The SWINSEG dataset contains 115 nighttime images of sky/cloud patches along with their corresponding binary ground truth maps The ground truth annotation was done in consultation with experts from Singapore Meteorological Services. All images were captured in Singapore using WAHRSIS, a calibrated ground-based whole sky imager, over a period of 12 months from January to December 2016. All image patches are 500x500 pixels in size, and were selected considering several factors such as time of the image capture, cloud coverage, and seasonal variations.

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This is the images and the image masks used in the paper "Z. Petrou and Y. Tian, Prediction of Sea Ice Motion with Convolutional Long Short-Term Memory Networks,IEEE Transactions on Geoscience and Remote Sensing."

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Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.

 

This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.

 

Instructions: 

The files are identified sequentially according to the processing step:

 

  • A1-img-nd_samples.xlsx: Digital numbers of water samples extract from the hyperspectral image
  • A2-img-nd_targets.xlsx: Digital numbers of reference targets extract from the hyperspectral image
  • B1-asd-rad_refl_targets.xlsx: Radiance values collected with ASD HandHeld of the reference targets and calculated Reflectance
  • B2-asd-simulatedbands_refl.xlsx: Target reflectance values calculated and simulated to match the hyperspectral camera response function
  • C1-trios-rad_refl_samples.xlsx: Radiance values collected with TriOS of the water points and calculated Reflectance
  • C2-trios-simulatedbands_refl.xlsx: Water reflectance values calculated and simulated to match the hyperspectral camera response function
  • D1-nd_data.csv: Digital number extracted from the hyperspectral image (CSV format, this is the input of the algorithm)
  • D1-nd_data.xlsx: Digital number extracted from the hyperspectral image (xlsx format)
  • D2-r_data.csv: Reflectance calculated from the spectroradiometers measurements (CSV format, this is the input of the algorithm)
  • D2-r_data.xlsx: Reflectance calculated from the spectroradiometers measurements (xlsx format)
  • D3-r_nd_targets.xlsx: Agregation from D1 and D2 data to compare the data
  • E1-calc_coef_line.py: Python algorithm to calibrate and validate the empirical line model
  • Fit.py: Python script class to calculate the Fit of linear and exponential function
  • output_graphs.zip: The results of the graphs generated for each of the evaluated combinations. In this package are different graphical representations for each of the combinations of samples and targets, as well as for the exponential and linear fits.

 

All files of the output folder are self-explained, because the filename identifies how the ELM was calibrated.

 

Details and descriptions about the full process steps are in the official paper (under journal review).

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This database is a image set of a strongest glint-affected region of inland water Capivara reservoir, Brazil. We carried out a flight survey in September 2016 on the confluence region of the Tibagi and Paranapanema Rivers. We use the hyperspectral camera manufactured by Rikola, model FPI2014, wich collect 25 spectral bands at following intervals and full widths at half maximum (FWHM), both expressed in nanometers (nm):

 

Instructions: 

Each folder have specific resources generated on the processing steps. The generated resources, step by step, are:

 

1-roi_target.zip: ROI and river target shapefiles to delimit the process; 

2-roitarget.zip: intersection of the ROI and river target;

3-imgs_roi.zip: Images clipped by the ROI target;

4-virtual_bands_None.zip: Virtual bandset generated using GDAL;

5ra-pixel_refs.zip: CSV file of mode values of each image band;

5rb-img_ref_fast_None.zip: Multscale image references generated by the author method proposes;

5rc-img_ref_gaussianmedian_None.zip: Local image references generated with Gaussian filter;

6ra-mosaics_refmodaNone.zip: Global reference mosaic;

6rb-mosaics_refDQNone.zip: Multiscale reference mosaic;

6rc-mosaics_ref_gaussianNone.zip: Local reference mosaic;

6sa-mosaic_first_None.zip: First value mosaic

6sb-mosaic_last_None.zip: Last value mosaic;

6sc-mosaic_mean_None.zip: Mean value mosaic;

6sd-mosaic_median_None.zip: Median value mosaic;

6se-mosaic_maximum_None.zip: Maximum value mosaic;

6sf-mosaic_minimum_None.zip: Minimum value mosaic;

 

Details and descriptions about the full process steps are in the oficial paper (under journal review).

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Stereolithographic files of three different mesh designs; Square, Triangular and Hexagonal (top to bottom rows in image). Each mesh design is provided in three different deformations; Flat/no deformation, Mode 1 deformation and Mode 5 deformation (left to right columns in image). Mode 1 and Mode 5 deformation was obtained by modal study with the four outer surfaces of the frame fixed. Note that the out-of-plane deformation has been scaled to better proportions.

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Emergency  managers  of  today  grapple  with  post-hurricane damage assessment that is often labor-intensive, slow,costly,   and   error-prone.   As   an   important   first   step   towards addressing  the   challenge,   this   paper   presents   the   development of  benchmark  datasets  to  enable  the  automatic  detection  ofdamaged buildings from post-hurricane remote sensing imagerytaken  from  both  airborne  and  satellite  sensors.  Our  work  has two  major  contributions:  (1)  we  propose  a  scalable  framework to  create  benchmark  datasets  of  hurricane-damaged  buildings

Instructions: 

Data can be used for object detection algorithms to properly annotate post disaster buildings as either damaged or non damaged aiding disaster response. This dataset contains ESRI Shapefiles of bounding boxes of buildings labeled as either non-damaged or damaged. Those labeled as damaged also have four degrees of damage from minor to catastrophic. Importantly, each bounding box is also indexed to one of the images in the NOAA post Harvey hurricane imagery dataset allowing users to match the bounding boxes with the correct imagery for training the algorithm.

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This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.

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This work quantifies water contamination in jet fuel (Jet A-1), using silica-based Bragg gratings. The optical sensor geometry exposes the evanescent optical field of a guided mode to enable refractometery. Quantitative analysis is made in addition to the observation of spectral features consistent with emulsification of water droplets and Stokes’ settling. Measurements are observed for cooling and heating cycles between ranges of 22oC and -60oC. The maximum spectral sensitivity for water contamination was 2.4 pm/ppm-v with a resolution of < 5 ppm-v.

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The Doppler spectrum (DS) of the signal backscattered at the low incidence angles from the sea surface was measured by the Ka-band radar in an experiment on a marine oceanographic platform on October 5, 2016. The dependence of DS shift and width on incidence angle and azimuth angle is given.

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Run_IMA_proceure.R is an script that fills the clouds of the 3 target images or the complete neighborhood of 27 images of LST and NDVI remote sensing data in Navarre(Spain) and estimates the standard errors.

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