Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection
Yi Wang Chenying Liu Arti Tiwari Micha Silver Arnon Karnieli Xiao Xiang Zhu Conrad M Albrecht
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-archaeology-ai-challenge.