Cloud scores used in AquaSat to pre-filter scenes and join Landast Path Row to unique_site_inventory

Graph and download economic data for Market Hotness: Supply Score in St. Cloud, MN (CBSA) (SUSCMSA41060) from Aug 2017 to Jun 2025 about St. Cloud, score, supplies, MN, and USA.

This dataset tracks annual diversity score from 1988 to 2023 for Red Cloud High School vs. Nebraska and Red Cloud Community Schools School District

Graph and download economic data for Market Hotness: Hotness Score in St. Cloud, MN (CBSA) (HOSCMSA41060) from Aug 2017 to May 2025 about St. Cloud, score, MN, and USA.

The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. This is the MIDI Linked Data Cloud, a dataset that represents multiple collections of digital music in the MIDI standard format as Linked Data. At the time of writing, the dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI scores, and provides Web-compatible descriptions of their MIDI events.

This dataset tracks annual diversity score from 2005 to 2023 for White Cloud Elementary School vs. Michigan and White Cloud School District

Satellite imagery has several applications, including land use and land cover classification, change detection, object detection, etc. Satellite based remote sensing sensors often encounter cloud coverage due to which clear imagery of earth is not collected. The clouded regions should be excluded, or cloud removal algorithms must be applied, before the imagery can be used for analysis. Most of these preprocessing steps require a cloud mask. In case of single-scene imagery, though tedious, it is relatively easy to manually create a cloud mask. However, for a larger number of images, an automated approach for identifying clouds is necessary. This model can be used to automatically generate a cloud mask from Sentinel-2 imagery.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-2 L2A imagery in the form of a raster, mosaic dataset or image service.OutputClassified raster containing three classes: Low density, Medium density and High density.Applicable geographiesThis model is expected to work well in Europe and the United States. This model works well for land based areas. Large water bodies such as ocean, seas and lakes should be avoided.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 94 percent with L2A imagery. The table below summarizes the precision, recall and F1-score of the model on the validation dataset. The comparatively low precision, recall and F1 score for Low density clouds might cause false detection of such clouds in certain urban areas. Also, for certain seasonal clouds some extremely bright pixels might be missed out.ClassPrecisionRecallF1 scoreHigh density0.9600.9750.968Medium density0.9050.8970.901Low density0.7740.5710.657Sample resultsHere are a few results from the model.

This statistic shows the Cloud Readiness Index (CRI) in Singapore in 2020, by segment. That year, Singapore was ranked second in the Asia Pacific region with a total CRI score of ****. For the cloud regulation segment, Singapore reached a score of ****, which was also the highest score given for that segment in the Asia Pacific region.

In 2020, for the cloud regulation segment, Australia reached a Cloud Readiness Index (CRI) score of ****. Australia was ranked number ***** in the Asia Pacific region with a total CRI score of **.

This dataset tracks annual diversity score from 2021 to 2023 for Cloud City High School vs. Colorado and Lake County School District No. R-1

In 2020, the Philippines was ranked number ** in the Asia Pacific region with a total Cloud Readiness Index (CRI) score of ****. For the cloud regulation segment, the Philippines reached a score of ****, which was the **** highest score for that segment in the Asia Pacific region.

This dataset tracks annual diversity score from 1992 to 2023 for St. Cloud Elementary School vs. New Jersey and West Orange School District

Market Hotness: Hotness in St. Cloud, MN (CBSA) was 74.58194 Score in May of 2025, according to the United States Federal Reserve. Historically, Market Hotness: Hotness in St. Cloud, MN (CBSA) reached a record high of 83.44482 in March of 2025 and a record low of 9.86622 in January of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Market Hotness: Hotness in St. Cloud, MN (CBSA) - last updated from the United States Federal Reserve on July of 2025.

This Climate Data Record (CDR) provides cloud fractional cover (CFC) derived from the Meteosat Visible and InfraRed Imager (MVIRI) on board the Meteosat First Generation (MFG) and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites. The covered time period ranges from January 1983 to December 2020. Original thermal radiances were inter-calibrated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The Meteosat CFC is presented as hourly, daily and monthly composites on a 0.05°x0.05° grid covering the Meteosat disk (Africa and Europe). The CFC data is derived from two Meteosat heritage channels by use of an advanced Bayesian retrieval algorithm. It employs continuous cloud scores, which are built on a contemporaneous clear sky background inversion. The Meteosat CFC is characterized by comparability to the SYNOP-based long-term CFC observations carried out at WMO ground stations. The Meteosat CFC is therefore useful to supplement the ground-based CFC estimates in areas with low station density or high spatio-temporal CFC variability. This is a Thematic Climate Data Record (TCDR).

This statistic shows the Cloud Readiness Index (CRI) in Malaysia in 2020, by segment. That year, Malaysia was ranked number ***** in the Asia Pacific region with a total CRI score of ****. For the cloud regulation segment, Malaysia reached a score of ****, which was the *** highest score in the Asia Pacific region.

The Klout scores of the top fifteen scientists using Twitter (based on the number of Twitter followers; source: http://news.sciencemag.org/scientific-community/2014/09/top-50-science-stars-twitter) was retrieved (on 7th of February, 2015; source: https://klout.com/home) and used for ranking in ascending order (with number one corresponding to the highest Klout score).

Average quarterly park evaluation scores from Q3 FY2005 to Q4 FY2014. These scores are collected and reported pursuant to 2003's Prop C, which requires city agencies to establish and publish standards for street, sidewalk, and park maintenance. Beginning FY2015 a new methodology was developed to evaluate parks, therefore these scores should not form the basis of direct comparisons with scores reported in FY2015 and onward. FY2015 data onward is published and maintained by the SF Controller's Office.

The Credit Rating Module Software market is experiencing robust growth, driven by increasing regulatory compliance needs within the financial services sector and the rising adoption of advanced analytics for risk management. The market's expansion is fueled by the need for efficient and accurate credit risk assessment across various financial institutions, including banks, insurance companies, and credit unions. The shift towards cloud-based solutions offers scalability and cost-effectiveness, further propelling market expansion. On-premise solutions remain prevalent, particularly amongst larger institutions with stringent data security requirements. However, cloud deployment is expected to gain significant traction over the forecast period due to its flexibility and reduced IT infrastructure costs. The competitive landscape features a mix of established players and emerging technology providers, leading to innovation in areas such as AI-powered credit scoring and improved data visualization tools. Geographic expansion is also a key driver, with North America and Europe currently holding the largest market share, but the Asia-Pacific region is projected to witness significant growth, driven by rising digitalization and economic development. The projected Compound Annual Growth Rate (CAGR) suggests a consistently expanding market, although precise figures require further specification. The market segmentation by application (banks, insurance, etc.) and type (on-premise, cloud) provides valuable insights into market dynamics. Understanding the regional distribution is crucial for strategic market entry and expansion. The presence of established players like FICO and Moody's Analytics indicates a market with high barriers to entry, yet the presence of smaller, specialized companies signifies opportunities for innovation and niche market penetration. Future growth hinges on continued technological advancements, regulatory changes, and the increasing demand for sophisticated risk management solutions across the financial ecosystem. Factors such as data privacy regulations and cybersecurity concerns present potential challenges for market participants.