Search results for " computing"
showing 10 items of 2075 documents
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.
Automotive Radar in a UAV to Assess Earth Surface Processes and Land Responses
2020
The use of unmanned aerial vehicles (UAVs) in earth science research has drastically increased during the last decade. The reason being innumerable advantages to detecting and monitoring various environmental processes before and after certain events such as rain, wind, flood, etc. or to assess the current status of specific landforms such as gullies, rills, or ravines. The UAV equipped sensors are a key part to success. Besides commonly used sensors such as cameras, radar sensors are another possibility. They are less known for this application, but already well established in research. A vast number of research projects use professional radars, but they are expensive and difficult to hand…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
First Results of Hyperspectral Scene Generation in Preparation of the Chime Imaging Spectrometer Mission
2021
End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes w…
Estimating Missing Information by Cluster Analysis and Normalized Convolution
2018
International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.
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…
Using MSG-Seviri Data to Monitor the Planet in Near Real Time
2018
The SEVIRI (Spinning Enhanced Visible and Infra Red Imager) instrument onboard MSG (Meteosat Second Generation) satellite series provides valuable data for the observation of our planet. We describe here the processing chain implemented at the Global Change Unit of the University of Valencia to provide information such as vegetation index, temperatures of both land and sea, synthetic quicklooks for an easy interpretation of the data as well as fire hotspots. Vegetation index and temperature data are available for download from a dedicated portal updated every 3 hours with the most recent processed data. Additionally, a web page displays this information for a non scientific public in near r…
On the Dependence of Cirrus Parametrizations on the Cloud Origin
2019
<p>Particle size distributions (PSDs) for cirrus clouds are important for both climate models as well as many remote sensing retrieval methods. Therefore, PSD parametrizations are required. This study presents parametrizations of Arctic cirrus PSDs. The dataset used for this purpose originates from balloon-borne measurements carried out during winter above Kiruna (Sweden), i.e. north of the Arctic circle. The observations are sorted into two types of cirrus cloud origin, either in-situ or liquid. The cloud origin describes the formation pathway of the ice particles. At temperatures below −38 °C, ice particles form in-situ from solution or ice nuclea…
A Cloud masking algorithm for the XBAER aerosol retrieval using MERIS data
2017
Abstract To determine aerosol optical thickness, AOT, and other geophysical parameters describing conditions in the atmosphere and at the earth's surface by inversion of remote sensing measurements from space based instrumentation, it is necessary to separate ground scenes into cloud free and cloudy or cloud contaminated. Identifying the presence of cloud in a ground scene and establishing an accurate and adequate cloud mask is a challenging task. In this study, measurements by the European Space Agency (ESA) MEdium Resolution Imaging Spectrometer (MERIS) have been used to develop a cloud identification and cloud mask algorithm for preprocessing prior to application of the new algorithm cal…
Evaluation of chloroplast genome annotation tools and application to analysis of the evolution of coffee species.
2018
International audience; Chloroplast sequences are widely used for phylogenetic analysis due to their high degree of conservation in plants. Whole chloroplast genomes can now be readily obtained for plant species using new sequencing methods, giving invaluable data for plant evolution However new annotation methods are required for the efficient analysis of this data to deliver high quality phylogenetic analyses. In this study, the two main tools for chloroplast genome annotation were compared. More consistent detection and annotation of genes were produced with GeSeq when compared to the currently used Dogma. This suggests that the annotation of most of the previously annotated chloroplast …