The 23rd IPPA Congress
The 23rd IPPA Congress
S47
Lidar Survey of Mount Penanggungan, East Java: Advancing Archaeological Remote Sensing on a Global Scale
Vladyslav Sydorov1*, Marlon Nicolay Ramon Ririmasse2, Alqiz Lukman2, Taufan Daniarta Sukarno2, Rama Putra Siswantara2, Dewangga Eka Mahardian2, Frandus2, Shinatria Adhityatama2, Helene Njoto1, Jean-Baptiste Chevance3, and Christophe Pottier1
1École française d’Extrême-Orient, France; 2National Research and Innovation Agency (BRIN), Indonesia; 3Archaeology and Development Foundation (ADF), Cambodia; *vladyslav.sydorov@efeo.net
In July 2024, a 229 km² airborne lidar acquisition using a Leica DragonEye sensor was conducted over Mount Penanggungan in East Java, a sacred mountain and key site of the Hindu-Buddhist period from the 13th to the 15th centuries CE. Over 200 sanctuaries have been recorded on its northern slopes, but large areas remain poorly documented due to steep terrain and dense forest cover. The survey was conducted through a collaboration between the EFEO, BRIN, and BPK XI. Preliminary processing identified 958 points of archaeological interest, many in previously unexplored areas, and field verification campaigns in 2024 and 2025 by joint BRIN-EFEO teams confirmed numerous undocumented features on the southern and western slopes, including a Majapahit-period site at approximately 1,165 m elevation where a carved stone probably bearing a Saka date of 1300 (1378 CE) was found in situ. The acquisition forms part of a wider effort: over the past decade, coordinated campaigns conducted through the EFEO with Cambodian, Laotian, Thai, and now Indonesian authorities have produced over 7,500 km² of high-density archaeological lidar across Southeast Asia's tropical landscapes. This collaboration strengthens heritage survey capacity in each participating country through the exchange of interpretive methods and field expertise, while also contributing to a growing large-scale dataset. We show how this allows for development of effective machine learning models for automated feature detection, which prove more robust when trained across the diverse settlement forms and environmental conditions of multiple Southeast Asian landscapes.