ADAPT Multi-Mission Payload

An open source platform for deploying state of the art deep-neural-network computer vision in real time on small unmanned aircraft systems (sUAS).

• Optimized drone-based collection of imagery and geospatial metadata with live feedback to maintain quality control.

• Integration with the open source do-it-yourself AI toolkit VIAME to annotate data and train mission-specific image-processing models.

• Upload your models for aerial deployment with real-time, georegistered analytics wirelessly transmitted to a ground station computer and beyond for rapid dissemination.

• Commodity hardware components, CAD models, and open-source software allows organizations to cheaply and easily build their own payloads

Supports a variety of unique missions

• Sea and River Ice Monitoring
• Monitoring Arctic Mammal Populations
• Person Search and Rescue
• Wild Fire Monitoring
• Coastline Erosion Monitoring

Ongoing work on the ADAPT project is funded by NOAA to support key missions.

Source Code

The ADAPT payload source code is hosted here: https://gitlab.kitware.com/adapt/adapt_ros_ws or Try the simulator with docker. Please use the Issue Tracker on Gitlab or contact us here.

License

This repository is under the Apache 2.0 license, see NOTICE and LICENSE file.

Documentation

Documentation: https://kitware.github.io/adapt/

Events

• Kitware and ACUASI September 2021 data collection in Fairbanks Alaska.
• The 3rd NOAA Workshop on Leveraging AI in Environmental Sciences

Papers / Presentations

• (2022) National Innovation Center Seminar: Slides

• (2022) Ocean Sciences Meeting: Slides

• (2022) 1st International Workshop on Practical Deep Learning in the Wild at AAAI Conference on Artificial Intelligence: Paper

• (2021) NOAA Innovators Series: Slides, Recording

• (2021) The 3rd NOAA Workshop on Leveraging AI in Environmental Sciences: Slides, Recording

Site

For more information go to https://kitware.github.io/adapt/