Update README.md

README.md

@@ -8,16 +8,17 @@ tags:
 - floods
 - earth-observation
 - deep-learnig
-- Phisat-2
-pretty_name: WorldFloods-Phisat-2
+- Φsat-2
+- PhiSat-2
+pretty_name: WorldFloods-PhiSat-2
 size_categories:
 - 10K<n<100K
 ---
 
-## WorldFloods Phisat-2 dataset
+## WorldFloods Φsat-2 dataset
 
-The **WorldFloods Phisat-2 dataset** is an adaptation of the [WorldFloodsv2 dataset](https://huggingface.co/datasets/isp-uv-es/WorldFloodsv2) created with the purpose of simulating a Phisat-2 like dataset for traning Foundation Models for this mission. 
-Phisat-2 like images were simulated using the code in the [OrbitalAI Challenge](https://github.com/AI4EO/orbitalAI). For storing the dataset, we followed the [PhiSatNet repo](https://github.com/sirbastiano/PhiSatNet) Data Specification Format.
-Since the WorldFloodsv2 dataset contains full flood scenes rather than chips, we created non-overlapping Train and Validation patches of size 512 from the simulated images, and only stored patches with less than 20% cloud cover, as we do on the fly to train the flood segmentation models described in the paper [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7).
-For Test samples, however, we kept the full scenes as this is a good practice when evaluating flood detection models. 
+The **WorldFloods Φsat-2 dataset** is an adaptation of the [WorldFloodsv2 dataset](https://huggingface.co/datasets/isp-uv-es/WorldFloodsv2) created with the purpose of simulating a Φsat-2-like dataset for traning Foundation Models for this mission. 
+Φsat-2 images were simulated using the code in the [OrbitalAI Challenge](https://github.com/AI4EO/orbitalAI). For storing the dataset, we followed the [PhiSatNet repo](https://github.com/sirbastiano/PhiSatNet) Data Specification Format.
+Since the WorldFloodsv2 dataset contains full flood scenes rather than small patches, we created non-overlapping Train and Validation patches of size 512x512 pixels from the simulated images, and only stored patches with less than 20% cloud cover, as we do on the fly to train the flood segmentation models described in the paper [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7).
+For Test samples, however, we kept the full scenes as this allows a better evaluation of flood detection models. 
 The total size of the dataset is 389 GB.