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What a label is
A label is the known or expected result recorded alongside an input. The Open Geospatial Consortium’s TrainingDML-AI standard defines a label as a known or expected result annotated as a value in a training sample. That definition is narrower than it sounds, and it is worth keeping in mind: in geospatial work, a “label” can also mean the text printed on a map, which is a different thing. In machine learning, the term refers only to the answer attached to a training example.
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The form of a label follows the task. A spam filter needs a class per message. A sentiment model needs a polarity or emotion tag. A named-entity model needs spans of text marked as people, places, or organizations. A vision model may need a box, a set of key points, or a class for every pixel.
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In supervised machine learning, labeled examples supply the target the model learns from. The model sees input features alongside the correct output and adjusts itself to reduce the gap between its predictions and those outputs. The OGC standard describes a training dataset as a collection of samples, often labeled with known terms or expected values for supervised learning, and notes that the dataset can be split into training, validation, and test sets.
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Labeling is not required for every kind of machine learning. The FDA glossary distinguishes supervised learning, where labeled data is provided to train the algorithm, from unsupervised approaches that work with unlabeled data, and from semi-supervised methods that combine supervised and unsupervised techniques. A project that clusters customers by purchasing behavior, for example, does not need anyone to label each customer in advance. Labeling becomes the central effort when the model is expected to reproduce a specific human or measured judgment.
Label formats by data type
The table below summarizes the label types named in Google Cloud’s and Amazon Web Services’ labeling guidance, plus the geospatial task types in the OGC standard. Examples are illustrative of the task type rather than an exhaustive list.
| Data type | Typical labels | Source for the label types |
|---|---|---|
| Images | Class (for example, whether a bird is present), bounding boxes, key points, pixel-level segmentation masks | AWS describes a range from a yes/no label to marking the exact pixels of an object |
| Text | Sentiment, intent, named entities, parts of speech, transcription of text inside an image or document | Google Cloud and AWS |
| Audio | Speech transcription, tags for wildlife or other sound events | Google Cloud and AWS |
| Video | Object tracking across frames, action recognition, scene segmentation | Google Cloud |
| Time series | Labels for trends, patterns, or anomalies in sensor or financial observations | Google Cloud |
| Geospatial imagery | Scene classification, object detection, semantic segmentation, change detection | OGC TrainingDML-AI task types |
How labels are produced
Three approaches dominate, and they are often combined in one project.
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Manual labeling
People inspect each example and assign the label. Manual work brings human judgment to nuanced or ambiguous cases, which is why it remains common for medical images, legal documents, and subjective content. The cost is time and labor, and throughput scales with the number of annotators.
Automated or programmatic labeling
Software or algorithms apply labels, often using rules or existing models. This can greatly increase throughput, but it can also copy errors or biases into the dataset. Google Cloud and IBM both stress that automated output needs evaluation and quality controls before it is trusted.
Hybrid, or human-in-the-loop, labeling
Humans label an initial subset. A model or set of rules then extends labels to more examples, and uncertain cases are routed back to people. AWS describes this pattern as feeding high-confidence automated results forward while sending lower-confidence results to human labelers. It is the approach most teams reach for when a dataset is too large to label by hand but too ambiguous to label automatically.
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Choosing the organizational route
The workforce model is a separate decision from the labeling method. IBM’s guidance lists internal teams, synthetic or programmatic methods, crowdsourcing, and outsourcing to managed teams. Each route involves trade-offs in expertise, management effort, worker quality, and quality assurance. IBM presents these as alternatives to compare against project requirements, not as a ranking.
A typical labeling workflow
A reliable project tends to follow the same sequence, whatever the data type:
- Decide what the model must predict. Write each label definition, the criteria for applying it, and worked examples for ambiguous cases.
- Choose a workflow and tools. Match the labeling method and workforce to the task, data sensitivity, and volume.
- Train annotators. Walk them through the guidelines using the edge cases you wrote down in step one.
- Label a sample, then the full set. Keep the sample small enough that a review of its results is still practical.
- Check consistency and errors. Use spot checks, inter-annotator agreement, and automated validation rules.
- Revise the guidelines when evaluation shows a mismatch. If the model performs poorly on a category, check whether the label definition or the annotations are the cause before changing the model.
Google Cloud recommends using representative data, privacy safeguards, and iterative review of how the labeled data affects model performance. Those steps apply regardless of the labeling method.
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What makes labels useful, and their limits
Labels are useful when they match the task and are applied consistently to examples that resemble the conditions where the model will run. Clear definitions and edge-case rules reduce avoidable ambiguity. Training annotators, consensus review, audits, and spot checks surface disagreement and mistakes.
AWS describes several specific mechanisms: sending the same object to multiple annotators and consolidating their responses, auditing labels after they are produced, and using active learning to choose which examples a human should label next. Active learning matters because it focuses limited annotator time on the examples the model is least certain about.
A label records an annotation. It is not a guarantee of objective truth. Some tasks, such as judging tone, rating the severity of a symptom, or deciding whether a clip contains a particular sound, involve subjective or ambiguous cases. In those tasks, document the decision rule, keep track of disagreements where they matter, and avoid describing disputed annotations as ground truth. The OGC standard treats provenance as part of describing training data, recording how the data was prepared, and it notes that class imbalance and mislabeling can affect model performance.
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Training data and test data
Training data is used to build the model. Test data is held back to estimate performance after training. The FDA glossary states that test data is never shown to the algorithm during training, and that for AI-enabled medical products the test data should be independent of the data used for training and tuning. Keeping these roles separate is what makes a reported accuracy figure meaningful. A model evaluated on its own training examples will look better than it performs on new data.
A worked official example
The National Institute of Standards and Technology published the RUFEERS (Recognizing Ultra Fine-grained Entities, Events, and Relations) Annotation Guidelines on May 18, 2026, as NIST Trustworthy and Responsible AI report 100-8. The guidelines instruct human annotators who create evaluation data for measuring entity, event, and relation extraction systems. The document is a useful illustration of how annotation instructions are written for a defined task and a defined evaluation purpose, rather than as general-purpose labeling advice.
Comparing labeling approaches and tools
When you compare labeling approaches or software, these six axes do most of the work:
- Task and modality: classification, text spans, boxes, segmentation, audio transcription, video tracking, or time-series labeling.
- Complexity and ambiguity: whether annotators need domain expertise or must resolve subjective edge cases.
- Quality controls: guideline support, reviewer workflows, consensus, audits, validation rules, and how disagreements are handled.
- Scale and duration: the volume of data, the speed required, and how often the labels will be revised.
- Data governance: privacy, access controls, provenance, and whether data may be sent to an external workforce or a hosted service.
- Operating burden: engineering setup, annotator management, and ongoing review.
These axes do not single out a best platform. Google Cloud describes specialized labeling tools with annotation management, quality-control, and collaboration features, and AWS describes managed human-labeler workflows through SageMaker Ground Truth. Product capabilities, pricing, regional availability, and program terms change, so confirm them on the vendor’s current pages before making a purchasing decision.
For definitional purposes, the sources agree on the core idea: training data labeling attaches task-specific, descriptive information to examples so a model can learn from them, and the quality of that information determines much of what the model can learn.
Sources cited: Google Cloud, “What is Data Labeling?” (guidance accessed October 7, 2026); Open Geospatial Consortium, TrainingDML-AI Part 1 (Conceptual Model, 2023) and Part 2 (JSON Encoding); U.S. Food and Drug Administration, Digital Health and Artificial Intelligence Glossary (adapting IMDRF 2022 terminology); Amazon Web Services, “What is Data Labeling?” (accessed October 7, 2026); IBM, “What Is Data Labeling?” (first published September 28, 2021; updated January 23, 2026); National Institute of Standards and Technology, RUFEERS Annotation Guidelines, report 100-8 (May 18, 2026).
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