Environmental Technology
AirSeed Technologies needed polygon-labeled maps to train computer vision models that guide drones to plant trees on degraded land.
Australian reforestation company using drones and AI to restore degraded land.
Needed large land areas segmented in QGIS; internal team lacked time and capacity.
Labeled 50 geofiles using QGIS; 6-person team handled annotation and QA over 5 months.
30+ ML-ready maps delivered on time, powering drone navigation and planting models.
An Australian company combining drones, machine learning, and seed pod delivery to accelerate reforestation.
Internal engineers didn’t have time for time-consuming GIS labeling
Needed precise segmentation to identify plantable zones in varied terrain
Files were large, complex, and came in different formats
Annotation had to match AirSeed’s internal GIS stack (QGIS)
Label Your Data created a polygon annotation workflow fully inside QGIS.
Each annotator worked on one geofile at a time
Segmentation followed detailed instructions from the client
A QA reviewer verified every file before delivery
Weekly check-ins aligned on tricky shapes or edge cases
The project began with a pilot batch and custom guidelines from AirSeed
Annotators labeled 50 test polygons each, reviewed by QA and the Account Manager. No rework was required after the first round.
Over 30 large geofiles annotated across a 5-month timeline
Output used to train models for optimal planting zone detection
No cleanup needed; labels fit AirSeed’s ML and GIS stack out of the box
On-time batches with clear and accurate output
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The workflow was smooth, progress was steady, and the quality consistently met our expectations throughout the project.
Jared Reabow
Vice President of Engineering
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