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DOE OSTI2025

Heliostat optical error inspection with polarimetric imaging drone

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

CSP Field↗

NASA NTRSDate not supplied

Flying Blind: Keeping aircraft safe without a pilot on board.

The unmanned aircraft market is one of the fastest growing sectors in the world today, poised to become a billion dollar industry in the next several years. This explosive growth in unmanned aircraft, both small and large, brings an increased risk of these vehicles interfering with current aircraft, or harming unsuspecting bystanders. This talk will discuss some of the research NASA is doing to keep the airspace safe, while allowing drone pilots the freedom to fly. The discussion will center on two NASA-developed systems: Safeguard, a platform-independent geofence; and DAIDALUS, a software suite for detect and avoid. In addition to describing what the systems are supposed to do, we'll also discuss how NASA uses formal methods to provide assurance that they actually do as intended.

unmanned aircraft↗

DOE OSTI2025

Optimizing Deep Learning Models for Climate-Related Natural Disaster Detection from UAV Images and Remote Sensing Data

This research study utilized artificial intelligence (AI) to detect natural disasters from aerial images. Flooding and desertification were two natural disasters taken into consideration. The Climate Change Dataset was created by compiling various open-access data sources. This dataset contains 6334 aerial images from UAV (unmanned aerial vehicles) images and satellite images. The Climate Change Dataset was then used to train Deep Learning (DL) models to identify natural disasters. Four different Machine Learning (ML) models were used: convolutional neural network (CNN), DenseNet201, VGG16, and ResNet50. These ML models were trained on our Climate Change Dataset so that their performance could be compared. DenseNet201 was chosen for optimization. All four ML models performed well. DenseNet201 and ResNet50 achieved the highest testing accuracies of 99.37% and 99.21%, respectively. This research project demonstrates the potential of AI to address environmental challenges, such as climate change-related natural disasters. This study’s approach is novel by creating a new dataset, optimizing an ML model, cross-validating, and presenting desertification as one of our natural disasters for DL detection. Three categories were used (Flooded, Desert, Neither). Our study relates to AI for Climate Change and Environmental Sustainability. Drone emergency response would be a practical application for our research project.

AI↗

NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

sensor fusion↗

NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

object tracking↗

DOE OSTI2025

Advancing Development of Emissions Detection (Final Report)

This document is the final report to the U.S. Department of Energy (DOE for contract DE-FE0031873) awarded to Colorado State University (CSU). CSU and partners at Harrisburg University of Science and Technology, University of Texas Arlington, and University of Texas at Austin organized several testing rounds to provide knowledge focused on advancing the detection capabilities of emissions monitoring devices. With this funding opportunity the research group began by establishing the Advancing the Development of Emission Detection (ADED) program, with the goal focused on enhancing the accuracy, reliability, and field applicability of methane detection technologies. This effort aimed to address the critical challenges of identifying and quantifying methane emissions while enabling industry stakeholders to meet regulatory compliance and environmental sustainability goals. The program engaged with industry, government, and technology stakeholders to promote adoption and consensus on testing techniques for methane detection solutions. Methane, a potent greenhouse gas, contributes significantly to global warming, and the oil and natural gas (O&G) sector is a primary source of methane emissions. Regulatory measures such as leak detection and repair (LDAR) programs have been implemented to address emissions. However, traditional LDAR approaches, reliant on handheld and component-level measurements, are resource-intensive. To address these limitations and move with evolving regulations, advanced methane technologies are emerging. These solutions include ground-based sensors, mobile systems (e.g., drones, vehicles, and aircraft), and satellite-based platforms. They offer innovative capabilities for autonomous monitoring, larger spatial coverage, and emission quantification using methods such as tracer gas techniques and inverse modeling with Gaussian plume analysis. The ADED program began with creating protocols for methane controlled release (CR) testing these continuous monitoring (CM) and survey technologies that detect and monitor methane emissions at O&G facilities. The protocols were then implemented throughout testing of CM and survey devices at CSU’s Methane Emissions Technology Evaluation Center (METEC) facility from 2021 through 2024. As apart of the protocol, solutions that tested under the ADED program installed their solutions at METEC, documented their system under test, and provided detection reports to the METEC team for analysis. The METEC team would provide the solutions with analyzed reports of their emissions and ground truth data of the releases conducted during their testing session. Under the ADED program, CMs were also tested at O&G facilities for a six week test run of challenge release (ChR) releases. The findings from the ADED program underscore the critical role of collaborative research and innovation in tackling methane emissions, offering a pathway for the oil and gas sector to achieve significant environmental and economic benefits. Results from METEC testing saw improvement of performance and accuracy across all solutions over the extent of the ADED experiments. The results also showed a variance in CM solution performance between CRs and ChRs. That variance pushed the team to further analyze the differences between CR testing environments and field conditions. With the drive from regulations and that variance in field conditions, the ADED team began designing a new CR testing protocol and additions to the METEC testing facility. The METEC team is furthering the progress made through the ADED program with awarded funding from DE-FE0032276. This funding pushes the development of METEC’s addition with new equipment, allowing for an updated facility layout. METEC still facilitates for traditional facilities, with a legacy pad, while expanding an new design based on how O&G infrastructure has Final Report - Contract Number: DE-FE0031873 been changed over the last decade. Throughout the ADED program the team has also been working with international partners to ensure staying in the trend globally. International partners have been essential in moving the new protocol forward to implement into CR testing at the METEC facility in Spring 2025.

42 ENGINEERING↗

NASA NTRSDate not supplied

Drone Applications for Wildfires and Other Emergency Situations

Potential Solutions to Wildfires This project is looking at multiple aspects of the use of UAV’s during wildfire season. This project identifies multiple solutions to communication problems, early action response, and innovative tactics to fight wildfires during the dark hours of the second shift. This project will identify the features and innovations needed to create a second shift that can more effectively fight wildland fires at night. In addition, this project will look to find which satellite systems work best for detection of wildfires, with a special focus on remote locations with more rural communities. There will also be a focus on ways to improve current UAVs and sensors for aerial fire surveillance as well as the understanding on how to establish a safe airspace for these UAVs during wildfires. More specifically, unmanned ariel vehicles (UAV) or drones can be used during natural disasters and other situations to help first responders be a more efficient team, ultimately saving hundreds of more lives. Depending on the situation, drones can be equipped with many different accessories, each with their own advantages. When it comes to operating a drone, communication between the first responder, dispatcher, and the person flying the drone is very important when exchanging useful information. A focus on existing systems, and what changes could be made to create more novel systems will also be shown. Moreover, wildfires not only pose a hazard to human safety and a dent on communication methods, but it also has detrimental effects on the environment. Specifically, the use of common fire retardants have dangerous health effects on human health and the environment as a whole. Therefore, this project will also explore the use of alternative eco-friendly fire retardants and recent innovations to the use of sustainable retardants.

Drones↗

NASA NTRSDate not supplied

Autonomous Spacecraft Inspection with Free-Flying Drones

This paper describes a proof-of-concept mission demonstrating a multi-agent system performing visual inspection of damage sustained by a spacecraft. Free-flying satellites, simulated by unmanned aerial vehicles (UAVs), autonomously fly around a mock space module maximizing the search space for damage detection. The free-flyers are responsible for independently coordinating their flights to avoid collision with the space module and each other, while executing mission tasks. Damage analysis on the surface of the mock space module is performed in real-time using video from each free-flyer. Three-dimensional modeling is deployed offline to supplement and improve damage detection. This approach demonstrates the feasibility of deploying real space systems for damage detection, where 2D analysis can quickly determine region of interest and 3D visualization can produce a human-navigable virtual environment with depth perspective for further investigation.

unmanned aerial vehicle (UAV)↗

NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗

DOE OSTI2024

Quantum Networks: A New Platform for Aerospace

The ability to distribute entanglement between quantum nodes may unlock new capabilities in the future that include teleporting information across multinode networks, higher resolution detection via entangled sensor arrays, and measurements beyond the quantum limit enabled by networked atomic clocks. These new quantum networks also hold promise for the Aerospace community in areas such as deep space exploration, improved satellite communication, and synchronizing drone swarms. Although exciting, these applications are a long way off from providing a “real-world” benefit, as they have only been theoretically explored or demonstrated in small-scale experiments. An outstanding challenge is to identify near-term use cases for quantum networks; this may be an intriguing new area of interest for the aerospace community, as the quantum networking field would benefit from more multidisciplinary collaborations. This paper introduces quantum networking, discusses the difficulties in distributing entanglement within these networks, highlights recent progress toward this endeavor, and features two current case studies on mobile quantum nodes and an entangled clock network, both of which are relevant to the aerospace community.

Engineering↗

DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗

DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Post-Severe Thunderstorm Damage Assessment from March 2-3, 2020, Nashville, TN, Using Synthetic Aperture Radar Observations

Severe weather events (e.g., hurricanes, tornadoes) are responsible for most weather-related infrastructure and building damages. The destruction caused by these events can cross several states, making damage estimates difficult, especially in heavily vegetated or rural areas. Drone or optical imagery is often relied upon in these cases but is limited by solar and atmospheric conditions. Synthetic Aperture Radar (SAR) is an active sensor allowing for day and night collections in all weather conditions. This research highlights the benefits and limitations of using SAR to detect tornado tracks and associated damages while also assessing the strengths and weaknesses of two SAR sensors with varying wavelengths and spatial resolutions from the March 2020 Tornado Outbreak. The outbreak occurred overnight on March 2-3, when several supercell thunderstorms tracked across multiple states producing numerous tornadoes (EF-0 through EF-4) and large hail. Most of the damage occurred in central Tennessee, resulting in 25 fatalities, hundreds of injuries, and over a billion dollars worth of damage. Publicly available C-band (~6 cm) imagery from the European Space Agency’s Sentinel-1 satellite and commercial X-band (~3 cm) imagery from Airbus’s TerraSAR-X satellite were used to generate amplitude and coherence products. The closest post-event collections were used for the amplitude products, and the closest pre- and post-collections were used to create the coherence pairs. If damage tracks were identified in any of the SAR products, the length (miles) and max width (yards) were recorded and compared to the National Weather Service Storm Data official records. SAR successfully detected five rated EF-1 or higher out of ten recorded tornadoes. However, none of the four EF-0 tornadoes were identified. X- and C-band SAR sensors are heavily impacted by dense vegetation, underestimating the extent of damage. Despite these limitations, SAR products can be beneficial when looking at severe weather impacts, especially during the winter and early spring when the coherence products are less influenced by vegetation. The upcoming L-band (~24 cm) NISAR mission will provide more accurate estimates of damage, with the longer wavelength, expanding the applications of SAR to assist in damage assessment caused by severe thunderstorms.

Hannah G Pankratz↗

NASA NTRS2022

Going to Extremes: Architecting Holographic Microscopes for Extreme Environments

Bacterial life exists on earth in extreme environments. These are environments described by large temperature excur- sions, large pressure excursions, and large radiation excursions from the nominal conditions near sea level which are largely populated by humans. These extreme locales represent such places as the ocean ice, the briny pools of Death Valley and deep mines, the acidic hot springs of the High Sierra or Yellowstone, or even the clouds of our upper atmosphere. The preponderance of bacterial life in these extreme environments here on earth suggests that bacterial life might likely exist in the extreme environments of our own solar system, such as the icy moons of Europa or Enceladus. Thus, architecting instruments for detect- ing life in these extreme environments on earth builds confidence that we can architect such instruments for flight missions. In this paper, we discuss our experience with designing digital holo- graphic microscope instruments to enable detection of bacteria in several extreme environments. In particular we discuss three different instruments. The first is our field instrument which is a small, portable instrument for examination of remote sites. The second is submersible instrument which enables exploration of deep aquatic environments and is deployed on a ocean-going drone. The third is a balloon-borne instrument to examine the bacterial content of the upper atmosphere. We will provide a review of each instrument and discuss aspects of instrument engineering for each particular application.

Ramirez, Alex↗

NASA NTRSDate not supplied

Demonstration of Lidar Sensors for Precision Safe Landing on Planetary Bodies

Missions to solar system bodies must meet increasingly ambitious objectives requiring highly reliable “precision landing”, and “hazard avoidance” capabilities. To meet these needs, we have developed two lidar sensor systems that can be used individually or in concert depending on the mission requirements. One is Navigation Doppler Lidar (NDL) capable of providing vehicle precision vector velocity and ground-relative altitude and the other is an Imaging Flash Lidar for Terrain Relative Navigation and Hazard Detection and Avoidance. Using both lidar sensors together will enable landing anywhere and under any lighting condition. The NDL performance has been extensively characterized through numerous ground and aircraft flight tests and will be soon demonstrated on two lunar landing missions (mid-2023). The Flash Lidar performance is currently being assessed onboard a drone and will soon be tested on helicopter and fixed-wing aircraft platforms. This paper describes both lidar sensors and their expected operation on landing vehicles.

Precision Landing↗

NASA NTRSDate not supplied

Ongoing Work: A Prototype Dataset for Low-flying Autonomous Medical UAS Operations

This paper presents ongoing work to create a dataset for low-flying autonomous medical UAS operations, focused on human stance recognition. This is an exploration of the viability of airborne classification for the Drone as a First Responder (DFR) concept in which a UAS arrives at the scene of an incident before emergency response personnel can get there and provides some level of situational awareness for the personnel arriving to the scene. Future incarnations could also see the UAS administer some level of care to injured parties at the scene. The data set, focused on detecting human stance, being developed here is the result of 30 test flights at NASA Langley Research Center in early 2024. In addition to flights where the participant (an anthropomorphic testing device or human) is alone in the viewing area holding a particular stance, two emergency scenes have been fabricated and collected through video - ``bike crash'' and ``difficult camping''. These test flights include four human participants. The contribution of this work upon completion will be a publicly available data set for the development of classification engines focused on human stance, and in the future, even triage.

Uncrewed Aerial Systems↗

NASA NTRSDate not supplied

Topographic Quintet: Comparing Five Methods for Measuring Ultra-High Resolution Topography

We compare different methods for collecting ultra-high resolution topography data within an analog planetary, human landing site scale area. Our aim is to investigate the cost and benefits of different 3D terrain mapping techniques, their associated data collection methods, and how their different specifications (e.g., range, spatial resolution, scanning-time, mobility, operating constraints, GPS-Denied operation, etc.) might be applied to landing-site characterization and mission operations. We compare 3D terrain data collected during a field campaign in November 2021 from an outcrop at Kilbourne Hole in southern New Mexico using different Light Detection and Ranging (LiDAR) sensors on the ground and stereo-derived 3D data from framing cameras mounted on small uncrewed aerial systems (sUAS).Our foci for this experiment are ground-based, surveying, and autonomous vehicle-type 3D scanning sensors that might be used for planetary surface exploration from landed assets (e.g., lander, rover, astronaut-mounted sensors, decent imaging, hoppers, or drones).[e.g. 1]This test is not meant to benchmark these scanners against one another, nor provide a recommendation for a specific make or model. Rather, our goal is to quantify time, effort, resolution, and operational trade-offs that are important for selecting a topographic instrument/methodology for a given scope of terrain characterization. Our results indicate that each technique is capable of exceptional quality terrain characterization for planetary exploration and scientific inquiry, but we hypothesize the appropriate technique is highly dependent on the scope of operational specifications and science requirements.

P Whelley↗

NASA NTRS2026

A Multi-Tier Autonomous Aerial Architecture for Wildfire Detection, Characterization, and Communication in Infrastructure-Denied Environments

Wildfire response depends on how fast an ignition can be confirmed and located, especially in remote regions where ground-based communication and monitoring may be limited. Geostationary sensors provide frequent observations but at kilometer-scale resolution, which is too coarse to resolve small fires in remote terrain. Ground camera networks require sightlines and infrastructure that back-country areas lack. To address these limitations, this work proposes a Multi-Tier Autonomous Wildfire Intelligence System that combines wide-area monitoring with targeted, high-resolution sensing. A solar-powered high-altitude long endurance (HALE) platform operating at approximately 60,000 ft provides persistent wide-area thermal and optical surveillance, running onboard edge inference to screen candidate ignitions and reduce false positives and downlink bandwidth. When a candidate ignition is detected, low-altitude uncrewed aircraft systems (UAS) can be deployed to conduct localized observations, including high-resolution imaging and atmospheric measurements such as wind and plume observation. By combining persistent detection with local sensing, the proposed architecture is designed to provide first responders with timely, high-resolution information about fire location and behavior to aid in emergency decision making.

Wildfire management, UAS, drones↗