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 is compiled from five years of observation from the Global Precipitation Measurement (GPM) core observatory Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). Retrieved emissivites and surface backscatter cross sections are gridded at quarter-degree, monthly resolution separately for non-snow-covered land, snow-covered land, and sea ice.
The 2020 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) and the Technical University of Munich, aims to promote research in large-scale land cover mapping based on weakly supervised learning from globally available multimodal satellite data. The task is to train a machine learning model for global land cover mapping based on weakly annotated samples.
Here we present OpenSARUrban: a Sentinel-1 dataset dedicated to the content- related interpretation of urban SAR images, including a well- defined hierarchical annotation scheme, data collection, well- established procedures for dataset compilation and organization as well as properties, visualizations, and applications of this dataset.
Cloud-free imageries, acquired from Landsat 8 OLI during 2016 to 2018, were used to delineate the extents of the glacial lakes in the mountainous terrain of CPEC
The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.
The Data Fusion Contest 2016: Goals and Organization
The 2016 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data.
New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification:
The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As a result, researchers have often resorted to annotating their own training and testing data. However, different researchers may annotate different classes, or use different train and test splits.
This dataset was developed at the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology as part of the ongoing activities at the Center for Energy and Geo-Processing (CeGP) at Georgia Tech and KFUPM. LANDMASS stands for “LArge North-Sea Dataset of Migrated Aggregated Seismic Structures”. This dataset was extracted from the North Sea F3 block under the Creative Commons license (CC BY-SA 3.0).
