Removed temporary pydantic dependency

simonreise · Jul 17, 2023 · 7d22abc · 7d22abc
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Showing 3 changed files with 4 additions and 6 deletions.
diff --git a/README.md b/README.md
@@ -9,7 +9,7 @@
 
 RSP is a tool for geospatial raster data processing.
 
-Most of remote sensing data like Sentinel of Landsat imagery needs to be preprocessed before using. RSP can preprocess Sentinel-2 and Landsat imagery, create raster mosaics, calculate vegetation indices and perform image segmentation tasks.
+RSP can preprocess Sentinel-2 and Landsat imagery, create raster mosaics, calculate vegetation indices and perform image segmentation tasks.
 
 Read the documentation for more details: https://remote-sensing-processor.readthedocs.io
 
@@ -60,7 +60,7 @@ x_train = x_i[0]
 # training UperNet that predicts landcover class based on sentinel imagery
 model = rsp.segmentation.train(x_train, y_train, x_val, y_val, model = 'UperNet', backbone = 'ConvNeXTV2', model_file = '/home/rsp_test/model/upernet.ckpt', epochs = 10, classification = classification, num_classes = num_classes, x_nodata = x_nodata, y_nodata = y_nodata)
 
-# testing_model
+# testing model
 rsp.segmentation.test(x_test, y_test, model = model)
 
 # mapping landcover based on predictions of our UperNet

diff --git a/conda-recipe/meta.yaml b/conda-recipe/meta.yaml
@@ -18,7 +18,6 @@ requirements:
     - lightning
     - numpy >=1.17
     - pip
-    - pydantic <2.0 # temp fix for lightning broken dependency
     - pyproj
     - python {{ python }}
     - pytorch >=1.10
@@ -34,7 +33,6 @@ requirements:
     - h5py
     - lightning
     - numpy >=1.17
-    - pydantic <2.0 # temp fix for lightning broken dependency
     - pyproj
     - python {{ python }}
     - pytorch >=1.10

diff --git a/docs/index.md b/docs/index.md
@@ -2,7 +2,7 @@
 
 RSP is a tool for geospatial raster data processing.
 
-Most of remote sensing data like Sentinel of Landsat imagery needs to be preprocessed before using. RSP can preprocess Sentinel-2 and Landsat imagery, create raster mosaics, calculate vegetation indices, cut rasters into tiles.
+RSP can preprocess Sentinel-2 and Landsat imagery, create raster mosaics, calculate vegetation indices and perform image segmentation tasks.
 
 Here is an example of some features that RSP provides. Sentinel-2 images are being preprocessed and merged into a mosaic, NDVI of that Sentinel-2 mosaic is calculated. Landcover images are merged into mosaic at the same resolution and projection as Sentinel-2 data. Then Sentinel-2 and landcover data is divided into tiles and U-Net model that predicts landcover based on Sentinel-2 data is trained. This model is used to create landcover map. 
 ```
@@ -49,7 +49,7 @@ x_train = x_i[0]
 # training UperNet that predicts landcover class based on sentinel imagery
 model = rsp.segmentation.train(x_train, y_train, x_val, y_val, model = 'UperNet', backbone = 'ConvNeXTV2', model_file = '/home/rsp_test/model/upernet.ckpt', epochs = 10, classification = classification, num_classes = num_classes, x_nodata = x_nodata, y_nodata = y_nodata)
 
-# testing_model
+# testing model
 rsp.segmentation.test(x_test, y_test, model = model)
 
 # mapping landcover based on predictions of our UperNet