and interpretation of complex cultural landscapes.

This contribution highlights the potential of AI and ML in managing spatiotemporal archaeological data and in advancing reproducible analytical frameworks. The methodological approach developed for the Omani funerary landscapes can be generalized to other MASPAG regions, supporting comparative analysis of desert landscapes and long-term dynamics of human–environment interaction across the Arabian Peninsula.