This dataset accompanies the IEEE Journal of Oceanic Engineering Special Issue on Verification and Validation of Airgun Source Signature and Sound Propagation Models. The special issue has is its origins in the International Airgun Modelling Workshop (IAMW) held in Dublin, Ireland, on 16 July 2016 (Ainslie et al., 2016).

Categories:
204 Views

This dataset is a companion to a paper, "Segmentation Convolutional Neural Networks for Automatic Crater Detection on Mars" by DeLatte et al. 2019. DOI link: http://dx.doi.org/10.1109/JSTARS.2019.2918302

 

These are the segmentation target files for the three targets described in the paper: solid filled, thicker edge, and thinner edge. 

Instructions: 

These files match with the tiles that can be downloaded from the THEMIS Daytime IR Global Mosaic: http://www.mars.asu.edu/data/thm_dir/

Alternatively, this directory can be used for the download: http://www.mars.asu.edu/data/thm_dir/large/

Use this file pattern to grab the tiles:

  • 0 to +30N: thm_dir_N00_*.png
  • -30N to 0: thm_dir_N-30_*.png 

 

Included here are three targets for the 24 tiles ±30º latitude, 0-360º longitude. (Each tile is 30º by 30º, 7680 x 7680 pixels, and has a resolution of 256 pixels per degree). Craters with 2-32km radius are included, as identified by the Robbins & Hynek global Mars dataset (http://craters.sjrdesign.net/). The original data file for the crater locations and parameters can be found here: http://craters.sjrdesign.net/RobbinsCraterDatabase_20121016.tsv.zip 

Any arbitrary range of segmentation crater targets can be created using the file and python OpenCV.

 

To use for segmentation, download the corresponding THEMIS Daytime IR Global Mosaic tiles and this dataset can be used as the target images for segmentation. The filenames of the target files will match the filenames in the THEMIS Daytime IR Global Mosaic.

 

The file names for each type match the following patterns:

  • solid filled: thm_dir_N*_2_32_km_segrng.png
  • thicker edge (8): thm_dir_N*_2_32_km_segrng_8_edge.png
  • thinner edge (4): thm_dir_N*_2_32_km_segrng_4_edge.png

(segrng = segmentation range, referring to the 2-32 km radius range of craters in this dataset)

The numbers 4 and 8 above refer to the thickness parameter in python OpenCV. The circle drawing function is described here: https://docs.opencv.org/3.0-alpha/modules/imgproc/doc/drawing_functions....

 

 

 

Categories:
688 Views

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).

Categories:
534 Views

After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms.

Instructions: 

To extract the dataset, please unzip the main file 'Post-hurricane.zip'. There will be 4 folders inside:

  1. train_another : the training data; 5000 images of each class
  2. validation_another: the validation data; 1000 images of each class
  3. test_another : the unbalanced test data; 8000/1000 images of damaged/undamaged classes
  4. test : the balanced test data; 1000 images of each class

All images are in JPEG format, the class label is the name of the super folder containing the images

Categories:
1186 Views

The Xuzhou dataset was collected by an airborne HYSPEX hyperspectral camera over the Xuzhou peri-urban site in November 2014. This dataset consists of 500 × 260 pixels, with a very high spatial resolution of 0.73 m/pixel. The number of spectral bands used in the experiment was 436, after removing the noisy bands ranging from 415 nm to 2508 nm.

Categories:
618 Views

Fig.  shows the radar echo energy of the four tilted beams with different tilted angles observed by the Wuhan MST radar using the low mode (unit: dB), which is the average value of three observation cases (LT: 2011-03-17, 08:30-10:00; 2011-03-15, 18:00-19:30; 2011-03-15, 19:30-21:00).

Categories:
110 Views

My first use of Ruby on Rails in an Arduino project is a simple temperature and humidity logger. Ideally I'll create another Arduino project to graph the data. Of course we need an Arduino with networking capabilities. You really can't go past the excellent, tiny, cheap HUZZAH from Adafruit.


Electronics

Components I used were:

Categories:
65 Views

My first use of Ruby on Rails in an Arduino project is a simple temperature and humidity logger. Ideally I'll create another Arduino project to graph the data. Of course we need an Arduino with networking capabilities. You really can't go past the excellent, tiny, cheap HUZZAH from Adafruit.


Electronics

Components I used were:

Categories:
169 Views

Beijing taxi trip data (sample)

Categories:
1830 Views

High-fidelity, physics-based multichannel radar data cube provided by the DARPA KASSPER project. This data is ideal for analyzing space-time adaptive processing (STAP) algorithms since both sample data and truth data are provided.

Categories:
1732 Views

Pages