Geoscience and Remote Sensing
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).
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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.
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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):
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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.
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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.
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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).
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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:
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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:
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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.
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