Sensor Data Management & Streaming Data Analysis

Land cover classification analysis from satellite imagery is important for monitoring change in ecosystems and urban growth over time. However, the land cover classifications that are widely available in the United States are generated at a low spatial and temporal resolution, so that the spatial distribution between vegetation and urban areas in the wildland urban interface is difficult to measure. High spatial and temporal resolution analysis is essential for understanding and managing changing environments in these regions.

In summer 2017, close to one million Rohingya, an ethnic minority group in Myanmar, have fled to Bangladesh due to the persecution of Muslims. This large influx of refugees has resided around existing refugee camps. Because of this dramatic expansion, the newly established Kutupalong-Balukhali expansion site lacked basic infrastructure and public service.

Discovering the Bayesian network (BN) structure from big datasets containing rich causal relationships is becoming increasingly valuable for modeling and reasoning under uncertainties in many areas with big data gathered from sensors due to high volume and fast veracity. Most of the current BN structure learning algorithms have shortcomings facing big data. First, learning a BN structure from the entire big dataset is an expensive task which often ends in failure due to memory constraints.

This talk will review some of our recent work on building this dynamic data driven cyberinfrastructure and impactful application solution architectures that showcase integration of a variety of existing technologies and collaborative expertise. The lessons learned from the development of the NSF WIFIRE cyberinfrastructure will be summarized. Open data issues, use of edge and cloud computing on top of high-speed network, reproducibility through containerization and automated workflow provenance will also be discussed in the context of WIFIRE.

Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific information and resources in a way compliant with the FAIR principles and sustain the development of key infrastructure and tools. This paper provides an account of the challenges, experiences and solutions involved in the adoption of FAIR around research objects over several Earth Science disciplines.

Through the benefits of machine learning, we could quantify an increase in built-up area from 0.4 km in January 2016 to 9.5 km in February 2018 replacing primarily shrub and farmland. We are further able to detect a densification and consequently 'browning' of the refugee camp over time and display its heterogeneous structure. The developed method is scalable, and applicable to rapidly expanding settlements across various regions.

Wildland fires and related hazards are increasing globally. A common observation across these large events is that fire behavior is changing to be more destructive, making applied fire research more important and time critical. Significant improvements towards modeling of the extent and dynamics of evolving plethora of fire related environmental hazards, and their socio-economic and human impacts can be made through intelligent integration of modern data and computing technologies with techniques for data management, machine learning and fire modeling.

We propose incorporating both spatial and temporal information via a combined CNN-LSTM classification model. We theorize that the inclusion of temporal information may reduce the number of false positives and improve generalizability to new environments. The model is trained and tested on images of landscapes with and without smoke from the HPWREN tower network in southern California, part of the SAGE remote-sensing infrastructure. We use traditional CNN-based classifiers leveraged in past smoke detection literature as baselines to evaluate our model's performance.

A key takeaway from the COVID-19 crisis is the need for scalable methods and systems for ingestion of big data related to the disease, such as models of the virus, health surveys, and social data, and the ability to integrate and analyze the ingested data rapidly. One specific example is the use of the Internet of Things and wearables (i.e., the Oura ring) to collect large-scale individualized data (e.g., temperature and heart rate) continuously and to create personalized baselines for detection of disease symptoms.

This paper overviews the enablers and phases for translational cyberinfrastructure for data-driven applications. In particular, it summarizes the translational process of and the lessons learned from the development of the NSF WIFIRE cyberinfrastructure. WIFIRE is an end-to-end cyberinfrastructure for real-time data fusion and data-driven simulation, prediction, and visualization of wildfire behavior. WIFIRE’s real-time data products and modeling services are routinely accessed by fire research and emergency response communities for modeling as well as the public for situational awareness.

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level in addition to the conventional large-capacity supercomputing approaches. The latest distributed architectures built around the composability of data-centric applications led to the emergence of a new ecosystem for container coordination and integration.

In this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real-world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT).

The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented.

This presentation discusses the process of delivering fire behavior forecasts on initial attack using earliest detections of fire from geostationary satellite data. The current GOES 16 and 17 satellites deliver rapid detections and the future GeoXO will increase the speed and accuracy of the earliest alerts. GeoXO will also deliver important information such as the radiative power of the fire detected, providing insight into the fire intensity.

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines.

Timely prediction of debris flow probabilities in areas impacted by wildfires is crucial to mitigate public exposure to this hazard during post-fire rainstorms. This paper presents a machine learning approach to amend an existing dataset of post-fire debris flow events with additional features reflecting existing vegetation type and geology, and train traditional and deep learning methods on a randomly selected subset of the data.

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios.

This paper shows that the prediction capability of wildfire progression can be improved by estimation of a single prevailing wind vector parametrized by a wind speed and a wind direction to drive a wildfire simulation created by FARSITE. Estimations of these wind vectors are achieved in this work by a gradient-free optimization via a grid search that compares wildfire model simulations with measured wildfire perimeters, where noisy observations are modeled as uncertainties on the locations of the vertices of the measured wildfire perimeters.

Early detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease.

WIFIRE Lab, from the University of California, San Diego, is among the first Pathfinders supporting the next generation of geostationary observations, GeoXO. NOAA plans for the Geostationary and Extended Orbits (GeoXO) Program to follow the Geostationary Operational Environmental Satellites (GOES) – R Series and Space Weather Follow-On (SWFO) missions in the 2030-2050 timeframe. This presentation will focus on the NOAA’s Pathfinder, WIFIRE lab, who supported the development of the synthetic data and exercise scenario as part of the predevelopment user engagement with GeoXO.

This presentation will present on how the NOAA Pathfinder value-chains aim to increase awareness to NOAA missions, products and services so that NESDIS can deliver maximum value to their users. This presentation will show two value chains (fire and oceans) that demonstrate how the Pathfinder value chains are used as a mechanism for incorporating user input into the development of the NOAA satellite lifecycle. This talk will also serve as an opportunity to recruit future NOAA Pathfinders.

Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine-scale models and do not capture variation in live fuel (i.e.