Update README.md

README.md

@@ -124,6 +124,33 @@ venus = opensr_test.load("venus")
   <a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/VENUS.gif" alt="header" width="80%"></a>
 </p>
 
+### **SPAIN CROPS (x4 scale factor)**
+
+The SPAIN CROPS dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focus in **crop fields and forests**.
+
+```python
+import opensr_test
+
+spain_crops = opensr_test.load("spain_crops")
+```
+
+<p align="center">
+  <a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/SPAIN_CROPS.gif" alt="header" width="80%"></a>
+</p>
+
+### **SPAIN URBAN (x4 scale factor)**
+
+The SPAIN URBAN dataset consists of 2.5m aerial imagery captured in the visible and near-infrared spectrum (RGBNIR) by the Spanish National Geographic Institute (IGN). The dataset includes all Sentinel-2 L1C and L2A bands. The dataset focus in **urban areas**.
+
+```python
+
+spain_urban = opensr_test.load("spain_urban")
+```
+
+<p align="center">
+  <a href="https://github.com/ESAOpenSR/opensr-test"><img src="images/SPAIN_URBAN.gif" alt="header" width="80%"></a>
+</p>
+
 ## **Deeper understanding**
 
 Explore the [API](https://esaopensr.github.io/opensr-test/docs/API/config_pydantic.html) section for more details about personalizing your benchmark experiments.
@@ -148,4 +175,4 @@ If you use `opensr-test` in your research, please cite our paper:
 
 ## **Acknowledgements**
 
-This work was made with the support of the European Space Agency (ESA) under the project “Explainable AI: application to trustworthy super-resolution (OpenSR)”. Cesar Aybar acknowledges support by the National Council of Science, Technology, and Technological Innovation (CONCYTEC, Peru) through the “PROYECTOS DE INVESTIGACIÓN BÁSICA – 2023-01” program with contract number PE501083135-2023-PROCIENCIA. Luis Gómez-Chova acknowledges support from the Spanish Ministry of Science and Innovation (project PID2019-109026RB-I00 funded by MCIN/AEI/10.13039/501100011033).
+This work was done with the support of the European Space Agency (ESA) under the project “Explainable AI: application to trustworthy super-resolution (OpenSR).” Cesar Aybar acknowledges support by the National Council of Science, Technology, and Technological Innovation (CONCYTEC, Peru) through the “PROYECTOS DE INVESTIGACIÓN BÁSICA – 2023-01” program with contract number PE501083135-2023-PROCIENCIA. Luis Gómez-Chova acknowledges support from the Spanish Ministry of Science and Innovation (project PID2019-109026RB-I00 funded by MCIN/AEI/10.13039/501100011033).