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In this paper, we report the outcomes of the 2015 data fusion contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society. As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource studies. The 2015 edition of the contest proposed a multiresolution and multisensorial challenge involving extremely high resolution RGB images (with a ground sample distance of 5 cm) and a 3-D light detection and ranging point cloud (with a point cloud density of approximatively 65 pts/m$^2$). The competition was framed in two parallel tracks, considering 2-D and 3-D products, respectively. In this Part B, we report the results obtained by the winners of the 3-D contest, which explored challenging tasks of road extraction and ISO containers identification, respectively. The 2-D part of the contest and a detailed presentation of the dataset are discussed in Part A. [ABSTRACT FROM PUBLISHER]

Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. Recent works showcase the necessity to improve cloud detection methods for imagery acquired by the Sentinel-2 satellites. However, the lack of consensus and transparency in existing reference datasets hampers the benchmarking of current cloud detection methods. Exploiting the analysis-ready data offered by the Copernicus program, we created CloudSEN12, a new multi-temporal global dataset to foster research in cloud and cloud shadow detection. CloudSEN12 has 49,400 image patches, including (1) Sentinel-2 level-1C and level-2A multi-spectral data, (2) Sentinel-1 synthetic aperture radar data, (3) auxiliary remote sensing products, (4) different hand-crafted annotations to label the presence of thick and thin clouds and cloud shadows, and (5) the results from eight state-of-the-art cloud detection algorithms. At present, CloudSEN12 exceeds all previous efforts in terms of annotation richness, scene variability, geographic distribution, metadata complexity, quality control, and number of samples., (© 2022. The Author(s).)

ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation ( Val ) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2-Validation-accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

ESA defines as Earth Observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation ( Val ) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006-2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1-Theory provides the multidisciplinary background of a priori color naming. The subsequent Part 2-Validation accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.

Spatiotemporal analytics of multi-source Earth observation (EO) big data is a pre-condition for semantic content-based image retrieval (SCBIR). As a proof of concept, an innovative EO semantic querying (EO-SQ) subsystem was designed and prototypically implemented in series with an EO image understanding (EO-IU) subsystem. The EO-IU subsystem is automatically generating ESA Level 2 products (scene classification map, up to basic land cover units) from optical satellite data. The EO-SQ subsystem comprises a graphical user interface (GUI) and an array database embedded in a client server model. In the array database, all EO images are stored as a space-time data cube together with their Level 2 products generated by the EO-IU subsystem. The GUI allows users to (a) develop a conceptual world model based on a graphically supported query pipeline as a combination of spatial and temporal operators and/or standard algorithms and (b) create, save and share within the client-server architecture complex semantic queries/decision rules, suitable for SCBIR and/or spatiotemporal EO image analytics, consistent with the conceptual world model.

Background: Strategies of emergency care in the treatment of ST-segment elevation myocardial infarction (STEMI) have evolved rapidly over the past two decades to include primary percutaneous coronary intervention (PPCI) when possible. Most U.S.-based transfer programs still use complicated protocols that include fibrinolytic therapy often resulting in transfer delays, inappropriately applied therapy (wrong diagnosis) and bleeding and stroke complications. These protocols are often emphasized in low-volume centers. We implemented a program absent fibrinolytic therapy and applied it to a network of 25 participating hospitals over a 100-mile radius in central Minnesota., Methods and Results: One-thousand consecutive patients ages 21 to 90 who presented within 12 hours of the onset of symptoms consistent with MI from April, 2004 to January, 2008 were included in this registry. Prior to transfer to the cardiac catheterization laboratory, patients received aspirin and heparin. Clopidogrel was added to the protocol in January, 2007. Glycoprotein (GP) IIb/IIIa inhibitors were typically utilized after diagnostic catheterization and prior to PPCI. Median door-to-balloon time was 56 minutes at the PCI Center and 110 minutes from referral sites (RS). Of the transfer patients, 71% underwent helicopter transfer. The success rate for PPCI was 99.4%. Despite inherent transfer delays, there was no difference in mortality between the PCI Center and RS. Overall mortality rates in-hospital, at 30 days, at 6 months, and 1 year were 2.1%, 2.9%, 3.8% and 4.5%, respectively, with follow up on 998 of 1,000 patients. In-hospital stroke, reinfarction and major bleeding were 0.7%, 2.0% and 2.7%, respectively., Conclusions: Despite increasing trends toward a pharmacoinvasive approach in transfer patients with STEMI, a protocol which stresses rapid transfer and PPCI results in excellent outcomes, with very low complication rates without fibrinolytic therapy.

The tetrapyrrole electron donors and acceptors (bacteriochlorophyll, BCh; bacteriopheophytin, BPh) within the bacterial photosynthetic reaction center (RC) are arranged with a specific geometry that permits rapid (picosecond time scale) electron tunneling to occur between them. Here we have measured the angle between the molecular planes of the bacteriochlorophyll dimer (primary donor), B(2), and the acceptor bacteriopheophytin, H, by analyzing the dichroism of the absorption change associated with H reduction, formed by photoselection with RCs of Rhodopseudomonas viridis. This angle between molecular planes is found to be 60 degrees +/- 2. This means that the ultrafast electron tunneling must occur between donors and acceptors that are fixed by the protein to have a noncoplanar alignment. Nearly perpendicular alignments have been determined for other electron tunneling complexes involving RCs. These geometries can be contrasted with models proposed for heme-heme electron transfer complexes, which have emphasized that mutually parallel orientations should permit the most kinetically facile transfers.