remote sensing
AndalUnmixingRGB is a Sentinel-2 satellite digital RGB imagery enriched with environmental ancillary data and designed for blind spectral unmixing using deep learning. Generally, spectral unmixing involves two main tasks: spectral signature identification of different available land use/cover types in the analyzed hyperspectral or multispectral imagery (endmember identification task) and their respective proportions measurement (abundance estimation task).
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BENELUX Region of Interest (ROI) , comprised of the Belgium, the Netherlands and Luxembourg
We use the communes administrative division which is standardized across Europe by EUROSTAT at:
https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
This is roughly equivalent to the notion municipalities in most countries.
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This dataset is used to create the results and figures presented on the manuscript: Ben Moshe et al., "Empirical Study on the Effect of Birds on Commercial Microwave Links and its Application for Bird" Detection".
Application for Bird Detection"
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The data relates to a study to captured deciduous broadleaf Bidirectional Scattering Distribution Functions (BSDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BSDF for extension to any illumination angle, viewing zenith, or azimuthal angle. Measurements were made from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra).
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Emergency managers of today grapple with post-hurricane damage assessment that is often labor-intensive, slow,costly, and error-prone. As an important first step towards addressing the challenge, this paper presents the development of benchmark datasets to enable the automatic detection ofdamaged buildings from post-hurricane remote sensing imagerytaken from both airborne and satellite sensors. Our work has two major contributions: (1) we propose a scalable framework to create benchmark datasets of hurricane-damaged buildings
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In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no-tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing and crop rotation, makes agriculture highly dynamic. In this paper, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping.
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