Geoscience and Remote Sensing

The Contest: Goals and Organization

The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples. The 2022 Data Fusion Contest will consist of two challenge tracks:

Track SLM:Semi-supervised Land Cover Mapping

Last Updated On: 
Mon, 03/07/2022 - 04:41

Radar data and Pluviometric data for Sardinia (Italy)

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The dataset contains UAV imagery and fracture interpretation of rock outcrops acquired in Praia das Conchas, Cabo Frio, Rio de Janeiro, Brazil. Along with georeferenced .geotiff images, the dataset contains filtered 500 x 500 .png tiles containing only scenes with fracture data, along with .png binary masks for semantic segmentation and original georeferenced shapefile annotations. This data can be useful for segmentation and extraction of geological structures from UAV imagery, for evaluating computer vision methodologies or machine learning techniques.

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Here, a dataset used in manuscript "Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models" Scepanovic et al. (https://doi.org/10.1109/JSTARS.2021.3116094) is published. The data contains preprocessed SAR backscatter digital numbers as 7000 geotiff image patches of size 512x512 (about 10 km x 10 km size) sampled from several wide-area SAR mosaics compiled from all summer Sentinel-1A images  acquired over Finland in the summer of 2018.

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This dataset is composed of a datacube with 271 x 271 pixels, and 96 bands.  The ground truth has 271 x 271 pixels.

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827 PoIs in Bogota D.C., Colombia, obtained from Foursquare. Format of the data is CSV with headers. The data include: id, geometry representation, name, category, latitude, longitude, and geohash.

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The files contain the measurement data of the PAZ antenna pattern for horizontal and vertical polarization at the location of the HITCHHIKER receiver.

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Targeting the Huangnibazi Landslide located in the southwestern mountainous region of China, which is mainly induced by the heavy rainfall and the Jiuzhaigou earthquake, we implement the self-potential (SP) monitoring system and global navigation satellite system (GNSS) on the slope. The SP and GNSS data monitored at the slow-moving stage of the landslide are supplied. 

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This dataset is the model data required in the manuscript for IEEE Transactions on geoscience and remote sensing.

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