Geoscience and Remote Sensing

Slow moving motions are mostly tackled by using the phase information of Synthetic Aperture Radar (SAR) images through Interferometric SAR (InSAR) approaches based on machine and deep learning. Nevertheless, to the best of our knowledge, there is no dataset adapted to machine learning approaches and targeting slow ground motion detections. With this dataset, we propose a new InSAR dataset  for Slow SLIding areas DEtections (ISSLIDE) with machine learning. The dataset is composed of standardly processed interferograms and manual annotations created following geomorphologist strategies.

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Indian Pines

This scene was gathered by AVIRIS sensor  over the Indian Pines test site in North-western Indiana and consists of 145\times145 pixels and 224 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters. This scene is a subset of a larger one. 

Salinas

This scene was collected by the 224-band AVIRIS sensor over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples.

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We used  Sentinel-2 images to create the dataset In order to estimate sequestered carbon in the above-ground forest Biomass.  Moreover, fieldwork was completed to gather related forest biomass volume. The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared.

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1055 Views

The  Sentinel-2 L2A multispectral data cubes include two regions of interest (roi1 and roi2) each of them containing 92 scenes across Switzerland within T32TLT, between 2018 and 2022, all band at 10m resolution These areas of interest show a diverse landscape, including regions covered by forests that have undergone changes, agriculture and urban areas.

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Progress toward the use of S-band SoOp in sea surface remote sensing was demonstrated in a 2012-2013 experiment based at the Harvest Oil Platform located at 34.469° N and 120.682° W, roughly 11 km from Point Conception, Santa Barbara, CA. Satellite transmissions from the XM-radio service were observed, using one channel each from the “Rhythm” (located above 85°W) and the “Blues” (115°W) satellites. Each downlink channel had a bandwidth of 1.886 MHz with a symbol rate of 1.64 Msps in Quadrature Phase Shift Key (QPSK) modulation.

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The dataset contains fitted three-pole Debye dielectric model parameters of 567 soil spectra. Three soils of loamy sand, sandy loam, and silt loam textures were tested. Of each soil, 20 samples of various water contents were prepared with the use of distilled water and potassium chloride solutions, 5 samples for each liquid. Air-dry samples were also prepared. Dielectric spectra were obtained with the use of a six-channel coaxial-transmission-line cell system at 9 controlled temperature steps from 0.5 to 40°C in the 0.02 – 3 GHz frequency range.

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207 Views

In precision agriculture, detecting productive crop fields is an essential practice that allows the farmer to evaluate operating performance separately and compare different seed varieties, pesticides, and fertilizers. However, manually identifying productive fields is often time-consuming, costly, and subjective. Previous studies explore different methods to detect crop fields using advanced machine learning algorithms to support the specialists’ decisions, but they often lack good quality labeled data.

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A computational experiment has been performed in order to evaluate systematic errors of atmospheric Total Water Vapor (TWV) and integral Liquid Water Content (LWC) microwave radiometric retrieval from satellites by means of dual-frequency method (inverse problem).  The errors under consideration may arise due to the non-linearity of brightness temperature level on true liquid water and effective cloud temperature dependencies and due to neglecting the spatial distribution of cumulus clouds in the satellite microwave radiometer antenna field-of-view (FOV).

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We propose a road intersection identification method based on spatial constraint relationships of vector data. To evaluate its wide applicability, OpenStreetMap (OSM) data from seven cities–Berlin, Beijing, London, Nanjing, New York City, Rome, and Shanghai–were employed to identify road intersections of these cities.

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