Update README.md

README.md

@@ -480,18 +480,18 @@ This section contains a short list of datasets relevant to deep learning, partic
 * SpaceNet - WorldView-3 [article here](https://spark-in.me/post/spacenet-three-challenge), and [semantic segmentation using Raster Vision](https://docs.rastervision.io/en/0.8/quickstart.html)
 
 ## Kaggle
-Kaggle hosts over > 100 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).
+Kaggle hosts over > 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).
 The [kaggle blog](http://blog.kaggle.com) is an interesting read.
 
-### Kaggle - Amazon from space - classification challenge
+### Kaggle - Amazon from space (classification challenge)
 * https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data
 * 3-5 meter resolution GeoTIFF images from planet Dove satellite constellation
 * 12 classes including - **cloudy, primary + waterway** etc
 * [1st place winner interview - used 11 custom CNN](http://blog.kaggle.com/2017/10/17/planet-understanding-the-amazon-from-space-1st-place-winners-interview/)
 * [FastAI Multi-label image classification](https://towardsdatascience.com/fastai-multi-label-image-classification-8034be646e95)
 * [Multi-Label Classification of Satellite Photos of the Amazon Rainforest](https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/)
 
-### Kaggle - DSTL - segmentation challenge
+### Kaggle - DSTL (segmentation challenge)
 * https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
 * Rating - medium, many good examples (see the Discussion as well as kernels), but as this competition was run a couple of years ago many examples use python 2
 * WorldView 3 - 45 satellite images covering 1km x 1km in both 3 (i.e. RGB) and 16-band (400nm - SWIR) images
@@ -505,13 +505,13 @@ The [kaggle blog](http://blog.kaggle.com) is an interesting read.
 * Rating - medium, most solutions using deep-learning, many kernels, [good example kernel](https://www.kaggle.com/kmader/baseline-u-net-model-part-1)
 * I believe there was a problem with this dataset, which led to many complaints that the competition was ruined
 
-### Kaggle - Draper - place images in order of time
+### Kaggle - Draper (place images in order of time)
 * https://www.kaggle.com/c/draper-satellite-image-chronology/data
 * Rating - hard. Not many useful kernels.
 * Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned.
 * Kaggle interviews for entrants who [used XGBOOST](http://blog.kaggle.com/2016/09/15/draper-satellite-image-chronology-machine-learning-solution-vicens-gaitan/) and a [hybrid human/ML approach](http://blog.kaggle.com/2016/09/08/draper-satellite-image-chronology-damien-soukhavong/)
 
-### Kaggle - Deepsat - classification challenge
+### Kaggle - Deepsat (classification challenge)
 Not satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in `.mat` Matlab format. JPEG?
 * [Imagery source](https://csc.lsu.edu/~saikat/deepsat/)
 * [Sat4](https://www.kaggle.com/crawford/deepsat-sat4) 500,000 image patches covering four broad land cover classes - **barren land, trees, grassland and a class that consists of all land cover classes other than the above three**