Update land_cover_classification_for_dc_using_unet.ipynb
"cell_type": "markdown",
"metadata": {},
"source": [
- "With the feature class and raster layer, we are now ready to export training data using the export_training_data() method in arcgis.learn module. In addtion to feature class, raster layer, and output folder, we also need to speficy a few other parameters such as tile_size (size of the image chips), strid_size (distance to move in the X when creating the next image chip), chip_format (TIFF, PNG, or JPEG), metadata format (how we are going to store those bounding boxes). More detail can be found [here](https://pro.arcgis.com/en/pro-app/tool-reference/image-analyst/export-training-data-for-deep-learning.htm). \n",
+ "With the feature class and raster layer, we are now ready to export training data using the export_training_data() method in arcgis.learn module. In addtion to feature class, raster layer, and output folder, we also need to speficy a few other parameters such as tile_size (size of the image chips), strid_size (distance to move each time when creating the next image chip), chip_format (TIFF, PNG, or JPEG), metadata format (how we are going to store those training labels). More detail can be found [here](https://pro.arcgis.com/en/pro-app/tool-reference/image-analyst/export-training-data-for-deep-learning.htm). \n",
"\n",
"Depending on the size of your data, tile and stride size, and computing resources, this opertation can take 15mins~2hrs in our experiment. Also, do not re-run it if you already run it once unless you would like to update the setting."
]
"metadata": {},
"source": [
"## Conclusion\n",
- "In thise notebook, we have covered a lot of ground. In part 1, we discussed how to export training data for deep learning using ArcGIS python API and what the output looks like. In part 2, we demonstrated how to prepare the input data, train a object detection model, visualize the results, as well as apply the model to an unseen image using the deep learning module in ArcGIS API for Python. Then we covered how to install and publish this model and make it production-ready in part 3.\n",
+ "In thise notebook, we have covered a lot of ground. In part 1, we discussed how to export training data for deep learning using ArcGIS python API and what the output looks like. In part 2, we demonstrated how to prepare the input data, train a pixel-based classification model, visualize the results, as well as apply the model to an unseen image. Then we covered how to install and publish this model and make it production-ready in part 3.\n",
"\n",
"## References\n",
"[1] Olaf Ronneberger, Philipp Fischer, Thomas Brox: U-Net: Convolutional Networks for Biomedical Image Segmentation, 2015; <a href='https://arxiv.org/abs/1505.04597'>arXiv:1505.04597</a>."
