@inproceedings{047fe4a4912242dfbcfb759dae11d4cb,
title = "Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection",
abstract = "Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys are both costly and limited in scale. With the increasing accessibility of aerial and satellite data, machine learning techniques bear large potential for the automatic detection and recognition of archaeological landscapes. In this paper, we propose a deep semantic model fusion method for ancient agricultural terrace detection. The input data includes aerial images and LiDAR generated terrain features in the Negev desert. Two deep semantic segmentation models, namely DeepLabv3+ and UNet, with EfficientNet backbone, are trained and fused to provide segmentation maps of ancient terraces and walls. The proposed method won the first prize in the International AI Archaeology Challenge. Codes are available at https://github.com/wangyi111/international-archaeologyai-challenge.",
keywords = "archeology, deep learning, semantic segmentation",
author = "Yi Wang and Chenying Liu and Arti Tiwari and Micha Silver and Arnon Karnieli and Zhu, {Xiao Xiang} and Albrecht, {Conrad M.}",
note = "Funding Information: This work is supported by the Helmholtz Association through the Framework of Helmholtz AI (grant number: ZTI- PF-5-01) - Local Unit {"}Munich Unit @Aeronautics, Space and Transport (MASTr){"}.The work of X. Zhu is additionally supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab {"}AI4EO - Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond{"} (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the {"}national center of excellence ML4Earth{"} (grant number: 50EE2201C). We thank both IDSI and Helmholtz Information and Data Science Academy (HIDA) for organizing the challenge. Funding Information: ACKNOWLEDGMENT This work is supported by the Helmholtz Association through the Framework of Helmholtz AI (grant number: ZT-I-PF-5-01) - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)”.The work of X. Zhu is additionally supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C). We thank both IDSI and Helmholtz Information and Data Science Academy (HIDA) for organizing the challenge. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/BigData55660.2022.10020329",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "4888--4892",
editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",
booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",
address = "United States",
}