A deep learning approach for automatic identification of ancient agricultural water harvesting systems

Abstract

Despite the harsh climatic conditions in the Central Negev Desert, Israel, thousands of dry stonewalls were built across ephemeral streams between the fourth and seventh centuries CE to sustain productive agricultural activity. Since 640 CE, many of these ancient terraces have remained untouched but buried by sediments, covered by natural vegetation, and partially destroyed. The main goal of the current research is to develop a procedure for the automatic recognition of ancient water harvesting systems by incorporating two remote sensing datasets (a high-resolution color orthophoto and LiDAR-derived topographic variables) and two advanced processing methods (an object-based image analysis (OBIA) and a deep convolutional neural networks (DCNN) model). A confusion matrix of object-based classification revealed an overall accuracy of 86% and a Kappa coefficient of 0.79. The DCNN model achieved a Mean Intersection over Union (MIoU) value for testing datasets of 53. The individual IoU values of terraces and sidewalls were 33.2 and 30.1, respectively. The current study demonstrates how incorporating OBIA, aerial photographs, and LiDAR in the context of DCNN improves the identification and mapping of archaeological structures.

Keywords

• Machine learning
• U-Net
• Semantic segmentation
• Terraces
• DCNN
• LiDAR