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== UAS-TALLGRASS-PRESCIBED-FIRE-2019-Data-Set== | == 1. UAS-TALLGRASS-PRESCIBED-FIRE-2019-NIR-Data-Set== | ||
The UAS-TALLGRASS-PRESCIBED-FIRE-2019- NIR Data-Set was collected using KHawk Thermal-55 UAS on Oct. 8th 2019 at the KUFS Anderson County Prairie Preserve (38.182073, -95.275708, close to Welda KS). Fire Perimeters are extracted from Registered Multitemporal NIR Orthomosacis at the following time: | |||
FirePerimeter_1 12:07:03-12:09:19 PM | |||
FirePerimeter_2 12:12:41-12:15:03 PM | |||
FirePerimeter_3 12:15:26-12:17:47 PM | |||
UAS sensing payload: Modified GoPro Hero 4 Black NIR Camera. | |||
Vegetation: C4 tallgrasses and a mixture of herbaceous forbs and legumes. | |||
Weather: temperature at 73 deg. F, humidity at 41%, wind velocity (2 m) at 6.26 m/s mostly from the South. | |||
Terrain: mostly flat. | |||
Fire setting pattern: ring fire with two groups starting the fire from the center of the north boundary and ending the firing at about the center of the south boundary. | |||
== 2. UAS-TALLGRASS-PRESCIBED-FIRE-2019-Thermal-Data-Set== | |||
=== Introduction=== | === Introduction=== | ||
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=== Paper=== | === Paper=== | ||
Details of the data collection procedure can be found in our paper | Details of the data collection procedure can be found in our paper on International Journal of Remote Sensing. | ||
Saket Gowravaram, Haiyang Chao, Tiebiao Zhao, Sheena Parsons, Xiaolin Hu, Ming Xin, Harold Flanagan, and Pengzhi Tian, Prescribed Grass Fire Evolution Mapping and Rate of Spread Measurement Using Orthorectified Thermal Imagery from a Fixed-Wing UAS, International Journal of Remote Sensing (IJRS), DOI: 10.1080/01431161.2022.2044538, 2022 | |||
== UAS-GPS-Inertial-OpticalFlow-DataSet== | == 3. UAS-GPS-Inertial-OpticalFlow-DataSet== | ||
=== Introduction=== | === Introduction=== | ||
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Revision as of 18:20, 5 December 2022
1. UAS-TALLGRASS-PRESCIBED-FIRE-2019-NIR-Data-Set
The UAS-TALLGRASS-PRESCIBED-FIRE-2019- NIR Data-Set was collected using KHawk Thermal-55 UAS on Oct. 8th 2019 at the KUFS Anderson County Prairie Preserve (38.182073, -95.275708, close to Welda KS). Fire Perimeters are extracted from Registered Multitemporal NIR Orthomosacis at the following time:
FirePerimeter_1 12:07:03-12:09:19 PM FirePerimeter_2 12:12:41-12:15:03 PM FirePerimeter_3 12:15:26-12:17:47 PM
UAS sensing payload: Modified GoPro Hero 4 Black NIR Camera. Vegetation: C4 tallgrasses and a mixture of herbaceous forbs and legumes. Weather: temperature at 73 deg. F, humidity at 41%, wind velocity (2 m) at 6.26 m/s mostly from the South. Terrain: mostly flat. Fire setting pattern: ring fire with two groups starting the fire from the center of the north boundary and ending the firing at about the center of the south boundary.
2. UAS-TALLGRASS-PRESCIBED-FIRE-2019-Thermal-Data-Set
Introduction
The UAS-TALLGRASS-PRESCIBED-FIRE-2019-Data-Set was collected using KHawk Thermal-55 UAS on Oct. 8th 2019 at the KUFS Anderson County Prairie Preserve (38.182073, -95.275708, close to Welda KS). Fire Perimeters are extracted from Registered Multitemporal Orthomosacis at the following time: FirePerimeter_1 12:06:50-12:09:18 PM FirePerimeter_2 12:09:34-12:10:44 PM FirePerimeter_3 12:12:41-12:15:02 PM FirePerimeter_4 12:15:27-12:17:47 PM UAS sensing payload: FLIR Vue Pro R thermal camera. Vegetation: C4 tallgrasses and a mixture of herbaceous forbs and legumes. Weather: temperature at 73 deg. F, humidity at 41%, wind velocity (2 m) at 6.26 m/s mostly from the South. Terrain: mostly flat. Fire setting pattern: ring fire with two groups starting the fire from the center of the north boundary and ending the firing at about the center of the south boundary. |
Dataset |
|
Paper
Details of the data collection procedure can be found in our paper on International Journal of Remote Sensing.
Saket Gowravaram, Haiyang Chao, Tiebiao Zhao, Sheena Parsons, Xiaolin Hu, Ming Xin, Harold Flanagan, and Pengzhi Tian, Prescribed Grass Fire Evolution Mapping and Rate of Spread Measurement Using Orthorectified Thermal Imagery from a Fixed-Wing UAS, International Journal of Remote Sensing (IJRS), DOI: 10.1080/01431161.2022.2044538, 2022
3. UAS-GPS-Inertial-OpticalFlow-DataSet
Introduction
A brief introduction of the WVU-KU-RED-OF-DataSet1, which is collected using WVU Phastball UAV platform for vision-aided inertial navigation researches. The details of the data collection can be found in the authors' two papers ACC 2013 (a comparison study of optical flow and traditional sensors in UAV navigation) and JAIS 2016 (Flight-Test Evaluation of Navigation Information in Wide-Field Optical Flow, AIAA Journal of Aerospace Information Systems, Vol. 13, No. 11, pp.419-432). Please reference our papers if you use our data. The overall file structure for each subfolder can be summarized as the following: |
|
Dataset
video
Video-Air-Red-05272011.avi, an aerial video file collected by Phastball UAV; |
GPSINSLog
Video-Air-Red-05272011.avi, the aerial video file collected by Phastball UAV; |
Matlab
basic MATLAB script to show how to extract frames from video and how to plot the telemetry; |
https://www.dropbox.com/s/1nyn6rk93js7re4/Video-Air-Red-05272011.avi?dl=0 |
https://www.dropbox.com/s/flbdo2eeygd5q33/GPSINSRange_30Hz.mat?dl=0 |
https://www.dropbox.com/s/mwuaz2znrgqrtr7/plot_frame.m?dl=0 https://www.dropbox.com/s/wom3c55fl6w9pcz/Plot_nav_sensors.m?dl=0 |
CamCalib
The GoPro camera is calibrated using Camera calibration toolbox for MATLAB(Link)
The calibration file can be downloaded using the following link: https://www.dropbox.com/s/xg51m1093ztz77y/GoProHero_r5_Calib.mat?dl=0 |
Paper
https://www.dropbox.com/sh/jnpqepgcf8n3b1n/AABZnRBNX_yS7X3wi5NT20wca?dl=0