Remote Sensing

This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).

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Level 2 data set. Averaging over multiple measurements were performed to reduce electronic noise and jitter

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This dataset includes the following data in supporting the submitted manuscript 'Datacube Parametrization-Based Model for Rough Surface Polarimetric Bistatic Scattering' to IEEE Transactions on Geoscience and Remote Sensing. 

  • LUT of coefficient c from fitting the contour level bounds
  • LUT of coefficient c from fitting the contour center shifts
  • specular scattering coefficients from the SEBCM simulated datacube
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Modern science is build on systematic experimentation and observation.  The reproducibility and replicability of  the experiments and observations are central to science. However, reproducibility and replicability are not always guaranteed, sometimes referred to as 'crisis of reproducibility'. To analyze the extent of the crisis, we conducted a survey on the state of reproducibility in remote sensing. This survey was conducted as an online survey. The answers of the respondents are saved in this dataset in full-text CSV format.

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Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.

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This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.

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deleted

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Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 

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Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware.

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437 Views

Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.

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869 Views

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