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.
This dataset contains examples of 4D sesimic and simulation models for 4D seismic matching. Each example is a tuple containing: the reference (observed 4D seismic), two candidate simulation models, and a label indicating the simulation model most similar to the reference according to human evaluation. It was created to train the three-way convolutional neural network described in the paper "A Three-Way Convolutional Network to Compare4D Seismic Data and Reservoir Simulation Modelsin Different Domains".
We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels); it is varied, covering 16 conurbations in France, with variousclimates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics.
This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).
This dataset extends the Urban Semantic 3D (US3D) dataset developed and first released for the 2019 IEEE GRSS Data Fusion Contest (DFC19). We provide additional geographic tiles to supplement the DFC19 training data and also new data for each tile to enable training and validation of models to predict geocentric pose, defined as an object's height above ground and orientation with respect to gravity. We also add to the DFC19 data from Jacksonville, Florida and Omaha, Nebraska with new geographic tiles from Atlanta, Georgia.
This dataset includes the following data in supporting the submitted manuscript 'Datacube Parametrization-Based Model for Rough Surface Polarimetric Bistatic Scattering' to IEEE Transactions on Geoscience and Remote Sensing.
LUT of coefficient c from fitting the contour level bounds
LUT of coefficient c from fitting the contour center shifts
specular scattering coefficients from the SEBCM simulated datacube
Depths to the various subsurface anomalies have been the primary interest in all the applications of magnetic methods of geophysical prospection. Depths to the subsurface geologic features of interest are more valuable and superior to all other properties in any correct subsurface geologic structural interpretations.
This dataset contains four types of geospatial events coverage in Indonesian news online portal: flood, traffic jam, earthquake, and fire. The corpus itself was composed of 926 manually annotated, disambiguated, and event extracted sentences that was filtered from 83 of 645,679 documents of our earlier news corpus based on four major geospatial events: flood, earthquake, fire, and accidents.
Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.
This dataset includes binary files of radiometric measurement sessions 2018-2019. Measurements of microwave descending radiation in the band of resonance absorption of water vapor 18 - 27.2 GHz were performed. The observations were carried out by means of special microwave multichannel (47 channels) radiometer-spectrometer developed in Kotel'nikov Institute of Radioengineering and Electronic of RAS Special Design Bureau. Radiometer was located in Fryazino, Moscow Region, Russian Federation.
