diff --git a/docs/tutorials/custom_segmentation_trainer.ipynb b/docs/tutorials/custom_segmentation_trainer.ipynb
index fd51a4e45f7..fd89571c3e7 100644
--- a/docs/tutorials/custom_segmentation_trainer.ipynb
+++ b/docs/tutorials/custom_segmentation_trainer.ipynb
@@ -18,7 +18,7 @@
     "\n",
     "In this tutorial, we demonstrate how to extend a TorchGeo [\"trainer class\"](https://torchgeo.readthedocs.io/en/latest/api/trainers.html). In TorchGeo there exist several trainer classes that are pre-made PyTorch Lightning Modules designed to allow for the easy training of models on semantic segmentation, classification, change detection, etc. tasks using TorchGeo's [prebuild DataModules](https://torchgeo.readthedocs.io/en/latest/api/datamodules.html). While the trainers aim to provide sensible defaults and customization options for common tasks, they will not be able to cover all situations (e.g. researchers will likely want to implement and use their own architectures, loss functions, optimizers, etc. in the training routine). If you run into such a situation, then you can simply extend the trainer class you are interested in, and write custom logic to override the default functionality.\n",
     "\n",
-    "This tutorial shows how to do exactly this to customize a learning rate schedule, logging, and model checkpointing for a semantic segmentation task using the [LandCoverAI](https://landcover.ai.linuxpolska.com/) dataset.\n",
+    "This tutorial shows how to do exactly this to customize a learning rate schedule, logging, and model checkpointing for a semantic segmentation task using the [LandCover.ai](https://landcover.ai.linuxpolska.com/) dataset.\n",
     "\n",
     "It's recommended to run this notebook on Google Colab if you don't have your own GPU. Click the \"Open in Colab\" button above to get started."
    ]