Remote Sensing

This dataset consists of high-resolution visible-spectrum (RGB) and thermal infrared (TIR) images of two vineyards (Vitis vinifera L.) with varieties of Mouhtaro and Merlot, which was captured by Unmanned Aerial Vehicle (UAV) carrying TIR and RGB sensors three times in a cultivation period.

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579 Views

Data related to figures 3 and 7 in T. Fordell, K. Hanhijärvi, A.E. Wallin, J. Myyry, T. Lindvall ”Out-of-Band Fibre-Optic Time and Frequency Transfer Using Asymmetric and Symmetric Opto-Electronic Repeaters”, IEEE
IEEE Trans. Ultrason. Ferroelectr. Freq. Control (2023).

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43 Views

Development of the Complex-Valued (CV) deep learning architectures has enabled us to exploit the amplitude and phase components of the CV Synthetic Aperture Radar (SAR) data. However, most of the available annotated SAR datasets provide only the amplitude information (Only detected SAR data) and disregard the phase information. The lack of high-quality and large-scale annotated CV-SAR datasets is a significant challenge for developing CV deep learning algorithms in remote sensing.

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1265 Views

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of  the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers.

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144 Views

SWAN is a large-scale outdoor point cloud semantic segmentation, instance segmentation and object detection dataset.  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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496 Views

SWAN is a Large-Scale Outdoor Point Cloud semantic segmentation dataset .  The dataset is targeted explicitly at the challenging urban environment, which aligns well with the needs of the intelligent transportation systems. The data is collected in the Central Business District (CBD) of Perth city in Australia, covering nearly 150km. It additionally used specialized equipment (portable trolley) to capture scenes of no-through roads and narrow streets.

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This data will be used for discontinuity identification and analysis, which contains three main sets of discontinuities with yields of 281.1°/82.0°, 114.0°/80.2° and 359.0°/81.6°.

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研究区域为 大红山铁矿,位于中国云南省玉溪市。 矿区海拔600米-1850米,属于侵蚀 和剥蚀山地地形,深切口,大起伏和 沟壑和山谷网络。矿井主体为裸露岩石 周围植被覆盖。主要岩性是变质熔岩, 辉长岩、辉长岩和白云石方钠石。

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The dataset includes channel frequency response (CFR) data collected through an IEEE 802.11ax device for human activity recognition. This is the first dataset for Wi-Fi sensing with the IEEE 802.11ax standard which is the most updated Wi-Fi version available in commercial devices. The dataset has been collected within a single environment considering a single person as the purpose of the study was to evaluate the impact of communication parameters on the performance of sensing algorithms.

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2006 Views

Bistatic MIMO Radar Sensing

This dataset includes the synthetic aperture measurement data and code accompanying the publication "Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer" [1].

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