Search results for "BS"

Cloud detection on the Google Earth engine platform

2017

The vast amount of data acquired by current high resolution Earth observation satellites implies some technical challenges to be faced. Google Earth Engine (GEE) platform provides a framework for the development of algorithms and products built over this data in an easy and scalable manner. In this paper, we take advantage of the GEE platform capabilities to exploit the wealth of information in the temporal dimension by processing a long time series of satellite images. A cloud detection algorithm for Landsat-8, which uses previous images of the same location to detect clouds, is implemented and tested on the GEE platform.

Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…

The size, shape, density and ring of the dwarf planet Haumea from a stellar occultation

2017

Ortiz, José Luis et. al.

THEMIS: A Parameter Estimation Framework for the Event Horizon Telescope

2020

This is an open access article.-- Full list of authors: Broderick, Avery E.; Gold, Roman; Karami, Mansour; Preciado-López, Jorge A.; Tiede, Paul; Pu, Hung-Yi; Akiyama, Kazunori; Alberdi, Antxon; Alef, Walter; Asada, Keiichi; Azulay, Rebecca; Baczko, Anne-Kathrin; Baloković, Mislav; Barrett, John; Bintley, Dan; Blackburn, Lindy; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Cho, Ilje; Conway, John E.; Cordes, James M.; Crew, Geoffrey B.; Cu…

Transferring deep learning models for cloud detection between Landsat-8 and Proba-V

2020

Abstract Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical proper…

Slender Ca II H fibrils mapping magnetic fields in the low solar chromosphere

2017

S. Jafarzadeh et. al.

Morphological Properties of Slender Ca ${\rm{II}}$ H Fibrils Observed by Sunrise II

2017

R. Gafeira et. al.

Analysing the effect of land use and vegetation cover on soil infiltration in three contrasting environments in northeast Spain

2017

Este estudio presenta el análisis conjunto de la información obtenida a partir de 195 ensayos de infiltración en el campo, que fueron realizados mediante dispositivos de doble anillo. Los experimentos se realizaron en 20 situaciones contrastadas de usos del suelo, los cuales se encuentran distribuidos en tres contextos geográficos (costa NE de Cataluña, monte bajo del sector central del valle del Ebro y montaña media de la vertiente Sur del Pirineo central). El objetivo de esta investigación es determinar los factores más importantes que explican la variabilidad de la infiltración: uso del suelo, tipo de cubierta vegetal, características del suelo y del substrato rocoso, humedad del suelo y…

2021

Intracratonic basins tend to subside much longer than the timescale predicted by thermal relaxation of the lithosphere. Many hypotheses have been suggested to explain their longevity, yet few have been tested using quantitative thermo-mechanical numerical models, which capture the dynamic of the lithosphere. Lithospheric-scale geodynamic modelling preserving the tectono-stratigraphic architecture of these basins is challenging because they display only few kilometres of subsidence over 1000 of km during time periods exceeding 250 Myr. Here we present simulations that are designed to examine the relative role of thermal anomaly, tectonics and heterogeneity of the lithosphere on the dynamics …

GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment

2019

Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. …