Update README.md

README.md

@@ -1,21 +1,22 @@
----
-license: mit
-language:
-- en
-metrics:
-- f1
-- accuracy
-tags:
-- unet
-- fusion
-- SAR
-- multispectral
-- LULC
-- environment
----
-
-This is the model presented as the best-performing model to perform a land use land cover (LULC) classification based on multispectral and SAR data in the following article: Solórzano et al. 2021. Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery. Remote Sensing, 13, 3600. [https://doi.org/10.3390/rs13183600](https://doi.org/10.3390/rs13183600). The additional Github repository containing the code to obtain this model can be found in: [https://github.com/JonathanVSV/U-netR](https://github.com/JonathanVSV/U-netR).
-
-The U-net architecture:
-
+---
+license: mit
+language:
+- en
+metrics:
+- f1
+- accuracy
+tags:
+- unet
+- fusion
+- SAR
+- multispectral
+- LULC
+- environment
+pipeline_tag: image-classification
+---
+
+This is the model presented as the best-performing model to perform a land use land cover (LULC) classification based on multispectral and SAR data in the following article: Solórzano et al. 2021. Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery. Remote Sensing, 13, 3600. [https://doi.org/10.3390/rs13183600](https://doi.org/10.3390/rs13183600). The additional Github repository containing the code to obtain this model can be found in: [https://github.com/JonathanVSV/U-netR](https://github.com/JonathanVSV/U-netR).
+
+The U-net architecture:
+
 ![unet2d.jpg](imgs/unet2d.jpg)