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.

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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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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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Data are collected on a 5m×10msized test bed, which is set up at Kadir Has University,Istanbul. Wireless access points are located around the corners of the testbed and markers are placed at every 45 cm. RSSI measurements done on the grid shown in Figure are stored via NetSurveyor program running on a Lenovo IdeapadFLEX 4 laptop, which has an Intel Dual Band Wireless-AC8260 Wi-Fi adaptor.At each measurement point, RSSI data are collected for1 min with a sampling interval of 250 ms.

Instructions: 

Data  are  collected  on  a  5m×10msized  test  bed,  which  is  set  up  at  Kadir  Has  University,Istanbul. Wireless access points are located around the cornersof  the  test  bed  and  markers  are  placed  at  every  45  cm.RSSI  measurements  done  on  the  grid  shown  in  Figure  2  arestored via NetSurveyor program running on a Lenovo IdeapadFLEX  4  laptop,  

which  has  an  Intel  Dual  Band  Wireless-AC8260 Wi-Fi adaptor.At  each  measurement  point,  RSSI  data  are  collected  for1  min  with  a  sampling  interval  of  250  ms.  XML file is read with MATLAB for data of full area and applied trajectory.

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The datasets in the compressed file were used in the case study of the article entitled Automated Machine Learning Pipeline for Geochemical Analysis by Germán H. Alférez, et al. Our approach was evaluated with a compositional dataset from 6 fault-separated blocks in the Peninsular Ranges Province and Transverse Ranges Province. The Peninsular Ranges are a group of mountain ranges, stretching from Southern California to Southern Baja California, Mexico. North of the Peninsular Ranges Province is the east-west Transverse Ranges Province.

Instructions: 

The Cinco.csv file contains the original dataset with 514 samples. The CincoTrain.csv file contains the dataset used to train and evaluate the models. The CincoUnknown.csv file contains the dataset used to predict the unknown samples.

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Segmentation of TC clouds in 2016. The segmentation task was accomplished by an algorithm which takes a time series of brightness temperature images of TCs and uses image processing techniques to acquire segmentation for each image in a semi-supervised manner. 

Instructions: 

2016 TC cloud segmentation animation

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165 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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1880 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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692 Views

The detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), Hewlett Packard Enterprise, SolarAid, and Data Science Experts, aims to promote research in automatic detection of human settlements deprived of access to electricity using multimodal and multitemporal remote sensing data.

Last Updated On: 
Thu, 01/06/2022 - 03:33
Citation Author(s): 
Colin Prieur, Hana Malha, Frederic Ciesielski, Paul Vandame, Giorgio Licciardi, Jocelyn Chanussot, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya

The dataset is a new high-quality dataset to advance sea-land segmentation with high-resolution remote sensing images. The dataset contains 1,726 hand-labeled and cropped Gaofen-1 images with an 8-meter spatial resolution and 4 bands, covering the various types of coastlines in Lianyungang, China.

Instructions: 

The dataset is a new high-quality dataset to advance sea-land segmentation with high-resolution remote sensing images. The dataset contains 1,726 hand-labeled and cropped Gaofen-1 images with an 8-meter spatial resolution and 4 bands, covering the various types of coastlines in Lianyungang, China.

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146 Views

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