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TU Dublin Quotes

Label Your Data were genuinely interested in the success of my project, asked good questions, and were flexible in working in my proprietary software environment.

Quotes
TU Dublin

Kyle Hamilton

PhD Researcher at TU Dublin

Trusted by ML Professionals

Yale
Princeton University
KAUST
ABB
Respeecher
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Bizerba
Thorvald
Advanced Farm
Searidge Technologies

Environmental Technology

Land
Segmentation Annotation for
Drone Reforestation

Location:
Australia Australia
Services:
Data Annotation
Airseed

Overview

AirSeed Technologies needed polygon-labeled maps to train computer vision models that guide drones to plant trees on degraded land.

~50 large geofiles provided
5 month project timeline
5 annotators
1 QA
Client

Client

Australian reforestation company using drones and AI to restore degraded land.

Challenges

Challenges

Needed large land areas segmented in QGIS; internal team lacked time and capacity.

Solutions

Solutions

Labeled 50 geofiles using QGIS; 6-person team handled annotation and QA over 5 months.

Results

Results

30+ ML-ready maps delivered on time, powering drone navigation and planting models.

Client

An Australian company combining drones, machine learning, and seed pod delivery to accelerate reforestation.

Drone used for reforestation project combining machine learning and seed pod delivery
Woman working on a laptop, representing data annotation team solving GIS labeling and segmentation challenges

Challenges

1

Internal engineers didn’t have time for time-consuming GIS labeling

2

Needed precise segmentation to identify plantable zones in varied terrain

3

Files were large, complex, and came in different formats

4

Annotation had to match AirSeed’s internal GIS stack (QGIS)

Solution

Label Your Data created a polygon annotation workflow fully inside QGIS.

1

Each annotator worked on one geofile at a time

2

Segmentation followed detailed instructions from the client

3

A QA reviewer verified every file before delivery

4

Weekly check-ins aligned on tricky shapes or edge cases

Satellite image showing urban and vegetation areas.
Annotated map with color-coded polygons highlighting buildings, vegetation, and terrain zones for GIS segmentation.

Training

1

The project began with a pilot batch and custom guidelines from AirSeed

2

Annotators labeled 50 test polygons each, reviewed by QA and the Account Manager. No rework was required after the first round.

Results

1

Over 30 large geofiles annotated across a 5-month timeline

2

Output used to train models for optimal planting zone detection

3

No cleanup needed; labels fit AirSeed’s ML and GIS stack out of the box

4

On-time batches with clear and accurate output

Color-coded GIS map showing annotated roads, buildings, water, and vegetation areas used for AI training and planting zone detection

Start Free Pilot

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Airseed Quotes

The workflow was smooth, progress was steady, and the quality consistently met our expectations throughout the project.

Quotes
Airseed
Jared Reabow

Jared Reabow

Vice President of Engineering

Trusted by ML Professionals

Yale
Princeton University
KAUST
ABB
Respeecher
Toptal
Bizerba
Thorvald
Advanced Farm
Searidge Technologies

Why Projects Choose Label Your Data

No Forced Commitment

No Forced Commitment

Check our performance based on a free trial

Flexible Pricing

Flexible Pricing

Pay per labeled object or per annotation hour

Tool-Agnostic

Tool-Agnostic

Working with every labeling tool, even your custom tools

Quality Backed by SLAs

Quality Backed by SLAs

We commit to accuracy and deadlines – or you don’t pay