# Use cases: programmable labeling interfaces

A gallery of labeling and evaluation interfaces built with Label Studio Enterprise programmable interfaces. Each entry is a starting point to adapt, not a fixed template.

**URL:** https://humansignal.com/use-cases

## Categories
- [Document AI](https://humansignal.com/use-cases/document-ai)
- [LiDAR](https://humansignal.com/use-cases/lidar)
- [Timeseries](https://humansignal.com/use-cases/timeseries)

## Interfaces (3)

### DocLang Document Annotation

**Category:** Document AI  
**URL:** https://humansignal.com/use-cases/document-ai/doclang-document-annotation

Draw regions on a document, set reading order, and fix table structure while the DocLang XML updates beside you. Docling predictions load in for correction, so your annotators fix conversions instead of building them from scratch.

Your documents do not look like Docling's training data: a lab report or an internal invoice has little in common with the public PDFs a converter learned on. This interface gives you a place to see where that breaks.

You draw bounding boxes around document elements, layer reading-order paths across them, and correct merged regions and table structure, with the DocLang XML updating in a sidebar as you work. Predictions from the Docling ML backend load as a starting point, and the result exports as a .dclx archive carrying both the structure and the page images.

**Links**
- [Open the Interface](https://app.humansignal.com/interfaces/-8/overview)

### LiDAR

**Category:** LiDAR  
**Data types:** LiDAR, COPC  
**URL:** https://humansignal.com/use-cases/lidar/lidar-annotation

Point clouds, calibrated cameras, temporal tracks, and QA -- all in one workspace.

Most perception teams annotate the same scene four times: once for cuboids, again for camera views, again for tracking, and once more for review. This interface keeps all of it in one workspace.

Annotators work the 3D point cloud alongside a top-down view and a camera panel that follows whatever cuboid is selected. Keyframe interpolation carries objects across frames, so adjusting one position updates the frames around it instead of redrawing each one — which is where identity swaps usually creep in. Model predictions load as editable suggestions to accept, adjust, or reject. Validation runs inside the task, so blocking errors surface before completion rather than in a separate audit pass weeks later.

Handles COPC files in the tens of millions of points, with cuboids, points, lines, lanes, and segmentation masks side by side.

**Links**
- [Read the Blog](https://humansignal.com/blog/lidar-annotation-label-studio/)

### Wearable Activity

**Category:** Timeseries  
**Data types:** Timeseries  
**URL:** https://humansignal.com/use-cases/timeseries/wearable-activity-timeseries-annotation

Scrub heart rate, HRV, accelerometer, respiration, and SpO2 on one time base, then mark spans in the activity, sleep, or rhythm tab. Both the label sets and the task fields they read are parameters you set per project.

A wearable session is five signals that have to be read against each other: heart rate climbing after the accelerometer, HRV dropping, respiration following, SpO2 holding until it dips. This interface puts all five on one time base with a context track underneath, so precomputed activity, sleep, and rhythm segments stay visible while you work, and outdoor sessions carry a GPS route beside the signals.

Labeling splits across three tabs, and the vocabularies ship as defaults you replace: Running through Sedentary for activity, Awake through Deep Sleep for sleep, Sinus Rhythm through AFib for rhythm, each label with a color and a hotkey.

The task fields are parameters too, so the interface reads whatever your sensor, segment, and route columns are called instead of forcing your data into its names.

**Links**
- [Open the Interface](https://app.humansignal.com/interfaces/-6/overview)

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