data_entry_ops · manufacturing · workflow
NASA JPL uses Labelbox video annotation to build life-detection ML algorithm 5x faster
The small MLIA team at NASA JPL needed to label large, diverse microscopy video datasets to train their life-detection ML model, but building and maintaining an in-house labeling solution was burdensome — PhD researchers were painstakingly writing Java GUIs and manually sorting data instead of doing ML work.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Microscopy video collected
Video datasets are collected from Digital Holographic Microscopes from water samples.
Tools used
LabelboxLabelbox BoostLabelbox WorkforceDigital Holographic Microscopesdecision treesSVMs
Outcome
Using Labelbox, the MLIA team achieved 5x the speed for editor customization and video annotation, completing setup in a single day against a budgeted week, while Boost delivered significant improvements to their model.
Results
Time savedbudgeted a week, completed in a day
Volume5x
Grounding & classification
Source type: vendor customer story
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