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.

翻译：在沙漠地区发现古代农业梯田对于监测地球表面长期气候变化具有重要意义。然而，传统地面调查成本高昂且规模有限。随着航拍和卫星数据获取的日益便捷，机器学习技术在考古景观自动检测与识别方面展现出巨大潜力。本文提出一种用于古代农业梯田检测的深度语义模型融合方法。输入数据包括内盖夫沙漠地区的航拍图像与LiDAR生成的地形特征。本研究训练并融合了两个深度语义分割模型（基于EfficientNet骨干网络的DeepLabv3+和UNet），以生成古代梯田与墙体的分割图。所提方法在国际人工智能考古挑战赛中荣获一等奖。代码已开源：https://github.com/wangyi111/international-archaeology-ai-challenge。