The T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year across all four (4) service classes (F - Scheduled Passenger/ Cargo Service, G - Scheduled All Cargo Service, L - Non-Scheduled Civilian Passenger/ Cargo Service, and P - Non-Scheduled Civilian All Cargo Service). This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes passengers, arrivals, departures, freight, and mail. Data is by origin airport. Along with yearly aggregate totals for these variables, this dataset also provides more granular information for the passenger and freight variable by service class and by scheduled vs non-scheduled statistics where applicable. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2020 to 2021 for 2 comparisons:

• England: the gap between each local area or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

The tool contains data for England, English regions and upper tier local authorities.

Comprehensive YouTube channel statistics for segment, featuring 181,000 subscribers and 24,639,571 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in CZ. Track 110 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

For the third quarter of 2024, the total amount of space sector funding rounds was 94 in total. ** percent of these funding rounds in the space industry were for manufacturing infrastructure, while ** percent of the funding rounds were for launch infrastructure.

The Segment Tool provides information on the causes of death and age groups that are driving inequalities in life expectancy at local area level. Targeting the causes of death and age groups which contribute most to the life expectancy gap should have the biggest impact on reducing inequalities.

The tool provides data tables and charts showing the breakdown of the life expectancy gap in 2015 to 2017 for 2 comparisons:

• England: the gap between each local authority or region as a whole and England as a whole
• within area: the gap between the most deprived quintile of each area and the least deprived quintile of the area

This statistic represents Kubota's revenue in the fiscal year of 2022, by segment. In the fiscal year of 2022, the Japanese heavy equipment manufacturer generated over *** trillion Japanese yen from its Farm and Industrial Machinery Segment.

Comparing the segments of the industrial robotics market worldwide in 2024 regarding the volume, other industry robotics were the largest segment with around *******. The segment electric/electronic industry robotics followed in second place with approximately *******, while automotive industry robotics ranked third with about ******.

Demographic Analysis of Shopping Behavior: Insights and Recommendations

Dataset Information: The Shopping Mall Customer Segmentation Dataset comprises 15,079 unique entries, featuring Customer ID, age, gender, annual income, and spending score. This dataset assists in understanding customer behavior for strategic marketing planning.

Cleaned Data Details: Data cleaned and standardized, 15,079 unique entries with attributes including - Customer ID, age, gender, annual income, and spending score. Can be used by marketing analysts to produce a better strategy for mall specific marketing.

Challenges Faced: 1. Data Cleaning: Overcoming inconsistencies and missing values required meticulous attention. 2. Statistical Analysis: Interpreting demographic data accurately demanded collaborative effort. 3. Visualization: Crafting informative visuals to convey insights effectively posed design challenges.

Research Topics: 1. Consumer Behavior Analysis: Exploring psychological factors driving purchasing decisions. 2. Market Segmentation Strategies: Investigating effective targeting based on demographic characteristics.

Suggestions for Project Expansion: 1. Incorporate External Data: Integrate social media analytics or geographic data to enrich customer insights. 2. Advanced Analytics Techniques: Explore advanced statistical methods and machine learning algorithms for deeper analysis. 3. Real-Time Monitoring: Develop tools for agile decision-making through continuous customer behavior tracking. This summary outlines the demographic analysis of shopping behavior, highlighting key insights, dataset characteristics, team contributions, challenges, research topics, and suggestions for project expansion. Leveraging these insights can enhance marketing strategies and drive business growth in the retail sector.

References OpenAI. (2022). ChatGPT [Computer software]. Retrieved from https://openai.com/chatgpt. Mustafa, Z. (2022). Shopping Mall Customer Segmentation Data [Data set]. Kaggle. Retrieved from https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data Donkeys. (n.d.). Kaggle Python API [Jupyter Notebook]. Kaggle. Retrieved from https://www.kaggle.com/code/donkeys/kaggle-python-api/notebook Pandas-Datareader. (n.d.). Retrieved from https://pypi.org/project/pandas-datareader/

This dataset contains synthetic and real images, with their labels, for Computer Vision in robotic surgery. It is part of ongoing research on sim-to-real applications in surgical robotics. The dataset will be updated with further details and references once the related work is published. For further information see the repository on GitHub: https://github.com/PietroLeoncini/Surgical-Synthetic-Data-Generation-and-Segmentation

GisGeoDbPres.MAPBASE.RoadSegmentDetails

Compact sport utility vehicles (SUVs) were most commonly sold in Europe: these types of SUV accounted for about 20 percent of vehicles sold here in 2021. Overall, SUVs across types had a market share over 45 percent that same year. Furthermore, European motorists purchased two million small cars in 2021.

Manipulating cluttered cables, hoses or ropes is challenging for both robots and humans. Humans often simplify these perceptually challenging tasks by pulling or pushing tangled cables and observing the resulting motions. We would like to build a similar system -- in accordance with the interactive perception paradigm -- to aid robotic cable manipulation. A cable motion segmentation method that densely labels moving cable image pixels is a key building block of such a system. We present MovingCables, a moving cable dataset, which we hope will motivate the development and evaluation of cable motion (or semantic) segmentation algorithms. The dataset consists of real-world image sequences automatically annotated with ground truth segmentation masks and optical flow.

The segment counts by social group and species or species group for the Waterfowl Breeding Population and Habitat Survey and associated segment effort information. Three data files are included with their associated metadata (html and xml formats). Segment counts are summed counts of waterfowl per segment and are separated into two files, described below, along with the effort table needed to analyze recent segment count information. wbphs_segment_counts_1955to1999_forDistribution.csv, which represents the period prior the collection of geolocated data. There is no associated effort file for these counts and segments with zero birds are included in the segment counts table, so effort can be inferred; there is no information to determine the proportion of each segment surveyed for this period and it must be presumed they were surveyed completely. Number of rows in table = 1,988,290. wbphs_segment_counts_forDistribution.csv, which contains positive segment records only, by species or species group beginning with 2000. wbphs_segment_effort_forDistribution.csv file is important for this segment counts file and can be used to infer zero value segments, by species or species group. Number of rows in table = 365,863. wbphs_segment_effort_forDistribution.csv. The segment survey effort and _location from the Waterfowl Breeding Population and Habitat Survey beginning with 2000. If a segment was not flown, it is absent from the table for the corresponding year. Number of rows in table = 65,122. Also included here is a small R code file, createSingleSegmentCountTable.R, which can be run to format the 2000+ data to match the 1955-1999 format and combine the data over the two time periods. Please consult the metadata for an explanation of the fields and other information to understand the limitations of the data.

Dataset

This dataset was created by AmitH2022

Released under Apache 2.0

Contents

Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training. SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.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 SamLoRA architecture in ArcGIS. Follow the guide and refer to this sample notebook to fine-tune this model.Input8-bit, 3-band imagery.OutputFeature class containing masks of various objects in the image.Applicable geographiesThe model is expected to work globally.Model architectureThis model is based on the open-source Segment Anything Model (SAM) by Meta.Training dataThis model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.

The Air Carrier Statistics database, also known as the T-100 data bank, contains domestic and international airline market and segment data. certificated U.S. air carriers report monthly air carrier traffic information using Form T-100. Foreign carriers having at least one point of service in the United States or one of its territories report monthly air carrier traffic information using Form T-100(f). The data is collected by the Office of Airline Information, Bureau of Transportation Statistics, Research and Innovative Technology Administration.

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The machine learning market segmentation report helps the market vendors with information about the geographical segmentation of this market.

With 38%, North America has the highest market share of the machine learning market. One of the key factors that will drive the market growth is increasing adoption of cloud-based offerings. Growth is organized retail distribution channel will drive the growth of this market in the North America region. To gather more information regarding the geographical landscape distribution, click here for a free sample report.

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Smartwatch Market research indicates that the global market can be primarily differentiated into Type (Integrated and Standalone), Market Landscape (watchOS, Tizen, Wear OS, and others), and Geography (North America, APAC, Europe, South America, and MEA). The smartwatch market has been analyzed on various dimensions, and the market segments have been reviewed both qualitatively and quantitatively.

Smartwatch Market Segmentation by TypeIntegratedStandaloneSmartwatch Market Segmentation by Market LandscapewatchOSTizenWear OSothersSmartwatch Market Segmentation by GeographyNorth AmericaAPACEuropeSouth AmericaMEA

Segmentation of the adult England population with interactive tool and raw data to help understand where different types of people are located and how to reach them. Postcode level data with segment counts available to download. Youth segmentation is being developed and will be added to this tool in autumn 2013