@@ -44,6 +44,6 @@ Model prediction using selected model weights. The evaluation metrics include ac
In `./u-net-model/val-imageval_sheet.xlsx`, we keep the manual annotation result we discuss in Section 3.2 and Appendix B.
### 2.6. Two-stage model
we have tested two-stage models with Sentinel 1 + 2 images. In the two-stage models, we first predict only the footprints of the buildings (the first stage) and then feed the predicted footprints into a height prediction model (the second stage). We have validated three architectures in the second stage of the two-stage models when predicting heights: (1) stacking the building footprint prediction results and the Sentinel images together as height prediction model inputs; (2) clipping Sentinel images with building footprint prediction results before putting them into the height prediction model; (3) training in parallel the building footprint prediction and height prediction in the encoder part, and use skip connection to concatenate them for the bottleneck and decoder parts of the U-Net. model
we have tested two-stage models with Sentinel 1 + 2 images. In the two-stage models, we first predict only the footprints of the buildings (the first stage) and then feed the predicted footprints into a height prediction model (the second stage). We have validated three architectures in the second stage of the two-stage models when predicting heights: (1) stacking the building footprint prediction results and the Sentinel images together as height prediction model inputs; (2) clipping Sentinel images with building footprint prediction results before putting them into the height prediction model; (3) training in parallel the building footprint prediction and height prediction in the encoder part, and use skip connection to concatenate them for the bottleneck and decoder parts of the U-Net model.
