data_entry_ops · education · workflow
NASA JPL uses Labelbox to build an AI-powered Martian frost map from multi-source imagery and thermal data
NASA JPL needed to construct an ML training dataset combining expert-annotated high-resolution imagery with thermal data to power a machine learning model for mapping frost on the Martian surface — a challenging task because visible indicators of frost vary widely and in subtle ways.
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 · Multi-source data collection
Researchers collected high-resolution image data from multiple sources including the Mars Reconnaissance Orbiter and thermal data from the Mars Climate Sounder.
Tools used
Labelbox AnnotateLabelbox
Outcome
The frost map dataset was created over many iterations via Labelbox and will be published to the broader scientific community for further refinement.
Grounding & classification
Source type: vendor customer story
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computer visionhuman review describednamed customertools describedworkflow describedgovernmentvendor customer storydata entry opsquality assurancehuman review queue