Geoscience and Remote Sensing

Synthetic Aperture Radar (SAR) satellite images are used increasingly more for Earth observation. While SAR images are useable in most conditions, they occasionally experience image degradation due to interfering signals from external radars, called Radio Frequency Interference (RFI). RFI affected images are often discarded in further analysis or pre-processed to remove the RFI.

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This dataset provides the high-resolution remote senisng data regarding various coastline scenes.

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This folder contains folders of images.
The original folder contains the non dehazed images, default and tuned contain the dehazed counterparts.
Default folder referes to the outputs obtained using an exponent of 0.8.
Tuned refers to the images with a PSNRBR of 54 or above.
The images in paper are kept in a seperate folder.

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Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of building roof type from satellite images is a highly challenging task due to ambiguous visual features within the satellite imagery.

Last Updated On: 
Tue, 03/07/2023 - 11:58

This file include Instrument lookup tables for EV8TOz algorithm use. It also inlcude sensor soft calibration tables.

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This dataset is about the manuscript "Density and Magnetization Vector Joint Inversion Method of Gravity and Magnetic Data With Tetrahedral Unstructured Grid and Its Application in the Huanggangliang–Ganzhuermiao Metallogenic Belt".

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This dataset contains support data for the manuscript "Parallel 3D Frequency Domain Electromagnetic Modeling based on Mesh Partitioning technique" submitted to TGRS.

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<p>Images from Sentinel 2 for dehazing. Contains 3 folders, one with original images, one with dehazed images at default exponent of 0.8 and the last with failed images with fine tuned exponent (thus becoming successful).</p>

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Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles.

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100 GPR images of expressways in China

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