The data here support the retrieval results presented in the submitted paper in IEEE Transactions on Geoscience and Remote Sensing, 'First TROPOMI Retrieval of Aerosol Effective Height using O4 Absorption Band at 477 nm and Aerosol Classification'.
The aerosol effective height (AEH) was retrieved from TROPOMI measurements based on the O4 absorption at 477 nm. The AEHs were retrieved over Northeast Asia, South Africa, and Sahara Desert during some case study periods.
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This dataset contains RF signals from drone remote controllers (RCs) of different makes and models. The RF signals transmitted by the drone RCs to communicate with the drones are intercepted and recorded by a passive RF surveillance system, which consists of a high-frequency oscilloscope, directional grid antenna, and low-noise power amplifier. The drones were idle during the data capture process. All the drone RCs transmit signals in the 2.4 GHz band. There are 17 drone RCs from eight different manufacturers and ~1000 RF signals per drone RC, each spanning a duration of 0.25 ms.
The dataset contains ~1000 RF signals in .mat format from the remote controllers (RCs) of the following drones:
- DJI (5): Inspire 1 Pro, Matrice 100, Matrice 600*, Phantom 4 Pro*, Phantom 3
- Spektrum (4): DX5e, DX6e, DX6i, JR X9303
- Futaba (1): T8FG
- Graupner (1): MC32
- HobbyKing (1): HK-T6A
- FlySky (1): FS-T6
- Turnigy (1): 9X
- Jeti Duplex (1): DC-16.
In the dataset, there are two pairs of RCs for the drones indicated by an asterisk above, making a total of 17 drone RCs. Each RF signal contains 5 million samples and spans a time period of 0.25 ms.
The scripts provided with the dataset defines a class to create drone RC objects and creates a database of objects as well as a database in table format with all the available information, such as make, model, raw RF signal, sampling frequency, etc. The scripts also include functions to visualize data and extract a few example features from the raw RF signal (e.g., transient signal start point). Instructions for using the scripts are included at the top of each script and can also be viewed by typing help scriptName in MATLAB command window.
The drone RC RF dataset was used in the following papers:
- M. Ezuma, F. Erden, C. Kumar, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using machine learning techniques," in Proc. IEEE Aerosp. Conf., Big Sky, MT, Mar. 2019, pp. 1-13.
- M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Detection and classification of UAVs using RF fingerprints in the presence of Wi-Fi and Bluetooth interference," IEEE Open J. Commun. Soc., vol. 1, no. 1, pp. 60-79, Nov. 2019.
- E. Ozturk, F. Erden, and I. Guvenc, "RF-based low-SNR classification of UAVs using convolutional neural networks." arXiv preprint arXiv:2009.05519, Sept. 2020.
Other details regarding the dataset and data collection and processing can be found in the above papers and attached documentation.
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Author Contributions:
- Experiment design: O. Ozdemir and M. Ezuma
- Data collection: M. Ezuma
- Scripts: F. Erden and C. K. Anjinappa
- Documentation: F. Erden
- Supervision, revision, and funding: I. Guvenc
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Acknowledgment
This work was supported in part by NASA through the Federal Award under Grant NNX17AJ94A.
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Wildfires are one of the deadliest and dangerous natural disasters in the world. Wildfires burn millions of forests and they put many lives of humans and animals in danger. Predicting fire behavior can help firefighters to have better fire management and scheduling for future incidents and also it reduces the life risks for the firefighters. Recent advance in aerial images shows that they can be beneficial in wildfire studies. Among different methods and technologies for aerial images, Unmanned Aerial Vehicles (UAVs) and drones are beneficial to collect information regarding the fire.
The aerial pile fire detection dataset consists of different repositories. The first one is a raw video recorded using the Zenmuse X4S camera. The format of this file is MP4. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 1.2 GB. The first video was used for the "Fire-vs-NoFire" image classification problem (training/validation dataset). The second one is a raw video recorded using the Zenmuse X4S camera. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 503 MB. This video shows the behavior of one pile from the start of burning. The resolution of these two videos is 1280x720.
The third video is 89 seconds of heatmap footage of WhiteHot from the thermal camera. The size of this repository is 45 MB. The fourth one is 305 seconds of GreentHot heatmap with a size of 153 MB. The fifth repository is 25 mins of fusion heatmap with a size of 2.83 GB. All these three thermal videos are recorded by the FLIR Vue Pro R thermal camera with an FPS of 30 and a resolution of 640x512. The format of all these videos is MOV.
The sixth video is 17 mins long from the DJI Phantom 3 camera. This footage is used for the purpose of the "Fire-vs-NoFire" image classification problem (test dataset). The FPS is 30, the size is 32 GB, the resolution is 3840x2160, and the format is MOV.
The seventh repository is 39,375 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Training/Validation dataset). The size of this repository is 1.3 GB and the format is JPEG.
The eighth repository is 8,617 frames that resized to 254x254 for the "Fire-vs-NoFire" image classification problem (Test dataset). The size of this repository is 301 MB and the format is JPEG.
The ninth repository is 2,003 fire frames with a resolution of 3480x2160 for the fire segmentation problem (Train/Val/Test dataset). The size of this repository is 5.3 GB and the format is JPEG.
The last repository is 2,003 ground truth mask frames regarding the fire segmentation problem. The resolution of each mask is 3480x2160. The size of this repository is 23.4 MB.
For more information please find the Table in
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from period 01/07/2020 to 03/11/2020.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.
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Robotic Vision
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This dataset contains multispectral high resolution 1627 image patches of size 10 x 10 pixels with each pixel size of 10mx10m. These patches are generated from the Sentinel-2 (A/B) satellite images acquired during the period of October 2018 to May 2019. It covered one life cycle (12 months) of the sugarcane crop in the region of the Karnataka, India. Many parameters like plantation season, soil type, plantation type, crop variety and irrigation type that affects the growth of the sugarcane crop are considered while generating the samples.
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