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This content is aimed at members of Earth Lab and the Analytics Hub. We use this wiki to track the status of projects, track compute infrastructure, and assist with onboarding new team members.
New to Earth Lab? Check out our onboarding page for help getting started! You may also find it useful to explore our best practices wiki as you begin projects in Earth Lab.
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Incubator projects are science projects that we undertake with Eardevelopmentth Lab members and affiliates that align with the vision of the Analytics Hub.
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Software projects include open source packages developed by our team and also those projects that receive contributions from the Analytics Hub (these contributions are useful for reporting purposes)
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Future development items are longer term to-do tasks/a wish list for future resource development and research activities.
To better track the compute infrastructure development and curation by our team, we have the following pages:
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Cloud computing, including Amazon Web Services, to enable scalable compute.
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Docker containerization technology to enhance workflow portability.
The Analytics Hub engages Earth Lab members and affiliates in incubator science projects:
- Understanding and predicting wildfire extremes: https://github.com/mbjoseph/wildfire-extremes
This is a collaborative project with team fire and others to try to improve upon previous efforts to model wildfire extremes.
- Deep learning to identify sonar anomalies: https://github.com/mbjoseph/sonar-anomalies (private)
This is a collaboration with Carrie Bell and Kris Karnauskas to use deep learning to automate anomaly detection in NOAA sonar data.
- Supporting wildfire emergency response with social media: https://github.com/mbjoseph/emergency-tweet-filter (private)
This is a project with Jeremy Diaz and Lise St. Denis aimed at automating Twitter filtering to deliver relevant tweets to emergency managers in real time as wildfire disasters unfold.
Incorporating Planet Dove Imagery for Scientific Investigation: https://github.com/joemcglinchy/<link_coming>
This project seeks to understand how we can incorporate the multi-system and multitemporal image data acquired by Planet Dove satellites for scientific investigation along with other well characterized satellite image data sources such as Landsat.
Impervious Surface Mapping in Urban Areas using DigitalGlobe WorldView Satellite Imagery: https://github.com/joemcglinchy/<link_coming>
This project investigates mapping urban cover type using DigitalGlobe multispectral satellite data acquired through their Geospatial Big Data (GBDX) platform.
Tracking wildfire spread using IR satellites and machine learning:
This project is prototyping an approach to predict wildfire spread using GOES-16 ABI infrared observations and a machine leaning algorithm detecting hot spots and screening for clouds. The goal is to demonstrate a use case for the Tools-Applications-Processing (TAP) Lab as part o the CU-Boulder AIA Grant.