Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

The DREAM (Data Rang or EArth Monitoring): a multimode database including optics, radar, DEM and OSM labels for deep machine learning purposes.

DREAM, is a multimodal remote sensing database, developed from open-source data.

The database has been created using the Google Earth Engine platform, the GDAL python library; the “pyosm” python package developed by Alexandre Mayerowitz (Airbus, France) It includes two subsets:

France  on a 10mx10 m UTM Grid:

Instructions: 

The two datasets are stored in two separate zip files :

-USA.zip and France.zip. After decompression, each directory contain different sub directories with different areas. Each available tile is a 1024x1024 tile GeoTiffs format.

In France:

CoupleZZ_S2_date1_date2_XX_YY, Uint16 GeoTiff, UTM, RGBCoupleZZ_SRTM_V2_XX_YY Int16 GeoTiffCoupleZZ_S1_date2_date1_XX_YY  Flot32 GeoTiff 2 bands, Red:VV, Green: HVCoupleZZ_S1moy_date2__dual_XX_YY Float32 GeoTiff 2 bands, Red:VV, Green: HVCoupleZZ_OSMraster_XX_YY  Uint8 3 bands RGB GeoTIff

 

 

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Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spec- troscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments.

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103 Views

As part of the 2018 IEEE GRSS Data Fusion Contest, the Hyperspectral Image Analysis Laboratory and the National Center for Airborne Laser Mapping (NCALM) at the University of Houston are pleased to release a unique multi-sensor optical geospatial representing challenging urban land-cover land-use classification task. The data were acquired by NCALM over the University of Houston campus and its neighborhood on February 16, 2017 between 16:31 and 18:18 GMT.

Instructions: 

Data files, as well as training and testing ground truth are provided in the enclosed zip file.

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184 Views

BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.

Instructions: 

If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.

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202 Views

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. 

Instructions: 

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, and in part by NSF under CNS-1939334 (AERPAW, one of NSF's Platforms for Advanced Wireless Research (PAWR) projects).

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2264 Views

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.

Instructions: 

The aerial pile burn 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.

The preprint article of this dataset is available here:

https://arxiv.org/pdf/2012.14036.pdf

For more information please find the Table at: 

https://github.com/AlirezaShamsoshoara/Fire-Detection-UAV-Aerial-Image-Classification-Segmentation-UnmannedAerialVehicle

To find other projects and articles in our group:

https://www.cefns.nau.edu/~fa334/

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2761 Views

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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153 Views

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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123 Views

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