Remote Sensing

In the attached dataset, a continuous record of 2493 chirp-sequence FMCW radar burst is presented. The data consist of the direct ADC output of an AWR1443BOOST radar board from Texas Instruments during 47.37 s of continuous operation. During this time, two FMCW radars of the model AWR1642BOOST were continuously transmitting sweeps with its main lobe aimed towards the first radar. This produces the apparition of two different uncorrelated interferences at the victim radar side, which can be seen on different indexes of the provided capture.

This dataset provides valuable insights into hand gestures and their associated measurements. Hand gestures play a significant role in human communication, and understanding their patterns and characteristics can be enabled various applications, such as gesture recognition systems, sign language interpretation, and human-computer interaction. This dataset was carefully collected by a specialist who captured snapshots of individuals making different hand gestures and measured specific distances between the fingers and the palm.

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.

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.