Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities

Severe hyponatraemia in diabetic patients treated with chlorpropamide or phenformine

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

CMIX: Cloud Mask Intercomparison eXercise

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masks have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. Here, we summarize results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10-30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), were evaluated within the CMIX. Those algorithms varied in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs were evaluated against existing reference cloud mask datasets. Those datasets varied in sampling methods, geographical distribution, sample unit (points, polygons, or full image labels), and generation approach (experts annotations, machine learning, or sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in cloud definitions used when producing the reference datasets. Average overall accuracy (across algorithms) varied 80.0±5.3% to 89.4±2.4% for Sentinel-2, and 79.8±7.1% to 97.6±0.8% for Landsat 8, depending on the reference dataset. An overall accuracy of 90% yields half the errors than an overall accuracy of 80%. The study identified algorithms that provided a balance between commission and omission errors, as well as algorithms, which are cloud conservative (high user’s accuracy) and non-cloud (clear) conservative (high producer’s accuracy). With repetitive observations like those of Sentinel-2, it seems reasonable to favor non-cloud conservative approaches, with maybe the exception of very cloudy regions where every cloud free observation is critical. When thin/semi-transparent clouds were not considered in the reference datasets algorithms’ performance generally improved: overall accuracy values increased by +1.5% to 7.4%. It should be noted though that these clouds are commonly occurring and are often present in optical imagery. Within CMIX, we also developed recommendations for further activities, which include provision of a quantitative definition for clouds (targeting moderate spatial resolution imagery by Landsat 8 and Sentinel-2), generation of new reference datasets, and expansion of the analysis framework (for example, multi-temporal analysis and application-driven validation). Such intercomparison studies will hopefully help the community to improve the algorithms and move towards standardization of cloud masking. Given the importance of cloud masking in optical satellite imagery we encourage CEOS to continue the CMIX activities

RD50 Status Report 2008 - Radiation hard semiconductor devices for very high luminosity colliders

The objective of the CERN RD50 Collaboration is the development of radiation hard semiconductor detectors for very high luminosity colliders, particularly to face the requirements of a possible upgrade scenario of the LHC.This document reports the status of research and main results obtained after the sixth year of activity of the collaboration

Track Reconstruction with Cosmic Ray Data at the Tracker Integration Facility

The subsystems of the CMS silicon strip tracker were integrated and commissioned at the Tracker Integration Facility (TIF) in the period from November 2006 to July 2007. As part of the commissioning, large samples of cosmic ray data were recorded under various running conditions in the absence of a magnetic field. Cosmic rays detected by scintillation counters were used to trigger the readout of up to 15\,\% of the final silicon strip detector, and over 4.7~million events were recorded. This document describes the cosmic track reconstruction and presents results on the performance of track and hit reconstruction as from dedicated analyses

Search for supersymmetry with Higgs boson to diphoton decays using the razor variables at s=\sqrt{s}= 13 TeV

An inclusive search for anomalous Higgs boson production in the diphoton decay channel and in association with at least one jet is presented, using LHC proton-proton collision data collected by the CMS experiment at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The razor variables $M_\mathrm{R}$ and $\mathrm{R}^2$, as well as the momentum and mass resolution of the diphoton system, are used to categorize events into different search regions. The search result is interpreted in the context of strong and electroweak production of supersymmetric particles. We exclude bottom squark pair-production with masses below 450 GeV for bottom squarks decaying to a bottom quark, a Higgs boson, and the lightest supersymmetric particle (LSP) for LSP masses below 250 GeV. For wino-like chargino-neutralino production, we exclude charginos with mass below 170 GeV for LSP masses below 25 GeV. In the GMSB scenario, we exclude charginos with mass below 205 GeV for neutralinos decaying to a Higgs boson and a goldstino LSP with 100% branching fraction

Search for low mass vector resonances decaying into quark-antiquark pairs in proton-proton collisions at s=\sqrt{s} = 13 TeV

A search for narrow vector resonances decaying into quark-antiquark pairs is presented. The analysis is based on data collected in proton-proton collisions at $\sqrt{s} =$ 13 TeV with the CMS detector at the LHC, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The hypothetical resonance is produced with sufficiently high transverse momentum that its decay products are merged into a single jet with two-prong substructure. A signal would be identified as a peak over a smoothly falling background in the distribution of the invariant mass of the jet, using novel jet substructure techniques. No evidence for such a resonance is observed within the mass range of 50-300 GeV. Upper limits at 95% confidence level are set on the production cross section, and presented in a mass-coupling parameter space. The limits further constrain simplified models of dark matter production involving a mediator interacting between quarks and dark matter particles through a vector or axial-vector current. In the framework of these models, the results are the most sensitive to date, extending for the first time the search region to masses below 100 GeV

Search for single production of a vector-like T quark decaying to a Z boson and a top quark in proton-proton collisions at s\sqrt s = 13 TeV

A search is presented for single production of a vector-like quark (T) decaying to a Z boson and a top quark, with the Z boson decaying leptonically and the top quark decaying hadronically. The search uses data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV in 2016, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The presence of forward jets is a particular characteristic of single production of vector-like quarks that is used in the analysis. Different T quark width hypotheses are studied, from negligibly small to 30% of the new particle mass. At the 95% confidence level, the product of cross section and branching fraction is excluded above values in the range 0.27-0.04 pb for T quark masses in the range 0.7-1.7 TeV, assuming a negligible width. A similar sensitivity is observed for widths of up to 30% of the T quark mass. The production of a heavy Z' boson decaying to Tt, with T $\to$ tZ, is also searched for, and limits on the product of cross section and branching fractions for this process are set between 0.13 and 0.06 pb for Z' boson masses in the range from 1.5 to 2.5 TeV. These are the best limits to date on the single production of heavy vector-like T quarks, the first to set limits for a variety of resonance widths, and the best limits for the production of a Z' boson decaying to Tt