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:


The two datasets are stored in two separate zip files : and 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




Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot


Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.


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.  


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 



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).


Morse code is a system of communication using dots and dashes to represent numbers, letters and symbols. For example, the letter 'B' is represented as a dash followed by 3 dots, i.e. "–...". The dataset used in this competition is synthetically generated, and mimics a human writing dots and dashes on a piece of paper. In this sense, it is like a 1-dimensional version of an image represented by numeric pixel values. The challenge is to classify the resulting 1-dimensional input into 1 out of 64 classes which represent various letter, numbers and symbols.

Last Updated On: 
Tue, 07/14/2020 - 21:14

The dataset comprises of image file s of size 20 x 20 pixels for various types of metals and non-metal.The data collected has been augmented, scaled and modified to represent a number a training set dataset.It can be used to detect and identify object type based on material type in the image.In this process both training data set and test data set can be generated from these image files. 


## Instruction

The dataset is contained in a zip file named as it and unzip it.

# command unzip in linux

# Simply unzip in windows

The folder contains five classes as followed.


1.copper 2. iron 3. nickel 4. plastic 5. silver.


These are stored as sub-directories under main directory(object_type_material_type).Each sub-directory contains 100 image files in jpg format of size 20 x 20 pixels.


Out of these classes 4 are metals type as copper, iron, nickel ,silver and one non-metal type as plastic.These image files can be used as training data set and test dataset as well.



We introduce a novel dataset containing a total of 61 distinct HEAs. The proposed appliances (e.g. fans, fridges, washers, etc.) are of different kinds, ages, brands and powerlevels. They have been recorded in steady-state conditions in a French 50 Hz electrical grid. The measurement setup consists of an AC current probe (E3N Chauvin Arnoux) with a 10 mV/A sensitivity and a differential voltage probe witha 1/100 attenuation.


CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed.


Download and extract the ZIP file containing all files. There is python code available (under 'scripts') to easily load the data set. Other programming languages should also handle .jpg, .hdf and .csv files for easy access. For easy access with python, a pickle dump file has been added. This has no extra information compared to the .csv file.


The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat.


This dataset comes up as a benchmark dataset for machines to automatically recognizing the handwritten assamese digists (numerals) by extracting useful features by analyzing the structure. The Assamese language comprises of a total of 10 digits from 0 to 9. We have collected a total of 516 handwritten digits from 52 native assamese people irrespective of their age (12-86 years), gender, educational background etc. The digits are captured in .jpeg format using a paint mobile application developed by us which automatically saves the images in the internal storage of the mobile.