Browsing by Author "Iban Berganzo-Besga"

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Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan
(Nature Research, 2023) Iban Berganzo-Besga; Hector A. Orengo; Felipe Lumbreras; Aftab Alam; Rosie Campbell; Petrus J. Gerrits; Jonas Gregorio de Souza; Afifa Khan; María Suárez-Moreno; Jack Tomaney; Rebecca C. Roberts; Cameron A. Petrie
This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map. © 2023, The Author(s).
Hidden in Plain Sight: The Unrecognized Contribution of the Survey of India in the Documentation of Ancient Settlements in Pakistan and India
(Taylor and Francis Ltd., 2025) Cameron A. Petrie; Junaid Abdul Jabbar; Abhayan; G. S; Aftab Alam; Iban Berganzo-Besga; Rosie Campbell; Francesc C. Conesa; Moazzam Durrani; Arnau Garcia-Molsosa; Petrus Johannes Gerrits; Adam S. Green; Lily M. Green; Jonas Gregorio De Souza; Muhammad Hameed; Afifa Khan; Marco Madella; M. Waqar Mushtaq; Hector A. Orengo; V. N. Prabhakar; S. V. Rajesh; David I. Redhouse; Rebecca C. Roberts; Mou Sarmah; Abdul Samad; Ravindra Nath Singh; Vikas Kumar Singh; Maria Suarez Moreno; Jack A. Tomaney; Azadeh Vafadari; Vaneshree Vidyarthi
The earliest documentation of hundreds of ancient settlements in South Asia, including some of the most famous and significant sites, lies in largely unacknowledged subaltern hands. Operating during the British colonial period, teams employed by the Survey of India systematically mapped the colonial dominions and produced high-quality maps that depicted topography and land use across vast areas. Systematic analysis of these map sheets combined with ground-truthing is demonstrating that these teams documented thousands of mound features, and a significant number of these are (or sadly in many cases were) archaeological sites. Members of the original survey teams were for the most part not in a position to contribute their thoughts to the historical narrative, but the legacy of what they documented has long been hidden in plain sight. The collaborative Mapping Archaeological Heritage in South Asia (MAHSA) project is systematically documenting this archaeological heritage. Its work is demonstrating that the teams carrying out the Survey of India topographic surveys incidentally conducted the first systematic survey of archaeological sites in South Asia. This was potentially the world’s most extensive (albeit incidental) archaeological survey. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.