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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Fireballs in the Sky: An Augmented Reality Citizen Science Program

Fireballs in the Sky is an innovative Australian citizen science program that connects the public with the research of the Desert Fireball Network (DFN). This research aims to understand the early workings of the solar system, and Fireballs in the Sky invites people around the world to learn about this science, contributing fireball sightings via a user-friendly augmented reality mobile app. Tens of thousands of people have downloaded the app world-wide and participated in the science of meteoritics. The Fireballs in the Sky app allows users to get involved with the Desert Fireball Network research, supplementing DFN observations and providing enhanced coverage by reporting their own meteor sightings to DFN scientists. Fireballs in the Sky reports are used to track the trajectories of meteors - from their orbit in space to where they might have landed on Earth. Led by Phil Bland at Curtin University in Australia, the Desert Fireball Network (DFN) uses automated observatories across Australia to triangulate trajectories of meteorites entering the atmosphere, determine pre-entry orbits, and pinpoint their fall positions. Each observatory is an autonomous intelligent imaging system, taking 1000 by 36 megapixel all-sky images throughout the night, using neural network algorithms to recognize events. They are capable of operating for 12 months in a harsh environment, and store all imagery collected. We developed a completely automated software pipeline for data reduction, and built a supercomputer database for storage, allowing us to process our entire archive. The DFN currently stands at 50 stations distributed across the Australian continent, covering an area of 2.5 million square kilometers. Working with DFN's partners at NASA's Solar System Exploration Research Virtual Institute, the team is expanding the network beyond Australia to locations around the world. Fireballs in the Sky allows a growing public base to learn about and participate in this exciting research.

Day, Brian↗
DOE OSTI2024

Deep learning models map rapid plant species changes from citizen science and remote sensing data

Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learning model trained using remote sensing images from California paired with half a million citizen science observations that can map the distribution of over 2,000 plant species. Our model— Deepbiosphere— not only outperforms many common species distribution modeling approaches (AUC 0.95 vs. 0.88) but can map species at up to a few meters resolution and finely delineate plant communities with high accuracy, including the pristine and clear-cut forests of Redwood National Park. These fine-scale predictions can further be used to map the intensity of habitat fragmentation and sharp ecosystem transitions across human-altered landscapes. In addition, from frequent collections of remote sensing data, Deepbiosphere can detect the rapid effects of severe wildfire on plant community composition across a 2-y time period. These findings demonstrate that integrating public earth observations and citizen science with deep learning can pave the way toward automated systems for monitoring biodiversity change in real-time worldwide.

Gillespie, Lauren E.↗
DOE OSTI2026

Citizen science coupled with machine learning to quantify green-blue infrastructure cooling potential in Maricopa County, Arizona

Here, this study investigates the spatiotemporal cooling performance of green and blue infrastructure (GBI) in the Dobson Ranch urban neighborhood in Phoenix, Arizona. We leveraged citizen science near-surface (2 m) air temperature (Tair) measurements to train a highly accurate Tair predicting LightGBM machine learning model (R 2 : 0.986, MAE: 0.251 °C, RMSE: 0.585 °C). On June 16, 2024, the park area exhibited approximately 1 °C cooling effect (relative to the neighborhood mean) during both day and night. In contrast, the nearby artificial lake exhibited a stronger cooling effect of 2.4 °C during the day but a slight warming of 0.3 °C at night. At 00:00, locations 50 m downwind of the park were 0.3 °C warmer than the park, while locations 50 m upwind were 0.8 °C warmer. At 11:00, we observed that the downwind area is 0.8 °C cooler and the upwind area is 0.6 °C warmer—at the same 50 m distances relative to the park. We also observed 1 °C cooler and warmer effects respectively at the same 50 m downwind and upwind locations at 19:00 on June 17, 2024. Our data-driven analysis highlights potential limitations of car-traverse measurements, showing that failure to account for temporal variations during the traverse can lead to overestimation of Tair at night and underestimation during the day. Our analysis also showed only a weak correlation (coefficient: 0.48) between Landsat-derived land surface temperature (LST) and model predicted Tair at the time of the local Landsat overpass (∼11.00). This highlights the potential error of relying solely on LST for human thermal exposure analysis—particularly within the heterogenous built-environment.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Follow-up Imaging of Disk Candidates from the Disk Detective Citizen Science Project: New Discoveries and False Positives in WISE Circumstellar Disk Surveys

The Disk Detective citizen science project aims to find new stars with excess 22 m emission from circumstellar dust in the All WISE data release from the Wide-field Infrared Survey Explorer. We evaluated 261 Disk Detective objects of interest with imaging with the Robo-AO adaptive optics instrument on the 1.5 m telescope at Palomar Observatory and with RetroCam on the 2.5 m du Pont Telescope at Las Campanas Observatory to search for background objects at 0 15–12 separations from each target. Our analysis of these data leads us to reject 7% of targets. Combining this result with statistics from our online image classification efforts implies that at most7.9%±0.2% of All WISE-selected infrared excesses are good disk candidates. Applying our false-positive rates to other surveys, we find that the infrared excess searches of McDonald et al. and Marton et al. all have false-positiverates >70%. Moreover, we find that all 13 disk candidates in Theissen & West with W4 signal-to-noise ratio >3are false positives. We present 244 disk candidates that have survived vetting by follow-up imaging. Of these,213 are newly identified disk systems. Twelve of these are candidate members of comoving pairs based on Gaia astrometry, supporting the hypothesis that warm dust is associated with binary systems. We also note the discovery of 22 m excess around two known members of the Scorpius–Centaurus association, and we identifyknown disk host WISEA J164540.79-310226.6 as a likely Sco-Cen member. Thirty of these disk candidates arecloser than 125 pc (including 26 debris disks), making them good targets for both direct-imaging exoplanetsearches.

Wide-field Infrared Survey Explorer↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗
NASA NTRSDate not supplied

Growing Beyond Earth: Student Citizen Science Contributing to Space Crop Production

Fairchild Tropical Botanic Garden and NASA have been partnering since 2015 to conduct a citizen science education program for middle and high school students called Growing Beyond Earth (GBE). Growing Beyond Earth is a multi-classroom science project designed to advance NASA’s research on growing plants in space. GBE was implemented locally and scaled nationally under two NASA Grants. Now serving more than 250 schools and over 10,000 middle and high school students nationwide, GBE successfully improved STEM education. It also contributed student-generated data to NASA, improving NASA research on the ground and on ISS, with two student-selected crops grown in space. GBE is unique in its focus on real scientific research, enabling student “citizen scientists” to contribute data toward NASA mission planning. Each classroom receives a Fairchild-designed plant habitat analogous to the plant growing equipment aboard the International Space Station (ISS). Fairchild and NASA scientists train teachers to conduct in-classroom GBE experiments, and students then share experimental data online with NASA. As NASA looks toward a long-term human presence beyond Earth’s orbit, there are specific science, technology, engineering, and math challenges related to food production in space. During this presentation, learn how GBE is addressing those challenges by expanding the diversity and quality of edible plants that can be grown aboard spacecraft. We will share the significant scientific and educational results that have come out of this partnership and explain how we quickly pivoted to allow students to continue to contribute during the COVID-era. Finally, we will explain how on Earth, GBE is also advancing technologies for growing plants in urban, indoor, and other resource-limited settings through the GBE Maker challenge for High School, University, and Professional communities of Makers across the country to develop the next generation of space crop production technologies. These programs are supported by NASA.

Growing Beyond Earth↗
NASA NTRS2020

Identifying Hurricane Impacts on Barbuda Using Citizen Science Ground Observations, Drone Photography and Satellite Imagery

Barbuda is a low-lying leeward island in the West Indies. In September 2017 Hurricane Irma battered the island with 185 mph winds. Damage to housing and infrastructure was so extensive that all 1800 residents of the island were immediately evacuated post hurricane to the neighboring island of Antigua. Our research is focused on documenting and analyzing the landscape scale changes that took place on the island as a result of the hurricane using a nested methodology. Ground level observations were recorded using a beta version of the Global Learning and Observation to Benefit the Environment (GLOBE) Observer Land Cover tool. Aerial photographs provide medium scale landscape resolution. Satellite images were processed to provide NDVI values. We describe how nested data at several spatial scales enable detailed description of Hurricane Irma’s impact across the landscape of this 62 sq. mile island. Here we are connecting observations at different spatial scales, using citizen science observations, aerial drone photography and satellite imagery to document and analyze hurricane impacts on the island of Barbuda, eastern Caribbean.

Barbuda↗
NASA NTRS2020

Spitzer Follow-up of Extremely Cold Brown Dwarfs Discovered by the Backyard Worlds: Planet 9 Citizen Science Project

We present Spitzer follow-up imaging of 95 candidate extremely cold brown dwarfs discovered by the Backyard Worlds: Planet 9 citizen science project, which uses visually perceived motion in multi epoch Wide-field Infrared Survey Explorer(WISE)images to identify previously unrecognized substellar neighbors to the Sun. We measure Spitzer[3.6]–[4.5]color to photo type our brown dwarf candidates, with an emphasis on pinpointing the coldest and closest Y dwarfs within our sample. The combination of WISE and Spitzer astrometry provides quantitative confirmation of the transverse motion of 75 of our discoveries. Nine of our motion-confirmed objects have best-fit linear motions larger than 1′′yr−1; our fastest-moving discovery is WISEA J155349.96+693355.2(μ≈215 yr−1),a possible T-type sub dwarf. We also report a newly discovered wide-separation(∼400 au)T8 comoving companion to the white dwarf LSPM J0055+5948(the fourth such system to be found), plus a candidate late T companion to the white dwarf LSR J0002+6357 at 55 projected separation(∼8700 au if associated). Among our motion-confirmed targets, five have Spitzer colors most consistent with spectral type Y. Four of these five have exceptionally red Spitzer colors suggesting types of Y1 or later, adding considerably to the small sample of known objects in this especially valuable low-temperature regime. Our Y dwarf candidates begin bridging the gap between the bulk of the Y dwarf population and the coldest known brown dwarf.

Y dwarfs↗
NASA NTRS2023

Understanding Volume Estimation Uncertainty of Lakes and Wetlands Using Satellites and Citizen Science

We studied variations in the volume of water stored in small lakes and wetlands using satellite remote sensing and lake water height data contributed by citizen scientists. A total of 94 water bodies across the globe were studied using satellite data in the optical and microwave wavelengths from Landsat 8, Sentinel-1, and Sentinel-2. The uncertainty in volume estimation as a function of geography and geophysical factors, such as cloud cover, precipitation, and water surface temperature, was studied. The key finding that emerged from this global study is that uncertainty is highest in regions with a distinct precipitation season, such as in the monsoon dominated South Asia or the Pacific Northwestern region of the USA. This uncertainty is further compounded when small lakes and wetlands are seasonal with alternating land use as a water body and agricultural land, such as the wetlands of Northeastern Bangladesh. On an average, 45% of studied lakes could be estimated of their volume change with a statistical significant uncertainty that is less than the expected volume in South Asia. In North America, this statistically significant uncertainty in volume estimation was found to be around 50% in lakes eastward of the 108th meridian with lowest uncertainty found in lakes along the East coast of the USA. The article provides a baseline for understanding the current state of the art in estimating volumetric change of lakes and wetlands using citizen science in anticipation of the recently launched Surface Water and Ocean Topography Mission.

Shahzaib Khan↗
NASA NTRS2016

CosmoQuest: Better Citizen Science Through Education

In the modern era, NASA SMD missions and facilities are producing data at a rate too great for the science community to maximally utilize. While software can help, what is really needed is additional eyes, hands, and minds - help we can find in the form of citizen scientist volunteers. The CosmoQuest virtual research facility has demonstrated through published research results that classroom students and the public can, with proper training and support from Subject Matter Experts (SMEs), fill roles more traditionally filled by university students. The research question behind CosmoQuest's creation was simple: if students and the public are provided a properly scaffolded experience that mirrors that of researchers, will they come and perform as well as our students? and can they rise up to be research collaborators? In creating CosmoQuest, we started with a core of citizen science portals, educational materials for both students and life-long learners, and collaboration areas. These three primary focuses mirror the research, courses, and collaboration spaces that form the foundation of a university department. We then went on to add the features that make a center stand out - we added seminars in the form of Google Hangouts on Air, planetarium content through our Science on the Half Sphere program, and even the chance to vicariously attend conferences through live blogging by our team members. With this design for a virtual research facility, the answer to our foundational question has been a resounding yes; the public can aid us in doing science provided they are properly trained. To meet the needs of our population we have developed four areas of engagement: research, education, media, and community.

Gay, P. L.↗
NASA NTRS2019

Evaluation of GEOS Total Cloud Fraction with GLOBE Citizen Science Observations and Co-Located Satellite Data

Here we evaluate the total cloud fraction in cycled forecast experiments with the NASA Global Earth Observing System (GEOS) model. Forecasts were run for summer and winter periods of 2017/2018 and compared with ground-based and satellite observations. Citizen science observations from the Global Learning and Observations to Benefit the Environment (GLOBE) Program were matched with MODIS satellite overpasses and geostationary cloud fraction, yielding a dataset of coincident observations for comparison with hourly model output. The observations indicate a model bias toward overcast and clear conditions, with underestimation of intermediate cloud fractions. We investigate underestimation of variance in the sub-grid total water probability density function (PDF) as a possible cause. The PDF determines large-scale cloud fraction as the sub-grid fraction with total water exceeding the saturation specific humidity. We conduct model experiments in which the shape, width, and spatial dependence of the PDF are varied, and consider the impacts on cloud fraction relative to the combined observations.

Starke, Matthew↗
NASA NTRS2018

General Aviation Citizen Science Study to Help Tackle Remote Sensing of Harmful Algal Blooms (HABs)

We present a new, low-cost approach, based on volunteer pilots conducting high-resolution aerial imaging, to help document the onset, growth, and outbreak of harmful algal blooms (HABs) and related water quality issues in central and western Lake Erie. In this model study, volunteer private pilots acting as citizen scientists frequently flew over 200 mi of Lake Erie coastline, its islands, and freshwater estuaries, taking high-quality aerial photographs and videos. The photographs were taken in the nadir (vertical) position in red, green, and blue (RGB) and near-infrared (NIR) every 5 s with rugged, commercially available built-in Global Positioning System (GPS) cameras. The high-definition (HD) videos in 1080p format were taken continuously in an oblique forward direction. The unobstructed, georeferenced, high-resolution images, and HD videos can provide an early warning of ensuing HAB events to coastal communities and freshwater resource managers. The scientists and academic researchers can use the data to compliment a collection of in situ water measurements, matching satellite imagery, and help develop advanced airborne instrumentation, and validation of their algorithms. This data may help develop empirical models, which may lead to the next steps in predicting a HAB event as some watershed observed events changed the water quality such as particle size, sedimentation, color, mineralogy, and turbidity delivered to the Lake site. This paper shows the efficacy and scalability of citizen science (CS) aerial imaging as a complimentary tool for rapid emergency response in HABs monitoring, land and vegetation management, and scientific studies. This study can serve as a model for monitoring/management of freshwater and marine aquatic systems.

Ansari, Rafat R.↗
NASA NTRS2017

The First Brown Dwarf Discovered by the Backyard Worlds: Planet 9 Citizen Science Project

The Wide-field Infrared Survey Explorer (WISE) is a powerful tool for finding nearby brown dwarfs and searching for new planets in the outer solar system, especially with the incorporation of NEOWISE and NEOWISE Reactivation data. However, so far, searches for brown dwarfs in WISE data have yet to take advantage of the full depth of the WISE images. To efficiently search this unexplored space via visual inspection, we have launched anew citizen science project, called "Backyard Worlds: Planet 9," which asks volunteers to examine short animations composed of difference images constructed from time-resolved WISE co adds. We report the first new substellar object discovered by this project, WISEA J110125.95+540052.8, a T5.5 brown dwarf located approximately 34 pc from the Sun with a total proper motion of approx.0. "7/ yr. WISEA J110125.95+540052.8 has a WISE W2 magnitude of W2 = 15.37+/- 0.09; our sensitivity to this source demonstrates the ability of citizen scientists to identify moving objects via visual inspection that are 0.9 mag fainter than the W2 single-exposure sensitivity, a threshold that has limited prior motion-based brown dwarf searches with WISE.

Wide-field Infrared Survey Explorer (WISE)↗
NASA NTRS2019

A 3 Gyr White Dwarf with Warm Dust Discovered via the Backyard Worlds: Planet 9 Citizen Science Project

Infrared excesses due to dusty disks have been observed orbiting white dwarfs with effective temperatures between 7200 and 25,000 K, suggesting that the rate of tidal disruption of minor bodies massive enough to create a coherent disk declines sharply beyond 1 Gyr after white dwarf formation. We report the discovery that the candidate white dwarf LSPM J0207+3331, via the Backyard Worlds: Planet 9 citizen science project and Keck Observatory follow-up spectroscopy, is hydrogen dominated with a luminous compact disk (L IR/L star = 14%) and an effective temperature nearly 1000 K cooler than any known white dwarf with an infrared excess. The discovery of this object places the latest time for large-scale tidal disruption events to occur at ~3 Gyr past the formation of the host white dwarf, making new demands of dynamical models for planetesimal perturbation and disruption around post-main-sequence planetary systems. Curiously, the mid-infrared photometry of the disk cannot be fully explained by a geometrically thin, optically thick dust disk as seen for other dusty white dwarfs, but requires a second ring of dust near the white dwarf's Roche radius. In the process of confirming this discovery, we found that careful measurements of WISE source positions can reveal when infrared excesses for white dwarfs are co-moving with their hosts, helping distinguish them from confusion noise.

Debes, John H.↗
NASA NTRS2020

WISE 2150-7520AB: A Very Low-mass, Wide Comoving Brown Dwarf System Discovered through the Citizen Science Project Backyard Worlds: Planet 9

We report the discovery of WISE 2150-7520AB (W2150AB): a widely separated (∼341 au) very low-mass L1 + T8 co-moving system. The system consists of the previously known L1 primary 2MASS J21501592-7520367 and a newly discovered T8 secondary found at position 21:50:18.99–75:20:54.6 (MJD = 57947) using Wide-field Infrared Survey Explorer data via the Backyard Worlds: Planet 9 citizen science project. We present Spitzer ch1 and ch2 photometry (ch1–ch2 = 1.41 ± 0.04 mag) of the secondary and Folded-port InfraRed Echellette prism spectra of both components. The sources show no peculiar spectral or photometric signatures, indicating that each component is likely field age. Using all observed data and the Gaia DR2 parallax of 41.3593 ± 0.2799 mas for W2150A we deduce fundamental parameters of log(L(bol)/L(☉)) = −3.69 ± 0.01, T(eff) = 2118 ± 62 K, and an estimated mass = 72 ± 12 M(Jup) for the L1 and log(L(bol)/L(☉)) = −5.64 ± 0.02, T(eff) = 719 ± 61 K, and an estimated mass = 34 ± 22 M(Jup) for the T8. At a physical separation of ∼341 au this system has E(bin) = 1041 erg, making it the lowest binding energy system of any pair with M(tot) < 0.1 M(Sun) not associated with a young cluster. It is equivalent in estimated mass ratio, E(bin), and physical separation to the ∼2 Myr M7.25 + M8.25 binary brown dwarf 2MASS J11011926-7732383AB (2M1101AB) found in the Chameleon star-forming region. W2150AB is the widest companion system yet observed in the field where the primary is an L dwarf or later.

Jacqueline Faherty↗
NASA NTRS2021

New Candidate Extreme T Subdwarfs from the Backyard Worlds: Planet 9 Citizen Science Project

Schneider et al. presented the discovery of WISEA J041451.67−585456.7 and WISEA J181006.18−101000.5, which appear to be the first examples of extreme T-type subdwarfs (esdTs; metallicity ≤−1 dex, T(eff) ≲ 1400 K). Here, we present new discoveries and follow-up of three T-type subdwarf candidates, with an eye toward expanding the sample of such objects with very low metallicity and extraordinarily high kinematics, properties that suggest membership in the Galactic halo. Keck/NIRES near-infrared spectroscopy of WISEA J155349.96+693355.2, a fast-moving object discovered by the Backyard Worlds: Planet 9 citizen science project, confirms that it is a mid-T subdwarf. With H(W2) = 22.3 mag, WISEA J155349.96+693355.2 has the largest W2 reduced proper motion among all spectroscopically confirmed L and T subdwarfs, suggesting that it may be kinematically extreme. Nevertheless, our modeling of the WISEA J155349.96+693355.2 near-infrared spectrum indicates that its metallicity is only mildly subsolar. In analyzing the J155349.96+693355.2 spectrum, we present a new grid of low-temperature, low-metallicity model atmosphere spectra. We also present the discoveries of two new esdT candidates, CWISE J073844.52−664334.6 and CWISE J221706.28−145437.6, based on their large motions and colors similar to those of the two known esdT objects. Finding more esdT examples is a critical step toward mapping out the spectral sequence and observational properties of this newly identified population.

Aaron M. Meisner↗
NASA NTRSDate not supplied

Hot on the Trail: Mapping Wildland Fires With Citizen Science and Live Image Processing

Heightened fire activity is straining existing firefighting resources and budgets across the nation and worldwide. For this reason, the 2021 NASA Academy at Langley Research Center has the challenge of applying NASA technology to the challenges faced by wild-land firefighting professionals. The approach is three-fold: Data Acquisition, Communications, and Equipment Development. The Communications Team focused on improving data latency for fire mapping and advancing communications between civilians and incident dispatch. The team developed two main concepts to address these areas: First, they tried to identify, map, and transmit fire perimeter data via an Uncrewed Aerial System (UAS), and second, they suggest a fully functioning web-app for early fire detection and fire monitoring using citizen science. This report outlines the materials and methods of these concepts, as well as future work necessary to bring the proposed technology into the field.

UAS data transfer ground stations↗