Greenroom Geolabeller - Enriching datasets for real-world AI

Cover Image for Greenroom Geolabeller -  Enriching datasets for real-world AI
Harry Hubbert
Harry Hubbert

Deploying vision-based AI for real world applications is a far more complex task than typical AI demonstrations that you probably see online.Tasks such as tracking dynamic targets from moving platforms, with sufficient reliability and accuracy are incredibly demanding tasks that require a system to use all available information to make informed recommendations. Changing environmental conditions such as lighting and ground terrain significantly complicates detection and classification taskings compared to urban or indoor taskings.

The outdoors provides situational context that we humans often take for granted. Intuition for attributes like relative size, aspect, movement, and distance can greatly enhance performance for tasks like detection and classification. Robust and reliable Machine Learning (ML) models must learn from more than just pixels.

GR has developed Geolabeller to build, enrich, and manage datasets with online web tooling built upon the latest in open source AI technologies. Geolabeller brings together efficient workflows to fuse image labelling processes with geo-spatial metadata for comprehensive training sets that allow ML to train with the data that humans rely upon. Applying the latest in cutting-edge labelling technology, we can exponentially develop datasets with auto-labelling techniques and in-built quality control measures.

The Geolabeller tooling is a portal into our ML training and deployment pipeline that forms a key part of the Greenroom Vision platform. With Greenroom Vision we can train and deploy advanced models with confidence for real-world applications such as remote and autonomous vehicle control, remote monitoring, and survey taskings.

The Geolabeller tool is accessible via a secure online portal for key industry and academic partners. Geolabeller will significantly help with data labelling, semantic segmentation, computer vision, machine learning and deep learning challenges, across land, sky and sea domains.

Our software team will assist you in designing and producing models or datasets to solve your problems, with an emphasis on integration and compatibility with other systems and sensors. We have significant experience expanding vision-based AI for robotics control, dashboards & reports for efficient decision making. If you have any questions or would like a demonstration of Geolabeller or our other products please contact us at


Geolabeller tool being applied to segmentation challenges.


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