Add Docs glossary links (#16448)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/detect/xview.md

@@ -6,7 +6,7 @@ keywords: xView dataset, overhead imagery, satellite images, object detection, h
 
 # xView Dataset
 
-The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world annotated using bounding boxes. The goal of the xView dataset is to accelerate progress in four computer vision frontiers:
+The [xView](http://xviewdataset.org/) dataset is one of the largest publicly available datasets of overhead imagery, containing images from complex scenes around the world annotated using bounding boxes. The goal of the xView dataset is to accelerate progress in four [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) frontiers:
 
 1. Reduce minimum resolution for detection.
 2. Improve learning efficiency.
@@ -19,8 +19,8 @@ xView builds on the success of challenges like Common Objects in Context (COCO)
 
 - xView contains over 1 million object instances across 60 classes.
 - The dataset has a resolution of 0.3 meters, providing higher resolution imagery than most public satellite imagery datasets.
-- xView features a diverse collection of small, rare, fine-grained, and multi-type objects with bounding box annotation.
-- Comes with a pre-trained baseline model using the TensorFlow object detection API and an example for PyTorch.
+- xView features a diverse collection of small, rare, fine-grained, and multi-type objects with [bounding box](https://www.ultralytics.com/glossary/bounding-box) annotation.
+- Comes with a pre-trained baseline model using the TensorFlow object detection API and an example for [PyTorch](https://www.ultralytics.com/glossary/pytorch).
 
 ## Dataset Structure
 
@@ -42,7 +42,7 @@ A YAML (Yet Another Markup Language) file is used to define the dataset configur
 
 ## Usage
 
-To train a model on the xView dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
+To train a model on the xView dataset for 100 [epochs](https://www.ultralytics.com/glossary/epoch) with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model [Training](../../modes/train.md) page.
 
 !!! example "Train Example"
 
@@ -71,7 +71,7 @@ The xView dataset contains high-resolution satellite images with a diverse set o
 
 ![Dataset sample image](https://github.com/ultralytics/docs/releases/download/0/overhead-imagery-object-detection.avif)
 
-- **Overhead Imagery**: This image demonstrates an example of object detection in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
+- **Overhead Imagery**: This image demonstrates an example of [object detection](https://www.ultralytics.com/glossary/object-detection) in overhead imagery, where objects are annotated with bounding boxes. The dataset provides high-resolution satellite images to facilitate the development of models for this task.
 
 The example showcases the variety and complexity of the data in the xView dataset and highlights the importance of high-quality satellite imagery for object detection tasks.
 
@@ -137,11 +137,11 @@ The xView dataset stands out due to its comprehensive set of features:
 - Over 1 million object instances across 60 distinct classes.
 - High-resolution imagery at 0.3 meters.
 - Diverse object types including small, rare, and fine-grained objects, all annotated with bounding boxes.
-- Availability of a pre-trained baseline model and examples in TensorFlow and PyTorch.
+- Availability of a pre-trained baseline model and examples in [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and PyTorch.
 
 ### What is the dataset structure of xView, and how is it annotated?
 
-The xView dataset comprises high-resolution satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It encompasses over 1 million objects across 60 classes in approximately 1,400 km² of imagery. Each object within the dataset is annotated with bounding boxes, making it ideal for training and evaluating deep learning models for object detection in overhead imagery. For a detailed overview, you can look at the dataset structure section [here](#dataset-structure).
+The xView dataset comprises high-resolution satellite images collected from WorldView-3 satellites at a 0.3m ground sample distance. It encompasses over 1 million objects across 60 classes in approximately 1,400 km² of imagery. Each object within the dataset is annotated with bounding boxes, making it ideal for training and evaluating [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) models for object detection in overhead imagery. For a detailed overview, you can look at the dataset structure section [here](#dataset-structure).
 
 ### How do I cite the xView dataset in my research?