Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.
The dataset contains information about roses cultivation in greenhouses. It is aimed at identifying corrective actions to improve the roses state. Data acquisition was done with an autonomous robot incorporating sensors such as: soil humidity, light, temperature and humidity, and CO2.
Data set contains logs from OptiTrack motion camera system and flex sensor information from a smart glove. Participants performed finger taps for 10 secs.
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.
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.
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."
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.
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):
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; Flate/no deformation, Mode 1 deformation and Mode 5 deformation (left to right columns in image). Mode 1 and Mode 5 deformation was obtained with the four outer surfaces of the frame fixed and the out-of-plane deformation scaled down.
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
