Remote Sensing

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.

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This dataset contains multispectral high resolution 1627 image patches of size 10 x 10 pixels with each pixel size of 10mx10m. These patches are generated from the Sentinel-2 (A/B) satellite images acquired during the period of October 2018 to May 2019. It covered one life cycle (12 months) of the sugarcane crop in the region of the Karnataka, India. Many parameters like plantation season, soil type, plantation type, crop variety and irrigation type that affects the growth of the sugarcane crop are considered while generating the samples.

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Presented here is a dataset used for our SCADA cybersecurity research. The dataset was built using our SCADA system testbed described in our paper below [*]. The purpose of our testbed was to emulate real-world industrial systems closely. It allowed us to carry out realistic cyber-attacks.

 

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The Dataset

We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite.

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This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).

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Level 2 data set. Averaging over multiple measurements were performed to reduce electronic noise and jitter

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This dataset includes the following data in supporting the submitted manuscript 'Datacube Parametrization-Based Model for Rough Surface Polarimetric Bistatic Scattering' to IEEE Transactions on Geoscience and Remote Sensing. 

  • LUT of coefficient c from fitting the contour level bounds
  • LUT of coefficient c from fitting the contour center shifts
  • specular scattering coefficients from the SEBCM simulated datacube
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Modern science is build on systematic experimentation and observation.  The reproducibility and replicability of  the experiments and observations are central to science. However, reproducibility and replicability are not always guaranteed, sometimes referred to as 'crisis of reproducibility'. To analyze the extent of the crisis, we conducted a survey on the state of reproducibility in remote sensing. This survey was conducted as an online survey. The answers of the respondents are saved in this dataset in full-text CSV format.

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Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.

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