<p>This is the image dataset for satellite image processing&nbsp; which is a collection therml infrared and multispectral images .</p>

Instructions: 

Dataset images
Thermal infrared images and multispectral images
image size:512x512
format:
image:.tiff
file :.h5

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28 Views

This study presents a dataset that comprises the magnetic field, Wi-Fi, and the data from the inertial measurement unit (IMU) sensors of the smartphone including accelerometer, gyroscope, and barometer. First, the important

characteristics of both the Wi-Fi and the magnetic field that require further investigation are highlighted, and later the data are collected. The data are collected over a longer period spanning approximately five years involving five

Instructions: 

For instructions, kindly check the following paper which is published in IEEE Access:

"MagWi: Benchmark Dataset for Long Term Magnetic Field And Wi-Fi Data Involving Heterogeneous Smartphones, Multiple Orientations, Spatial Diversity and Multi-floor Buildings".

For further queries, contact at ashrafimran@live.com

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39 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

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} }

 

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148 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Categories:
5 Views

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Categories:
2 Views

With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

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:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @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} }

Categories:
2 Views

With the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.

Instructions: 

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:

  1. Airplane
  2. Baseball Diamond
  3. Buildings
  4. Freeway
  5. Golf Course
  6. Harbor
  7. Intersection
  8. Mobile home park
  9. Overpass
  10. Parking lot
  11. River
  12. Runway
  13. Storage tank
  14. Tennis court

Paper

``` @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} }

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2 Views

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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109 Views

Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information.

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139 Views

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