docs: use reference style links (#342)

README.md

@@ -9,19 +9,29 @@
 
 <div align="center">
 
-[![CI](https://img.shields.io/github/actions/workflow/status/geospaitial-lab/aviary/ci.yaml?branch=main&color=black&label=CI&logo=GitHub)](https://www.github.com/geospaitial-lab/aviary/actions/workflows/ci.yaml)
-[![Coverage](https://img.shields.io/codecov/c/github/geospaitial-lab/aviary/main?color=black&label=Coverage&logo=codecov&logoColor=white)](https://app.codecov.io/gh/geospaitial-lab/aviary)
-[![Docs](https://img.shields.io/github/actions/workflow/status/geospaitial-lab/aviary/docs.yaml?branch=main&color=black&label=Docs&logo=materialformkdocs&logoColor=white)](https://geospaitial-lab.github.io/aviary)
+[![CI][CI Badge]][CI]
+[![Coverage][Coverage Badge]][Coverage]
+[![Docs][Docs Badge]][Docs]
 
 </div>
 
 <div align="center">
 
-[![PyPI version](https://img.shields.io/pypi/v/geospaitial-lab-aviary?color=black&label=PyPI)](https://www.pypi.org/project/geospaitial-lab-aviary)
-[![Python version](https://img.shields.io/pypi/pyversions/geospaitial-lab-aviary?color=black&label=Python)](https://www.pypi.org/project/geospaitial-lab-aviary)
+[![PyPI version][PyPI version Badge]][PyPI]
+[![Python version][Python version Badge]][PyPI]
 
 </div>
 
+  [CI Badge]: https://img.shields.io/github/actions/workflow/status/geospaitial-lab/aviary/ci.yaml?branch=main&color=black&label=CI&logo=GitHub
+  [CI]: https://www.github.com/geospaitial-lab/aviary/actions/workflows/ci.yaml
+  [Coverage Badge]: https://img.shields.io/codecov/c/github/geospaitial-lab/aviary/main?color=black&label=Coverage&logo=codecov&logoColor=white
+  [Coverage]: https://app.codecov.io/gh/geospaitial-lab/aviary
+  [Docs Badge]: https://img.shields.io/github/actions/workflow/status/geospaitial-lab/aviary/docs.yaml?branch=main&color=black&label=Docs&logo=materialformkdocs&logoColor=white
+  [Docs]: https://geospaitial-lab.github.io/aviary
+  [PyPI version Badge]: https://img.shields.io/pypi/v/geospaitial-lab-aviary?color=black&label=PyPI
+  [Python version Badge]: https://img.shields.io/pypi/pyversions/geospaitial-lab-aviary?color=black&label=Python
+  [PyPI]: https://www.pypi.org/project/geospaitial-lab-aviary
+
 aviary provides composable components for building inference and postprocessing pipelines
 for remote sensing data.
 This enables you to easily run models on large datasets, export the predictions in a
@@ -62,8 +72,9 @@ pip install geospaitial-lab-aviary
 
 Note that aviary requires Python 3.10 or later.
 
-Have a look at the [installation guide](https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_pip)
-for further information.
+Have a look at the [installation guide][installation guide pip] for further information.
+
+  [installation guide pip]: https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_pip
 
 ### Installation with uv
 
@@ -73,29 +84,37 @@ uv pip install geospaitial-lab-aviary
 
 Note that aviary requires Python 3.10 or later.
 
-Have a look at the [installation guide](https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_uv)
-for further information.
+Have a look at the [installation guide][installation guide uv] for further information.
+
+  [installation guide uv]: https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_uv
 
 ### Installation with Docker
 
 ```
 docker pull ghcr.io/geospaitial-lab/aviary
 ```
 
-Have a look at the [installation guide](https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_docker)
-for further information.
+Have a look at the [installation guide][installation guide docker] for further information.
+
+  [installation guide docker]: https://geospaitial-lab.github.io/aviary/how_to_guides/installation/how_to_install_aviary_with_docker
 
 ## Next steps
 
-Have a look at the [how-to guides](https://geospaitial-lab.github.io/aviary/how_to_guides)
-to get started.
+Have a look at the [how-to guides] to get started.
+
+  [how-to guides]: https://geospaitial-lab.github.io/aviary/how_to_guides
 
 ## Documentation
 
-The full documentation is available at [geospaitial-lab.github.io/aviary](https://geospaitial-lab.github.io/aviary).
+The full documentation is available at [geospaitial-lab.github.io/aviary].
+
+  [geospaitial-lab.github.io/aviary]: https://geospaitial-lab.github.io/aviary
 
 ## About
 
-aviary is developed by the [geospaitial lab](https://www.github.com/geospaitial-lab)
-at the [Westfälische Hochschule - Westphalian University of Applied Sciences](https://www.w-hs.de)
+aviary is developed by the [geospaitial lab]
+at the [Westfälische Hochschule - Westphalian University of Applied Sciences]
 in Gelsenkirchen, Germany.
+
+  [geospaitial lab]: https://www.github.com/geospaitial-lab
+  [Westfälische Hochschule - Westphalian University of Applied Sciences]: https://www.w-hs.de

docs/README.md

@@ -1,10 +1,14 @@
 ## Documentation
 
-The documentation is built with [MkDocs](https://www.mkdocs.org)
-and the [Material for MkDocs](https://squidfunk.github.io/mkdocs-material) theme.<br />
-The API reference is generated with [mkdocstrings](https://mkdocstrings.github.io).
+The documentation is built with [MkDocs] and the [Material for MkDocs] theme.<br />
+The API reference is generated with [mkdocstrings].
 
-The docs are built and deployed automatically on new releases with the [docs workflow](../.github/workflows/docs.yaml).
+The docs are built and deployed automatically on new releases with the [docs workflow].
+
+  [MkDocs]: https://www.mkdocs.org
+  [Material for MkDocs]: https://squidfunk.github.io/mkdocs-material
+  [mkdocstrings]: https://mkdocstrings.github.io
+  [docs workflow]: ../.github/workflows/docs.yaml
 
 ## Build the docs locally
 

docs/api_reference/index.md

@@ -6,19 +6,25 @@ aviary's Python API is structured into the following namespaces:
 
 <div class="grid cards" markdown>
 
--   [**`aviary`**](bounding_box.md)<br />
+-   [**`aviary`**]<br />
     Core types
 
--   [**`aviary.data`**](data/data_fetcher/data_fetcher.md)<br />
+-   [**`aviary.data`**]<br />
     Data fetching and processing
 
--   [**`aviary.geodata`**](geodata/coordinates_filter/coordinates_filter.md)<br />
+-   [**`aviary.geodata`**]<br />
     Geodata processing
 
--   [**`aviary.inference`**](inference/exporter/exporter.md)<br />
+-   [**`aviary.inference`**]<br />
     Models and exporters
 
--   [**`aviary.pipeline`**](pipeline/postprocessing_pipeline.md)<br />
+-   [**`aviary.pipeline`**]<br />
     Pre-built pipelines
 
 </div>
+
+  [**`aviary`**]: bounding_box.md
+  [**`aviary.data`**]: data/data_fetcher/data_fetcher.md
+  [**`aviary.geodata`**]: geodata/coordinates_filter/coordinates_filter.md
+  [**`aviary.inference`**]: inference/exporter/exporter.md
+  [**`aviary.pipeline`**]: pipeline/postprocessing_pipeline.md

docs/aviary/index.md

@@ -34,22 +34,22 @@ so canopies don't cover buildings or impervious surfaces.
 
 ### Create the model with the Python API
 
-If you want to use the [Python API](../api_reference/index.md),
-you can create an [`ONNXSegmentationModel`](../api_reference/inference/model/onnx_segmentation_model.md)
-as follows:
+If you want to use the [Python API], you can create an [`ONNXSegmentationModel`] as follows:
 
 ``` python
 from aviary.inference import ONNXSegmentationModel
 
 model = ONNXSegmentationModel.from_aviary('sparrow')
 ```
 
+  [Python API]: ../api_reference/index.md
+  [`ONNXSegmentationModel`]: ../api_reference/inference/model/onnx_segmentation_model.md
+
 ---
 
 ### Create the model with the CLI
 
-If you want to use the [command-line interface (CLI)](../cli_reference/segmentation_pipeline.md),
-you can use the following configuration:
+If you want to use the [command-line interface (CLI)], you can use the following configuration:
 
 ``` yaml title="config.yaml"
 model:
@@ -58,9 +58,12 @@ model:
     name: sparrow
 ```
 
+  [command-line interface (CLI)]: ../cli_reference/segmentation_pipeline.md
+
 ---
 
 ### Next steps
 
-Have a look at the [how-to guide](../how_to_guides/cli/how_to_run_the_segmentation_pipeline.md)
-on how to run the segmentation pipeline with the CLI using sparrow.
+Have a look at the [how-to guide] on how to run the segmentation pipeline with the CLI using sparrow.
+
+  [how-to guide]: ../how_to_guides/cli/how_to_run_the_segmentation_pipeline.md

docs/cli_reference/index.md

@@ -23,10 +23,13 @@ You can choose from the following pipelines:
 
 <div class="grid cards" markdown>
 
--   [**Segmentation pipeline**](segmentation_pipeline.md)<br />
+-   [**Segmentation pipeline**]<br />
     Run a segmentation model on your data
 
--   [**Postprocessing pipeline**](postprocessing_pipeline.md)<br />
+-   [**Postprocessing pipeline**]<br />
     Postprocess geospatial data
 
 </div>
+
+  [**Segmentation pipeline**]: segmentation_pipeline.md
+  [**Postprocessing pipeline**]: postprocessing_pipeline.md

docs/cli_reference/postprocessing_pipeline.md

@@ -8,7 +8,7 @@ The postprocessing pipeline is a pre-built pipeline designed to postprocess geos
 It consists of the following components:
 
 - A path to the geodataframe
-- A [`GeodataPostprocessor`](../api_reference/geodata/geodata_postprocessor/geodata_postprocessor.md) to postprocess the geodata
+- A [`GeodataPostprocessor`] to postprocess the geodata
 - A path to export the geodataframe
 
 These components are set up in a configuration file (.yaml file) that is passed to the pipeline.<br />
@@ -26,8 +26,7 @@ path:
 
 The `name` field must be the name of the class that you want to use for the component.<br />
 The `config` field must contain its corresponding configuration.<br />
-Note that each class has its own configuration, which can be found in the
-[API reference](../api_reference/pipeline/postprocessing_pipeline.md#aviary.pipeline.PostprocessingPipelineConfig).
+Note that each class has its own configuration, which can be found in the [API reference].
 
 To run the postprocessing pipeline, run the following command:
 
@@ -59,9 +58,13 @@ To run the postprocessing pipeline, run the following command:
     -v path/to/file:/aviary/file
     ```
 
+  [`GeodataPostprocessor`]: ../api_reference/geodata/geodata_postprocessor/geodata_postprocessor.md
+  [API reference]: ../api_reference/pipeline/postprocessing_pipeline.md#aviary.pipeline.PostprocessingPipelineConfig
+
 ---
 
 ## Next steps
 
-Have a look at the [how-to guide](../how_to_guides/cli/how_to_run_the_postprocessing_pipeline.md)
-on how to run the postprocessing pipeline with an example configuration file.
+Have a look at the [how-to guide] on how to run the postprocessing pipeline with an example configuration file.
+
+  [how-to guide]: ../how_to_guides/cli/how_to_run_the_postprocessing_pipeline.md

docs/cli_reference/segmentation_pipeline.md

@@ -7,11 +7,11 @@
 The segmentation pipeline is a pre-built pipeline designed to run a segmentation model on your data.
 It consists of the following components:
 
-- A [`DataFetcher`](../api_reference/data/data_fetcher/data_fetcher.md) to fetch data from a source
-- A [`ProcessArea`](../api_reference/process_area.md) to specify the area of interest
-- A [`DataPreprocessor`](../api_reference/data/data_preprocessor/data_preprocessor.md) to preprocess the fetched data
-- A [`SegmentationModel`](../api_reference/inference/model/segmentation_model.md) to do the inference on the preprocessed data
-- An [`SegmentationExporter`](../api_reference/inference/exporter/segmentation_exporter.md) to export the predictions dynamically as geospatial data
+- A [`DataFetcher`] to fetch data from a source
+- A [`ProcessArea`] to specify the area of interest
+- A [`DataPreprocessor`] to preprocess the fetched data
+- A [`SegmentationModel`] to do the inference on the preprocessed data
+- An [`SegmentationExporter`] to export the predictions dynamically as geospatial data
 
 These components are set up in a configuration file (.yaml file) that is passed to the pipeline.<br />
 The configuration file must have the following structure:
@@ -41,8 +41,7 @@ num_workers: 4
 
 The `name` field must be the name of the class that you want to use for the component.<br />
 The `config` field must contain its corresponding configuration.<br />
-Note that each class has its own configuration, which can be found in the
-[API reference](../api_reference/pipeline/segmentation_pipeline.md#aviary.pipeline.SegmentationPipelineConfig).
+Note that each class has its own configuration, which can be found in the [API reference].
 
 To run the segmentation pipeline, run the following command:
 
@@ -74,9 +73,17 @@ To run the segmentation pipeline, run the following command:
     -v path/to/file:/aviary/file
     ```
 
+  [`DataFetcher`]: ../api_reference/data/data_fetcher/data_fetcher.md
+  [`ProcessArea`]: ../api_reference/process_area.md
+  [`DataPreprocessor`]: ../api_reference/data/data_preprocessor/data_preprocessor.md
+  [`SegmentationModel`]: ../api_reference/inference/model/segmentation_model.md
+  [`SegmentationExporter`]: ../api_reference/inference/exporter/segmentation_exporter.md
+  [API reference]: ../api_reference/pipeline/segmentation_pipeline.md#aviary.pipeline.SegmentationPipelineConfig
+
 ---
 
 ## Next steps
 
-Have a look at the [how-to guide](../how_to_guides/cli/how_to_run_the_segmentation_pipeline.md)
-on how to run the segmentation pipeline with an example configuration file.
+Have a look at the [how-to guide] on how to run the segmentation pipeline with an example configuration file.
+
+  [how-to guide]: ../how_to_guides/cli/how_to_run_the_segmentation_pipeline.md

docs/how_to_guides/api/how_to_use_the_bounding_box.md

@@ -1,22 +1,25 @@
 ## How to use the bounding box
 
-Follow along this step-by-step guide to learn about the [`BoundingBox`](../../api_reference/bounding_box.md).
+Follow along this step-by-step guide to learn about the [`BoundingBox`].
 
 ???+ note "Open in Google Colab"
-    Open the how-to guide as an interactive
-    [notebook](https://www.githubtocolab.com/geospaitial-lab/aviary/blob/main/docs/how_to_guides/api/notebooks/how_to_use_the_bounding_box.ipynb)
-    in [Google Colab](https://colab.google)
-    or download the
-    [notebook](https://www.github.com/geospaitial-lab/aviary/blob/main/docs/how_to_guides/api/notebooks/how_to_use_the_bounding_box.ipynb) to run it locally.
+    Open the how-to guide as an interactive [notebook][notebook colab] in [Google Colab]
+    or download the [notebook][notebook github] to run it locally.
+
+  [`BoundingBox`]: ../../api_reference/bounding_box.md
+  [notebook colab]: https://www.githubtocolab.com/geospaitial-lab/aviary/blob/main/docs/how_to_guides/api/notebooks/how_to_use_the_bounding_box.ipynb
+  [Google Colab]: https://colab.google
+  [notebook github]: https://www.github.com/geospaitial-lab/aviary/blob/main/docs/how_to_guides/api/notebooks/how_to_use_the_bounding_box.ipynb
 
 ### Create a bounding box
 
 A bounding box specifies the spatial extent of an area of interest.
 
-You can pass the coordinates to the initializer of the
-[`BoundingBox`](../../api_reference/bounding_box.md).<br />
+You can pass the coordinates to the initializer of the [`BoundingBox`].<br />
 You can access the coordinates of the bounding box with the `x_min`, `y_min`, `x_max` and `y_max` attributes.
 
+  [`BoundingBox`]: ../../api_reference/bounding_box.md
+
 ``` python
 import aviary
 
@@ -143,8 +146,9 @@ for coordinate in bounding_box:
 
 #### Create a bounding box from a geodataframe
 
-You can create a bounding box from a geodataframe with the
-[`from_gdf`](../../api_reference/bounding_box.md#aviary.BoundingBox.from_gdf) class method.
+You can create a bounding box from a geodataframe with the [`from_gdf`] class method.
+
+  [`from_gdf`]: ../../api_reference/bounding_box.md#aviary.BoundingBox.from_gdf
 
 ``` python
 import geopandas as gpd
@@ -208,8 +212,9 @@ The red polygons represent the districts.
 
 ### Buffer the bounding box
 
-You can expand the bounding box with the
-[`buffer`](../../api_reference/bounding_box.md#aviary.BoundingBox.buffer) method.
+You can expand the bounding box with the [`buffer`] method.
+
+  [`buffer`]: ../../api_reference/bounding_box.md#aviary.BoundingBox.buffer
 
 ``` python
 bounding_box = aviary.BoundingBox(
@@ -255,8 +260,9 @@ The red polygon represents the original bounding box.
 
 ---
 
-You can also shrink the bounding box with the
-[`buffer`](../../api_reference/bounding_box.md#aviary.BoundingBox.buffer) method.
+You can also shrink the bounding box with the [`buffer`] method.
+
+  [`buffer`]: ../../api_reference/bounding_box.md#aviary.BoundingBox.buffer
 
 ``` python
 bounding_box = aviary.BoundingBox(
@@ -304,8 +310,9 @@ The red polygon represents the original bounding box.
 
 ### Quantize the bounding box
 
-You can align the bounding box to a grid with the
-[`quantize`](../../api_reference/bounding_box.md#aviary.BoundingBox.quantize) method.
+You can align the bounding box to a grid with the [`quantize`] method.
+
+  [`quantize`]: ../../api_reference/bounding_box.md#aviary.BoundingBox.quantize
 
 ``` python
 bounding_box = aviary.BoundingBox(
@@ -353,8 +360,9 @@ The red polygon represents the original bounding box.
 
 ### Convert the bounding box to a geodataframe
 
-You can convert the bounding box to a geodataframe with the
-[`to_gdf`](../../api_reference/bounding_box.md#aviary.BoundingBox.to_gdf) method.
+You can convert the bounding box to a geodataframe with the [`to_gdf`] method.
+
+  [`to_gdf`]: ../../api_reference/bounding_box.md#aviary.BoundingBox.to_gdf
 
 ``` python
 bounding_box = aviary.BoundingBox(