Accuracy of Unmanned Aerial System (Drone) Height Measurements

Vertical height estimates of earth surface features using an Unmanned Aerial System (UAS) are important in natural resource management quantitative assessments. An important research question concerns both the accuracy and precision of vertical height estimates acquired with a UAS and to determine if it is necessary to land a UAS between individual height measurements or if GPS derived height versus barometric pressure derived height while using a DJI Phantom 3 would affect height accuracy and precision. To examine this question, height along a telescopic height pole on the campus of Stephen F. Austin State University (SFASU) were estimated at 2, 5, 10 and 15 meters above ground using a DJI Phantom 3 UAS. The DJI Phantom 3 UAS (i.e., drone) was flown up and down the telescopic height pole to estimate height at the 2, 5, 10 and 15 meter locations using four different user controlled flight modes with a total of 30 observations per flight mode. Flight mode configurations consisted of having GPS estimate height while landing the drone between flights, non-GPS mode to estimate height via barometric pressure while landing the drone between flights, flying continuously up and down the height pole while estimating height with GPS on, and flying continuously up and down the height pole in non-GPS mode to estimate height via barometric pressure. A total of 480 height measurements were recorded (30 measurements per height interval per all four flight mode combinations). Standard deviation results indicated that height measurements taken with the drone were less precise when landing was not reset between measurements. Root mean square error (RMSE) analysis indicated that having the landing reset without GPS on achieved the highest accuracy of all measurements taken. An ANOVA conducted on the absolute errors reconfirmed that having the landing reset before each height measurement using the drone achieved higher accuracy compared to flying the drone continuously. This indicates the practical application of height measurement of the DJI Phantom 3 UAS and the importance of resetting the UAS before each height measurement

Accuracy of Unmanned Aerial System (Drone) Height Measurements

Vertical height estimates of earth surface features using an Unmanned Aerial System (UAS) are important in natural resource management quantitative assessments. An important research question concerns both the accuracy and precision of vertical height estimates acquired with a UAS and to determine if it is necessary to land a UAS between individual height measurements or if GPS derived height versus barometric pressure derived height while using a DJI Phantom 3 would affect height accuracy and precision. To examine this question, height along a telescopic height pole on the campus of Stephen F. Austin State University (SFASU) were estimated at 2, 5, 10 and 15 meters above ground using a DJI Phantom 3 UAS. The DJI Phantom 3 UAS (i.e., drone) was flown up and down the telescopic height pole to estimate height at the 2, 5, 10 and 15 meter locations using four different user controlled flight modes with a total of 30 observations per flight mode. Flight mode configurations consisted of having GPS estimate height while landing the drone between flights, non-GPS mode to estimate height via barometric pressure while landing the drone between flights, flying continuously up and down the height pole while estimating height with GPS on, and flying continuously up and down the height pole in non-GPS mode to estimate height via barometric pressure. A total of 480 height measurements were recorded (30 measurements per height interval per all four flight mode combinations). Standard deviation results indicated that height measurements taken with the drone were less precise when landing was not reset between measurements. Root mean square error (RMSE) analysis indicated that having the landing reset without GPS on achieved the highest accuracy of all measurements taken. An ANOVA conducted on the absolute errors reconfirmed that having the landing reset before each height measurement using the drone achieved higher accuracy compared to flying the drone continuously. This indicates the practical application of height measurement of the DJI Phantom 3 UAS and the importance of resetting the UAS before each height measurement

Integrating Faculty Led Service Learning Training to Quantify Height of Natural Resources from a Spatial Science Perspective

Arthur Temple College of Forestry and Agriculture (ATCOFA) faculty members were trained how to integrate service learning activities within senior level classes at Stephen F. Austin State University (SFASU) in Nacogdoches, Texas. The service learning training, taught under the acronym Mentored Undergraduate Scholarship (MUGS), involved meeting with fellow faculty members over the course of an academic year during the fall semester to first learn how to incorporate service learning activities in a senior level class followed by its incorporation into a class the following spring semester. The service learning model was applied to students in GIS 420, a senior level Landscape Modeling, Spatial Analysis, and Quantitative Assessment course within ATCOFA. The students were instructed within a hands-on interactive environment on how to use geospatial analysis to quantify natural resources. The overall goal was for a student to demonstrate proficiency in understanding how to apply aerial photo interpretation, satellite remote sensing, global positioning system and geographic information systems technology to quantify, qualify, map, monitor and manage natural and environmental resources at the local and landscape scales. Students applied this concept within a quantitative resource assessment, whereby students compared the conventional methodology of measuring height of vertical features within a landscape using a clinometer with the newer ways of measuring height using Pictometry hyperspatial imagery and drone acquired digital imagery. Conventional results were compared to newer technological methodologies to determine the most efficient and accurate way to quantify vertical resources from a spatial perspective

Online Estimation of Particle Track Parameters based on Neural Networks for the Belle II Trigger System

The Belle II particle accelerator experiment is experiencing substantial background from outside of the interaction point. To avoid taking data representing this background, track parameters are estimated within the pipelined and dead time-free level 1 trigger system of the experiment and used to suppress such events. The estimation of a particle track\u27s origin with respect to the z-Axis, which is along the beamline, is performed by the neural z-Vertex trigger. This system is estimating the origin or z-Vertex using a trained multilayer perceptron, leveraging the advantages of training to current circumstances of operation. In order fulfil the requirements set by the overall trigger system it has to provide the estimation within an overall latency of 5 us while matching a refresh rate of up to 31.75 for new track estimations. The focus of this contribution is this system\u27 current status. For this both implementation and integration into the level 1 trigger will be presented, supported by first data taken during operation as well as figures of merit such as latency and resource consumption. In addition its upgrade plan for the near future will be presented. The center of these is a Hough based track finding approach that uses Bayes theorem for training the weighting of track candidates. Characteristics of this system\u27s current prototypical implementation on FPGAs as well as present plants towards integration for future operation will be presented

Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images

The analysis of natural disasters in a timely manner often suffers from limited sensor data. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called “Volunteered Geographic Information (VGI)”. To save the analyst from manual inspection of all images posted online, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 results from 55% to 87% after 5 rounds of feedback

Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle~II central drift chamber using Field-Programmable Gate Arrays~(FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle~II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated pre-processing of graphs, we enable the deployment of novel Graph Neural Networks~(GNNs) in first level triggers of particle physics experiments.Comment: 18 page

The Event Timing Finder for the Central Drift Chamber Level-1 Trigger at the Belle II experiment

The level-1 trigger system of the Belle II experiment is designed to select physics events of interest with almost 100% efficiency for hadronic events. In terms of event timing decision, the level-1 trigger is required to have an accuracy of less than 10 ns. The Central Drift Chamber (CDC) level-1 trigger provides the event timing information as one of the level-1 timing sources. We developed the new algorithm to measure the event timing with an accuracy of about 10 ns based on the CDC hit timing. Two-dimensional charged track reconstruction by Hough transformation was utilized to reduce high background hits. We used a new-developed general-purpose FPGA board (Universal Trigger board 4) for this module for the first time. We report the performance of the new algorithm using e$^+$e$^−$ collision data collected in 2020