Pred_height_from_tiff_DL_patch_MTL: using deep-learning-based (DL) models trained by Multi-Task-Learning (MTL). Pred_height_from_tiff_DL_patch: using deep-learning-based (DL) models trained by Single-Task-Learning (STL). Building Height and Footprint predictionĪfter preparing above necessary images, building height and footprint information can be predicted by: Year = year, dst_dir = dst_dir, file_name = file_name, dst = dst)Īlso, SHAFT gives functions such as sentinel1_download, sentinel2_download and srtm_download to download images in a batch way by a. GEE_ops import sentinel2_download_by_extent # -specify the spatial extent and year for Sentinel-2's images lon_min = - 87.740 lat_min = 41.733 lon_max = - 87.545 lat_max = 41.996 year = 2018 # -define the output settings dst = "Drive" dst_dir = "Sentinel-2_export" file_name = "Chicago_2018_sentinel_2.tif" # -start data downloading sentinel2_download_by_extent( lon_min = lon_min, lat_min = lat_min, lon_max = lon_max, lat_max = lat_max,
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