add river turbidity sample and updates text for sparse land cover
"* GIS Analysts and Data Scientists\n",
" * Adds [Classification of Raw Point Clouds using Deep Learning & generating 3D Building Models](https://developers.arcgis.com/python/sample-notebooks/classification-of-raw-point-clouds-using-deep-learning-&-generating-3d-building_models)\n",
" * Adds [Prediction of energy generation from Solar Photovoltaic Power Plants using weather variables](https://developers.arcgis.com/python/sample-notebooks/solar-energy-prediction-using-weather-variables)\n",
- " * Adds [Detection of electric utility features and vegetation encroachments from satellite images using deep learning](https://developers.arcgis.com/python/sample-notebooks/detection-of-electric-utility-features-and-vegetation-encroachments-from-satellite-images-using-deep-learning)"
+ " * Adds [Detection of electric utility features and vegetation encroachments from satellite images using deep learning](https://developers.arcgis.com/python/sample-notebooks/detection-of-electric-utility-features-and-vegetation-encroachments-from-satellite-images-using-deep-learning)\n",
+ " * Adds [River Turbidity Estimation using Sentinel-2 data](https://developers.arcgis.com/python/sample-notebooks/river-turbidity-estimation-using-sentinel2-data-)"
]
},
{
" * Removes hard-coded `lr_find()` value\n",
" * Fixes Table of Contents and hyperlinks in [Information extraction from Madison city crime incident reports using Deep Learning](https://developers.arcgis.com/python/sample-notebooks/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning/)\n",
" * Updates model training data in [Automate Building Footprint Extraction using Deep learning](https://developers.arcgis.com/python/sample-notebooks/automate-building-footprint-extraction-using-instance-segmentation/)\n",
- " * Updates to organization type and data paths in [Land Cover Classification using Satellite Imagery and Deep Learning](https://developers.arcgis.com/python/sample-notebooks/land-cover-classification-using-unet/)"
+ " * Updates to organization type and data paths in [Land Cover Classification using Satellite Imagery and Deep Learning](https://developers.arcgis.com/python/sample-notebooks/land-cover-classification-using-unet/)\n",
+ " * [Land cover classification using sparse training data](https://developers.arcgis.com/python/sample-notebooks/land-cover-classification-using-sparse-training-data/)\n",
+ " * Updates no data explanations\n",
+ " * Removes data mapping"
]
},
{
