The data set is compressed into a zip file. Please unzip this file in the desired place and inside the main folder, you will find the file Readme.md with the instructions and the details of the database.

A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Email the authors at ushasi@iitb.ac.in for any query.

Classes in this dataset:

Airplane

Baseball Diamond

Buildings

Freeway

Golf Course

Harbor

Intersection

Mobile home park

Overpass

Parking lot

River

Runway

Storage tank

Tennis court

Paper

The paper is also available on ArXiv: A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images

Feel free to cite the author, if the work is any help to you:

``` @InProceedings{Chaudhuri_2020_EoC, author = {Chaudhuri, Ushasi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai}, title = {A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images}, booktitle = {http://arxiv.org/abs/2008.05225}, month = {Aug}, year = {2020} }

For any question, please send an email to antonio.marceddu@polito.it.

The anti-islanding protection relays are included in the "Relay Protection Bus B20 (20 kV)" subsystem.

This dataset was used for OFDM Signal Real-Time Modulation Recognition Based on Deep Learning and Software-Defined Radio, which provides additional details and description of the dataset. We generate 6 modulated OFDM baseband signals with header modulation and payload modulation as BPSK+BPSK, BPSK+QPSK, BPSK+8PSK, QPSK+BPSK, QPSK+QPSK, QPSK+8PSK, respectively. The SNR range of each signal is from -10 dB to +20 dB at intervals of 2 dB. There are 4096 pieces of data generated for each signal type under a specific SNR and each piece of data has 1024 samples. That is, 6×16×4096 = 393216 pieces in total.

Instructions

The dataset consists of 21 data sequences recorded in 12 agricultural scenarios in the autumn season. The following <..sequence_id..>'s are available:

• 01_forest -- Closed loop, Forest trail, No wind, Daytime
• 02_forest -- Closed loop, Forest trail, No wind, Daytime
• 03_green_meadow -- Closed loop, Meadow, grass up to 30 cm, No wind, Daytime
• 04_green_meadow -- Closed loop, Meadow, grass up to 30 cm, Mild wind, Daytime
• 05_road_asphalt -- Closed loop, Asphalt road, No wind, Nighttime
• 06_plantation -- Closed loop, Shrubland, Mild wind, Daytime
• 07_plantation -- Closed loop, Asphalt road, No wind, Nighttime
• 08_plantation_water -- Random movement, Sprinklers (water drops on camera lens), No wind, Nighttime
• 09_cattle_farm -- Closed loop, Cattle farm, Mild wind, Daytime
• 10_cattle_farm -- Closed loop, Cattle farm, Mild wind, Daytime
• 11_cattle_farm_feed_table -- Closed loop, Cattle farm feed table, Mild wind, Daytime
• 12_cattle_farm_feed_table -- Closed loop, Cattle farm feed table, Mild wind, Daytime
• 13_ditch -- Closed loop, Sandy surface, Edge of ditch or drainage channel, No wind, Daytime
• 14_ditch -- Closed loop, Sandy surface, Shore or bank, Strong wind, Daytime
• 15_young_pines -- Closed loop, Sandy surface, Pine coppice, No wind, Daytime
• 16_winter_cereal_field -- Closed loop, Winter wheat sowing, Mild wind, Daytime
• 17_winter_cereal_field -- Closed loop, Winter wheat sowing, Mild wind, Daytime
• 18_winter_rapeseed_field -- Closed loop, Winter rapeseed, Mild wind, Daytime
• 19_winter_rapeseed_field -- Closed loop, Winter rapeseed, Mild wind, Daytime
• 20_field_with_a_cow -- Closed loop, Cows tethered in pasture, Mild wind, Daytime
• 21_field_with_a_cow -- Closed loop, Cows tethered in pasture, Mild wind, Daytime

Each sequence contains the following separately downloadable files:

• <..sequence_id..>_data.tar.gz -- entire date sequence in raw data format (AEDAT2.0 - DVS, images - RGB-D, point clouds in pcd files - LIDAR, and IMU csv files with original sensor timestamps). Timestamp conversion formulas are available.
• <..sequence_id..>_rosdata.tar.gz -- main sequence in ROS bag format. All sensors timestamps are aligned with DVS with an accuracy of less than 1 ms.
• <..sequence_id..>_rawcalib_data.tar.gz -- recorded fragments that can be used to perform the calibration independently (intrinsic, extrinsic and time alignment).
• <..sequence_id..>_video.mp4 -- provides an overview of the sequence data (for the DVS and RGB-D sensors).

The contents of each archive are described below...

Raw format data

The archive <..sequence_id..>_data.tar.gz contains the following files and folders:

+ meta-data/ - all the useful information about the sequence
| + meta-data.md - detailed information about the sequence,
| | sensors, files, and data formats
| + cad_model.pdf - sensors placement
| + <...>_timeconvs.json - timestamp conversion formulas
| + ground-truth/ - movement ground-truth data,
| | calculated using 3 different Lidar-SLAM algorithms
| | (Cartographer, HDL-Graph, LeGo-LOAM)
| + calib-params/ - intrinsic and extrinsic calibration parameters
+ recording/ - main sequence
+ dvs/ - DVS events and IMU data
+ lidar/ - Lidar point clouds and IMU data
+ realsense/ - Realsense camera RGB, Depth frames, and IMU data
+ sensorboard/ - environmental sensors data
(temperature, humidity, air pressure)


ROS Bag format data

Will be published later...

Calibration data

The <..sequence_id..>_rawcalib_data.tar.gz archive contains the following files and folders:

+ imu_alignments/ - IMU recordings of the platform lifting/releasing before
| and after the main sequence
| (can be used for custom timestamp alignment)
+ solenoids/ - IMU recordings of the solenoid vibrations before
| and after the main sequence
| (can be used for custom timestamp alignment)
+ lidar_rs/ - Lidar vs Realsense camera extrinsic calibration
| by placing a spherical object (ball) in the field of view of both sensors
+ dvs_rs/ - DVS and Realsense camera intrinsic and extrinsic
calibration frames (checkerboard pattern)


Detailed documentation is provided in the following link: https://github.com/hmchuong/ML-BoneSuppression