Update README.md

README.md

@@ -370,6 +370,7 @@ Translate images e.g. from SAR to RGB.
 * [Image-Similarity-Search](https://github.com/spaceml-org/Image-Similarity-Search) -> an app that helps perform super fast image retrieval on PyTorch models for better embedding space interpretability
 * [Interactive-TSNE](https://github.com/spaceml-org/Interactive-TSNE) -> a tool that provides a way to visually view a PyTorch model's feature representation for better embedding space interpretability
 * [Fine tuning CLIP with Remote Sensing (Satellite) images and captions](https://huggingface.co/blog/fine-tune-clip-rsicd) -> fine tuning CLIP on the [RSICD](https://github.com/201528014227051/RSICD_optimal) image captioning dataset, to enable querying large catalogues in natural language. With [repo](https://github.com/arampacha/CLIP-rsicd)
+* [How Airbus Detects Anomalies in ISS Telemetry Data Using TFX](https://blog.tensorflow.org/2020/04/how-airbus-detects-anomalies-iss-telemetry-data-tfx.html) -> uses an autoencoder
 
 ## Few/one/zero/low shot learning
 This is a class of techniques which attempt to make predictions for classes with few, one or even zero examples provided during training. In zero shot learning (ZSL) the model is assisted by the provision of auxiliary information which typically consists of descriptions/semantic attributes/word embeddings for both the seen and unseen classes at train time ([ref](https://learnopencv.com/zero-shot-learning-an-introduction/)). These approaches are particularly relevant to remote sensing, where there may be many examples of common classes, but few or even zero examples for other classes of interest.
@@ -783,9 +784,10 @@ In this challenge, you will build a model to classify cloud organization pattern
 * [andraugust spacenet-utils](https://github.com/andraugust/spacenet-utils) -> Display geotiff image with building-polygon overlay & label buildings using kNN on the pixel spectra
 
 ## Tensorflow datasets
-* [resisc45](https://www.tensorflow.org/datasets/catalog/resisc45) - RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
-* [eurosat](https://www.tensorflow.org/datasets/catalog/eurosat) - EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
-* [bigearthnet](https://www.tensorflow.org/datasets/catalog/bigearthnet) - The BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels.
+* [resisc45](https://www.tensorflow.org/datasets/catalog/resisc45) -> RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
+* [eurosat](https://www.tensorflow.org/datasets/catalog/eurosat) -> EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
+* [BigEarthNet](https://www.tensorflow.org/datasets/catalog/bigearthnet) -> a large-scale Sentinel-2 land use classification dataset, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels. [Official website includes version of the dataset with Sentinel 1 & 2 chips](http://bigearth.net/)
+* [so2sat](https://www.tensorflow.org/datasets/catalog/so2sat) -> a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by Sentinel 1 & 2
 
 ## AWS datasets
 * [Earth on AWS](https://aws.amazon.com/earth/) is the AWS equivalent of Google Earth Engine