The Experts below are selected from a list of 3084 Experts worldwide ranked by ideXlab platform
M Sturm - One of the best experts on this subject based on the ideXlab platform.
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mapping snow depth from Manned Aircraft on landscape scales at centimeter resolution using structure from motion photogrammetry
The Cryosphere, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from Manned Aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of Aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (
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Mapping snow-depth from Manned-Aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry
The Cryosphere Discussions, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from Manned Aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of Aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.
Matt Nolan - One of the best experts on this subject based on the ideXlab platform.
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mapping snow depth from Manned Aircraft on landscape scales at centimeter resolution using structure from motion photogrammetry
The Cryosphere, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from Manned Aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of Aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (
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Mapping snow-depth from Manned-Aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry
The Cryosphere Discussions, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from Manned Aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of Aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.
Andrew D Zeitlin - One of the best experts on this subject based on the ideXlab platform.
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Technology Milestones – Detect , Sense & Avoid for UnManned Aircraft Systems
AIAA Infotech@Aerospace 2007 Conference, 2007Co-Authors: Andrew D ZeitlinAbstract:Collision avoidance represents an essential step towards operating UnManned Aircraft Systems in civil airspace. These systems have broad scope, and yet the necessary sensors and algorithms have not been thoroughly evaluated. An extension of modeling and simulation is recommended, along the path used for Manned Aircraft collision avoidance. The paper also discusses safety evaluations and the work of a standards committee.
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Technology milestones-detect, sense & avoid for unManned Aircraft systems
AIAA Infotech@ Aerospace Conference and Exhibit, 2007Co-Authors: Andrew D ZeitlinAbstract:Collision avoidance represents an essential step towards operating UnManned Aircraft Systems in civil airspace. These systems have broad scope, and yet the necessary sensors and algorithms have not been thoroughly evaluated. An extension of modeling and simulation is recommended, along the path used for Manned Aircraft collision avoidance. The paper also discusses safety evaluations and the work of a standards committee.
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IMPROVING THE RESOLUTION ADVISORY REVERSAL LOGIC OF THE TRAFFIC ALERT AND COLLISION AVOIDANCE SYSTEM
2006 IEEE Systems and Information Engineering Design Symposium, 2006Co-Authors: Andrew D Zeitlin, Thierry Arino, James K. KucharAbstract:Lack of safety and regulatory framework currently prevent the routine use of unManned Aircraft systems (UAS) within the U.S National Airspace System (NAS). Demonstrating a level of safety equivalent to that of Manned Aircraft will allow UAS to fly and interoperate in civil airspace. An unManned aerial collision avoidance system (UCAS) designed to communicate and interact with the traffic alert collision avoidance system (TCAS) implemented on Manned Aircraft is proposed. Considering intruding Aircraft equipped with TCAS as cooperating Aircraft, UCAS will also be able to sense, detect, and avoid non-cooperative Aircraft through the use of sensor technology. Simulation and analysis has been carried out to generate a safety metric quantifying the safety of the UCAS system. A Monte Carlo simulation has been performed for a set of outer loop state variables to generate the probability of a near midair collision. A second encounter model is carried out to show the benefits of incorporating the mitigation strategy selected, in this case the collision avoidance capabilities. An existing case study is analyzed to demonstrate the value of the model and the efficiency of the system
Christopher F Larsen - One of the best experts on this subject based on the ideXlab platform.
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mapping snow depth from Manned Aircraft on landscape scales at centimeter resolution using structure from motion photogrammetry
The Cryosphere, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from Manned Aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of Aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (
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Mapping snow-depth from Manned-Aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry
The Cryosphere Discussions, 2015Co-Authors: Matt Nolan, Christopher F Larsen, M SturmAbstract:Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from Manned Aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of Aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.
Kevin W Williams - One of the best experts on this subject based on the ideXlab platform.
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An Investigation of Sensory Information, Levels of Automation, and Piloting Experience on UnManned Aircraft Pilot Performance
2012Co-Authors: Kevin W WilliamsAbstract:Abstract : The current experiment was intended to examine the effect of sensory information on pilot reactions to system failures within a UAS control station simulation. This research also investigated the level of automation used in controlling the Aircraft and the level of Manned flight experience of the participants, since these also have been shown to influence pilot effectiveness. While the presence of sound did improve responses to engine failures, it did not improve responses to failures in heading control. The prediction that higher levels of automation would lead to complacency or vigilance decrements was not supported. The finding that pilots, in the manual conditions, flew significantly closer to the flight path than non-pilots was unexpected. The results suggest differences between those with Manned Aircraft experience and those without, but it is unclear whether these differences are due to Manned Aircraft training and flight experience or whether other factors, such as personality, may be evident.
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Documentation of Sensory Information in the Operation of UnManned Aircraft Systems
2008Co-Authors: Kevin W WilliamsAbstract:Abstract : For Manned Aircraft, the presence of multi-sensory inputs is a given. Pilots of Manned Aircraft might not even be aware of the availability of several different types of sensory inputs occurring at the same time. However, it is likely that each type of input has a reinforcing effect on the others that allows for a rapid diagnosis and response of both normal and unusual events in the cockpit. The situation for the pilot of an UnManned Aircraft System (UAS) is much different. UAS pilots receive information regarding the state and health of their Aircraft solely through electronic displays. This report includes a comparison of Manned sensory information to sensory information available to the unManned Aircraft pilot, a review of remediations for sensory deficiencies from the current UAS inventory, a review of human factors research related to enhancing sensory information available to the UAS pilot, and a review of current FAA regulations related to sensory information requirements. Analyses demonstrated that UAS pilots receive less and fewer types of sensory information, compared with Manned Aircraft pilots. One consequence is the enhanced difficulty for UAS pilots to recognize and diagnose anomalous flight events that could endanger the safety of the flight. Recommendations include the incorporation of multi- sensory alert and warning systems into UAS control stations.