The 2023 NASA Space Apps Challenge was held on October 7-8, 2023. This challenge is now closed.

Summary

Data from the three Earth-observing platforms on the International Space Station are used to advance science across various disciplines, but currently only one of these platforms has a streamlined data monitoring and access pipeline. This limitation hampers the scientific community’s ability to integrate datasets and produce boundary shattering, innovative, interdisciplinary science. Your challenge is to expand the functionality of the open-source web-based tool—VSWIR (Visible to ShortWave InfraRed) Imaging Spectroscopy Interface for Open Science (VISIONS)— to include more remote sensing platforms and/or enhanced features.

Background

The space station is roughly the size of a football field and travels in low orbit around Earth at ~17,000 miles per hour or five miles per second. Astronauts living on the space station see 15 sunrises and sunsets a day, and will revisit any given location on Earth every three days. Several Earth-observing remote sensing platforms are attached to the space station, including EMIT (Earth Surface Mineral Dust Source Investigation), ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station), and GEDI (Global Ecosystem Dynamics Investigation).

These platforms collect imaging spectroscopy (EMIT), thermal infrared (ECOSTRESS), and waveform lidar (GEDI) data that are applied broadly in scientific communities to detect greenhouse gasses (EMIT), investigate urban heat islands (ECOSTRESS), analyze plant health (ECOSTRESS), monitor carbon biomass (GEDI), characterize habitat structure (GEDI), and monitor forests, fires, and the Earth surface (EMIT, ECOSTRESS, GEDI).

Scientists at NASA’s Jet Propulsion Laboratory (JPL) originally developed the NASA Advanced Multi-Mission Operations System (AMMOS) Multi-Mission Geographic Information System (MMGIS) application to track past and present Mars Rovers. Recently, they expanded MMGIS to forecast the footprint of EMIT imaging spectroscopy acquisitions. This new open-source application, called VISIONS, allows anyone to see when and where data has been and will be collected by EMIT. This information enables scientists to plan and execute in situ field campaigns synchronously with the overpass of the station, as well as where the EMIT data intersects with other datasets. These capabilities are crucial to evaluating quality of ongoing EMIT products and enable the broader scientific community to conduct novel research.

Currently, the VISIONS application can only be used with the EMIT platform. If VISIONS’ capabilities could be expanded to include other remote sensing platforms and additional features, the scientific community could more easily plan simultaneous data campaigns and integrate data sets from multiple platforms to produce boundary-shattering, innovative, interdisciplinary science.

Objectives

Your challenge is to improve the open-source web-based tool VISIONS by adding remote sensing datasets or new features. Consider incorporating ECOSTRESS or GEDI into the VISIONS application. Alternatively, consider improving an existing feature in VISIONS or developing a new feature of your own. There are many capabilities that could be added to VISIONS to increase its usefulness—from area-and point-based subsetting, to production of a heat map indicating the number of images available at a given location, to the ability to alert users when new data has been delivered or forecasted at a location of interest. If you choose to add data from another mission, think about the best way to do so. Is there a way to selectively filter for overlapping data from multiple sensors? If you were an Earth Scientist, what kind of information would be most important and useful to you? How can multiple data structures be incorporated in a meaningful way in a single application? There is a lot of space to devise your own creative improvements!

Are you experienced with Python, PostgreSQL (Postgre Structured Query Language), STAC (Spatio Temporal Asset Catalog), and cloud-optimized data formats? Have you identified a half-developed analysis feature in an app that you think you can complete? Do you have a creative idea for a feature that would enhance VISIONS? Do some investigating and think about what types of capacities and data scientists would want added to VISIONS and check the listed resources for some examples of features that you could potentially add.

Potential Considerations

You may (but are not required to) consider the following when developing your project:

VISIONS has well-written, readable code and a maintainable architecture.
If the service itself replies but the app is not producing the expected data, check the VISIONS service documentation or consider substituting deprecated services with currently functional data sources. The Resources section contains lists of example data sources and application programming interfaces (APIs).
Potential steps to complete the challenge could include:
- Briefly review VISIONS and MMGIS, their functionality, code bases, documentation, and videos.
- Download the VISIONS source code.
- Try to run VISIONS in your local development environment.
- Briefly review GEDI and/or ECOSTRESS, their data collection and delivery protocols, and metadata tags associated with data.
- Create your project by adding data or an analysis feature to VISIONS.
- Refine the new feature if there's still time.
- Submit your project, including the source code.
Simply providing maintenance capabilities to the original VISIONS application is considered valuable.
Feature completeness is preferred over feature richness. In other words, including a single, well-implemented analysis feature is preferred over incorporating multiple, half-developed ones.
Code clarity should be maintained to incentivize future hackathon participants to build upon your work.
Publishing a live version of your project is optional, but desirable.
If you're really confident in your work, you can submit a Pull Request with your updates to enable your work to be integrated into the original version of VISIONS. This is the spirit of Open Source!

For data and resources related to this challenge, refer to the Resources tab at the top of the page.

International Space Station Earth-Observing Data VISION-aries wanted!