Codes for the HIDA/IDSI international archaeology AI challenge where we won first place among the participants. The task is multiclass semantic segmentation of ancient agricultural terrace/wall in the Negev desert. We use UNet/DeepLabV3+ with EfficientNet backbone and get an overal IOU (foreground only) of 0.31.

Orthophoto	Prediction	GroundTruth

• The training data are 500 512*512 image patches, each patch containing 9 feature images (Orthophoto, Aspect, DTM, Flow_Accum, Flow_Direction, Prof_curv, Slope, Tang_curv, Topo_Wetness).
• The testing data (for final score) are 200 unseen image patches.

• architecture: UNet/DeepLabV3+, we merge the output probability map of both models to calculate the final prediction
• backbone: efficientnet-b5 (pretrained on ImageNet)

Codes for the models are based on segmentation-models-pytorch.

Cross entropy loss + DICE loss, AdamW optimizer, cosine loss decay

• train: python main_train.py or bash train.sh
• inference: python main_inference.py or bash inference.sh (python main_inference_2model.py to do inference with merged models)
• evaluate: python main_test.py

@inproceedings{wang2022deep,
  title={Deep Semantic Model Fusion for Ancient Agricultural Terrace Detection},
  author={Wang, Yi and Liu, Chenying and Tiwari, Arti and Silver, Micha and Karnieli, Arnon and Zhu, Xiao Xiang and Albrecht, Conrad M},
  booktitle={2022 IEEE International Conference on Big Data (Big Data)},
  pages={4888--4892},
  year={2022},
  organization={IEEE}
}