modified markdown text

app.py

@@ -38,14 +38,26 @@ if not hasattr(st, 'daily_model'):
     st.daily_model = st.daily_model.eval()
     st.annual_model = st.annual_model.eval()
 
-
 # Load Model
 # @title Load pretrained weights
 
 
-
-st.title('Lombardia Sentinel 2 daily Crop Mapping')
-st.markdown('Using a daily FPN and giving a zip that contains 30 tiff with 7 channels, correctly named you can reach prediction of crop mapping og the area.')
+st.title('In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series')
+st.markdown(""" Demo App for the model presented in the paper:
+```
+@article{gallo2022in_season,
+  title = {In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series},
+  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
+  volume = {195},
+  pages = {335-352},
+  year = {2023},
+  issn = {0924-2716},
+  doi = {https://doi.org/10.1016/j.isprsjprs.2022.12.005},
+  url = {https://www.sciencedirect.com/science/article/pii/S0924271622003203},
+  author = {Ignazio Gallo and Luigi Ranghetti and Nicola Landro and Riccardo {La Grassa} and Mirco Boschetti},
+}
+```
+""")
 
 file_uploaded = st.file_uploader(
     "Upload",
@@ -166,10 +178,8 @@ if st.paths is not None:
     target = np.squeeze(target)
     target = [classes_color_map[p] for p in target]
 
-
     fig, ax = plt.subplots()
     ax.imshow(target)
-    
 
     markdown_legend = ''
     for c, l in zip(color_labels, labels_map):
@@ -181,3 +191,13 @@ if st.paths is not None:
         st.pyplot(fig)
     with col2:
         st.markdown(markdown_legend, unsafe_allow_html=True)
+
+st.markdown(""" 
+    ## Lombardia Dataset
+    You can download other patches from the original dataset created and published on 
+    [Kaggle](https://www.kaggle.com/datasets/ignazio/sentinel2-crop-mapping) and used in our paper.
+    
+    ## How to build an input file for the Demo
+    Using a daily FPN and giving a zip that contains 30 tiff with 7 channels, correctly named you can reach prediction of 
+    crop mapping og the area...
+    """)