Geoscience and Remote Sensing

This is the data Archive for Zhang, et al., “A Geophysical Model Function for S-band Reflectometry of Ocean Surface Winds in Tropical Cyclones,” accepted by Geophysical Research Letters. This data set was generated from twelve (12) days of airborne S-band (2.3 GHz) reflectometry data collected during the 2014 hurricane season between 2 July 2014 and 17 September 2014. Cross-correlations between the direct and reflected S-band signals, commonly referred to as the “waveform” or delay-Doppler map (DDM) are provided with corresponding aircraft time and position data.


The benchmark dataset  are consisted of 2,413 three-channel RGB images obtained from Google Earth satellite images and AID dataset.


Infrared imaging from aerial platforms can be used to detect landmines and minefields remotely and can save many lives. This dataset contains thermal images of buried and surface landmines. The images were recorded from a fixed camera for 24 hours with 15-minute intervals. DM-11 type anti-personnel landmines were used. This dataset is available for landmine detection research.


This dataset provides digital images and videos of surface ice conditions were collected from two Alberta rivers - North Saskatchewan River and Peace River - in the 2016-2017 winter seasons.

Images from North Saskatchewan River were collected using both Reconyx PC800 Hyperfire Professional game cameras mounted on two bridges in Edmonton as well as a Blade Chroma UAV equipped with a CGO3 4K camera at the Genesee boat launch.

Data for the Peace River was collected using only the UAV at the Dunvegan Bridge boat launch and Shaftesbury Ferry crossing.


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.


The paper describes different rainfall observing techniques available in the City of Genoa (Italy): the Ligurian regional tipping-bucket rain gauge (TBRG) network and the Monte Settepani long range weather radar (WR) operated by the Ligurian Regional Environmental Protection Agency (ARPAL) and Smart Rainfall System (SRS), a network of microwave sensors for satellite down-links developed by the University of Genoa and Artys srl.


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.



The PS-InSAR analysis method is a technique that utilizes persistent scatter in SAR images and performs image analysis by interfering with 25 or more slave images in a master image. Determining the accuracy of the above algorithm is the denser between images, the higher the coherence, the more accurate the image is. Therefore, the Minimum Spanning Tree (MST) algorithm is used to find the optimum coherence by considering the temporal, spatial, and coherence of each image rather than Star graph, which interferes with the rest of the slave images in one master image.


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).


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:


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