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NASA NTRSDate not supplied

Populating a Graph Database to Run a Usage-Based Discovery Tool

Most dataset discovery tools for Earth Observation data rely on descriptions and other metadata of the datasets, using keyword searches or attribute filtering to determine relevance. However, these descriptions often do not include the potential uses of the data. Thus, a user working on floods will rarely see few if any rainfall datasets show up in such a search. The Usage Based Discovery tool, on the other hand, offers usage instances to the user, either research articles or applications, along with the datasets that those usage instances used. This allows a user, particularly one new to the world of Earth Observation data, to investigate which datasets are used in similar cases. The information that powers Usage-Based Discovery is a graph database of relationships of usage to dataset and usage to topic, allowing the user to narrow their search for similar cases. In order to scale out to a graph database rich enough to provide a satisfactory user experience, we combine manual and automated processes to populate the graph. The initial content of the graph has been seeded primarily via human-aided data curation methods, using sites like Google Scholar. To scale up this effort, we’ve employed crowdsourcing. It is easy for anyone to contribute to our graph using their Open Researcher and Contributor Identifier for authorization. We’re now experimenting with Machine Learning and Natural Language Processing to help automate population of the graph, starting with the classification of research articles by topic. Finding adequate training data in the absence of a comprehensive and open research article API continues to be a significant challenge.

Vincent Inverso↗
NASA NTRS2002

Wire Detection Algorithms for Navigation

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. Two approaches were explored for this purpose. The first approach involved a technique for sub-pixel edge detection and subsequent post processing, in order to reduce the false alarms. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter. The second approach involved the use of an example-based learning scheme namely, Support Vector Machines. The purpose of this approach was to explore the feasibility of an example-based learning based approach for the task of detecting wires from their images. Support Vector Machines (SVMs) have emerged as a promising pattern classification tool and have been used in various applications. It was found that this approach is not suitable for very thin wires and of course, not suitable at all for sub-pixel thick wires. High dimensionality of the data as such does not present a major problem for SVMs. However it is desirable to have a large number of training examples especially for high dimensional data. The main difficulty in using SVMs (or any other example-based learning method) is the need for a very good set of positive and negative examples since the performance depends on the quality of the training set.

Kasturi, Rangachar↗
NASA NTRS2010

Fast Solution in Sparse LDA for Binary Classification

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Moghaddam, Baback↗
NASA NTRS1993

Automated Analysis of a Large-Scale Sky Survey: The SKICAT System

We describe the application of decision tree based classification techniques to the development of an automated tool for the reduction of a large scientific data set. The primary benefits of the SKICAT approach are increased data reduction throughput, repeatability, and consistency of classification.

data analysis image databases↗
NASA NTRS2018

A 3D Citizen Science Video Game for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network aimed at accurately assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. We present here the active learning component of the project, which consists of an interactive video game prototype for tablet and mobile devices where players are able to intuitively label morphology classifications over mm-scale 3D coral reef imagery. Active learning applications present a novel methodology for engaging the public while efficiently providing large-scale training and test data for increasingly complex and data-intensive machine learning algorithms. NeMO-Net trains players on domain-specific knowledge through interactive tutorials and periodically checks players' input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide personalized classification training. Players can rate the classifications of other players, unlock rewards and join a global community as they explore and classify coral reefs and other shallow marine environments.

Citizen Science↗
NASA NTRS2015

Application of Data Cubes for Improving Detection of Water Cycle Extreme Events

As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).

water cycle extreme events↗
NASA NTRS2023

Invasion in the Niger Delta: Remote Sensing of Mangrove Conversion to Invasive Nypa fruticans from 2015-2020

Invasive species are a leading threat to biodiversity worldwide. Nypa palm ( Nypa fruticans ) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines, and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbates mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyze multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015-2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel-1 SAR, Sentinel-2 MSI, and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel-1 were best suited for mapping Nypa extent with similar accuracies (78% and 75% respectively). Based on data availability and accuracy, we focused our change analysis on Sentinel-1 SAR. Our results show ~28,000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from GEDI (Global Ecosystem Dynamics Investigation) LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top-of-canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves.

GEE↗
NASA NTRSDate not supplied

The Radiation Biology Ontology: A New Tool Supporting FAIR Principles Across Radiation Biology Facilitating Data Discovery and Integration

Development of the Radiation Biology Ontology (RBO) was motivated by the need for a comprehensive, well-structured ontology for encoding radiation biology metadata. The primary use-cases were archiving data in the STORE database (https://www.storedb.org/), the repository for the RadoNorm Project, and in GeneLab (https://genelab.nasa.gov), NASA’s ‘omics database. The scope of radiobiology research ranges from physics to radiation oncology to socio-legal studies; no existing ontology has the necessary breadth or depth. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR radiation biology data.

ontology↗
NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRS2023

Data Sharing in Radiation Biology: Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally "Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗
NASA NTRSDate not supplied

Supervised Machine Learning Approach for Classifying Earth Science Publications

The data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding these collections' algorithms, their validation, and their applications. As NASA data centers collect these publications for public use, it is helpful to categorize them based on how they relate to their associated datasets. Specifically, whether the publication linked to the GES DISC dataset is using it for applicational research, describing the algorithm used for the dataset creation, validating the dataset, or providing a general overview of the data collection. Currently, this process requires simple manual labeling, and as such, it may be possible to solve via automation. To approach this problem, machine learning classifiers were developed to predict a publication's category. Manually labeled publications were used as the training data for the supervised machine learning algorithms, specifically Random Forest and Multinomial Naïve Bayes. After balancing the dataset and implementing the Multinomial Naïve Bayes algorithm, the classification accuracy achieved was substantially higher than the baseline accuracy, thus significantly improving the efficiency of publication labeling.

Rohan Dayal↗
NASA NTRSDate not supplied

Mapping National Forest Aboveground Biomass in Mexico By Integrating GEDI and Landsat Times Series Data

Mexico is one of the countries with great potential for the UN's Reducing Emissions from Deforestation and Forest Degradation (REDD+) program, a key nature-based solution for the forest sector. To monitor carbon stock changes, there is a growing demand for unbiased Monitoring Reporting Verification (MRV) systems to facilitate effective forest management and climate change mitigation strategies. Remote sensing-based national aboveground biomass density (AGBD) estimation over Mexico is scarce and often limited to one-time static mapping, leading to spatiotemporal inconsistency in inputs. As an effort under NASA's Carbon Monitoring System (CMS) program, we have developed a remote sensing-based approach to create consistent historical AGBD maps of Mexico using multi-stream remote sensing data, including spaceborne lidar GEDI and long-term Landsat time series, as well as topographic information. We employ the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat surface reflectance, followed by the inference of forest AGBD using a random forest machine learning algorithm with the temporal information of land surface dynamics extracted by the CCDC as input. GEDI provides unprecedented forest structure and AGBD sampling datasets for model training and validation practices. In this presentation, we share the progress made in developing a spatially explicit mapping of historical AGBD changes associated with land surface changes and post-disturbance landscapes.

Taejin Park↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
NASA NTRS1992

System diagnostic builder

The System Diagnostic Builder (SDB) is an automated software verification and validation tool using state-of-the-art Artificial Intelligence (AI) technologies. The SDB is used extensively by project BURKE at NASA-JSC as one component of a software re-engineering toolkit. The SDB is applicable to any government or commercial organization which performs verification and validation tasks. The SDB has an X-window interface, which allows the user to 'train' a set of rules for use in a rule-based evaluator. The interface has a window that allows the user to plot up to five data parameters (attributes) at a time. Using these plots and a mouse, the user can identify and classify a particular behavior of the subject software. Once the user has identified the general behavior patterns of the software, he can train a set of rules to represent his knowledge of that behavior. The training process builds rules and fuzzy sets to use in the evaluator. The fuzzy sets classify those data points not clearly identified as a particular classification. Once an initial set of rules is trained, each additional data set given to the SDB will be used by a machine learning mechanism to refine the rules and fuzzy sets. This is a passive process and, therefore, it does not require any additional operator time. The evaluation component of the SDB can be used to validate a single software system using some number of different data sets, such as a simulator. Moreover, it can be used to validate software systems which have been re-engineered from one language and design methodology to a totally new implementation.

Nieten, Joseph L.↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS2006

PixelLearn

PixelLearn is an integrated user-interface computer program for classifying pixels in scientific images. Heretofore, training a machine-learning algorithm to classify pixels in images has been tedious and difficult. PixelLearn provides a graphical user interface that makes it faster and more intuitive, leading to more interactive exploration of image data sets. PixelLearn also provides image-enhancement controls to make it easier to see subtle details in images. PixelLearn opens images or sets of images in a variety of common scientific file formats and enables the user to interact with several supervised or unsupervised machine-learning pixel-classifying algorithms while the user continues to browse through the images. The machinelearning algorithms in PixelLearn use advanced clustering and classification methods that enable accuracy much higher than is achievable by most other software previously available for this purpose. PixelLearn is written in portable C++ and runs natively on computers running Linux, Windows, or Mac OS X.

Mazzoni, Dominic↗
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↗