In total 50 cases segmented with liver segments 1-8.Free to use and download. Check out our 5 segment model at www.medseg.aiAll cases obtained Decathlon's dataset, see details and reference here: https://arxiv.org/abs/1902.09063Segmentations done by MedSeg#Update 2/4/21: 40 new cases added, case 1 replaced with new case

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 number of users is forecast to experience significant growth in all segments in 2030. Comparing the three different segments for the year 2030, the segment 'Event Tickets' leads the ranking with 863.61 million users. Contrastingly, 'Dating Services' is ranked last, with 478.51 million users. Their difference, compared to Event Tickets, lies at 385.1 million users. Find other insights concerning similiar markets and segments, such as a comparison of revenue in Russia and a comparison of revenue in Germany.

Yolo Segment

## Overview

Yolo Segment is a dataset for instance segmentation tasks - it contains Test annotations for 7,356 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The detrimental effects of excess nutrients and sediment entering the Chesapeake Bay estuary from its watersheds have necessitated regulatory actions. Federally-mandated reductions are apportioned to bay jurisdictions based on the U.S. Environmental Protection Agency's Chesapeake Bay Time-Variable Watershed Model (CBPM). The Chesapeake Assessment Scenario Tool (CAST version CAST-19; cast.chesapeakebay.net; Chesapeake Bay Program, 2020) is a simplified, on-line version of the Phase 6 CBPM that simulates watershed nutrients delivery to the estuary using the original model's annual land-surface nutrient source and removal inputs and time-averaged climatological forecasting. Because it runs much faster than the CBPM, CAST facilitates rapid generation and comparison of alternate input reduction scenarios. The purpose of this data release is to make the baseline annual nitrogen, phosphorus, and sediment input data used by CAST available to the scientific community in a standardized, public-domain format, such that CBPM baseline predictions can be corroborated, or the model can be refined through independent scientific investigations. Because it constitutes the best available estimate, as of 2019, of past and projected future land-surface nitrogen, phosphorus, and sediment inputs over the entire extent of the Chesapeake watershed, this data set also supports broader USGS Chesapeake Bay Studies through fiscal year 2025. Source-specific annual nutrient source and removal inputs for years 1985 through 2025 were downscaled from the CBPM land-river segment scale (2,049 segments; mean area 118 square kilometers) to the National Hydrography Dataset Plus version 2.0 (NHDPlus) 1:100,000 catchment scale (83,331 segments, mean area 2.1 square kilometers). Eleven source or removal categories are represented for all counties that intersect the Chesapeake Bay watershed. These categories are listed below and further defined in the Purpose section. 1. Atmospheric deposition (atm. dep.) 2. Biosolids 3. Combined sewer overflow (CSO) 4. Direct deposit (manure directly excreted on pasture and in streams) 5. Fertilizer 6. Manure applied as fertilizer 7. Nitrogen fixation by agricultural crops (Nfix) 8. Rapid infiltration basins (RIB) 9. Septic systems 10. Nutrient uptake by agricultural crops that is removed from the field 11. Wastewater For most of these categories, nutrient source and removal inputs are tabulated for five species: ammonia, nitrate, organic nitrogen, phosphate, and organic phosphorus; sediment inputs are provided as total suspended sediment. Consistent with CBPM, plant uptake is specified only as total nitrogen and total phosphorus, and wastewater inputs are specified as biological oxygen demand and dissolved oxygen (Chesapeake Bay Program, 2020). In addition to these sources, annual proportional land-use layers used in the downscaling process are provided, also at NHDPlus 1:100,000 scale. Layers for each year represent proportional coverage of 14 Chesapeake Bay 2013 1-meter Land Use Data classes, interpolated (1985-2013) based on evolution of land-cover derived from NLCD 1992, 2001, 2006, and 2011 layers, and projected (2014-2025) using land use estimated for 2025 using the USGS Chesapeake Bay Land Change model (USGS, 2020). Best management practices (BMPs) are not included in this data release. BMPs have varying effects on nutrient inputs and runoff. These effects are best represented in CAST. Moreover, the BMP history is regularly revised by the states and the most current history is available as a downloadable file from CAST. Chesapeake Bay Program, 2020. Chesapeake Assessment and Scenario Tool (CAST) Version 2019. Chesapeake Bay Program Office, Last accessed November 2021.

Segment

## Overview

Segment is a dataset for instance segmentation tasks - it contains 1 2 3 4 annotations for 252 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

In the fiscal year of 2023, AutoZone's net sales amounted to 17.46 billion U.S. dollars, 98 percent of which was accounted for by the net sales from the Auto Part Stores segment. AutoZone is a leading distributor of automotive replacement parts and accessories in North America.

The stream geomorphic assessment (SGA) is a physical assessment competed by geomorphologists to determine the condition and sensitivity of a stream. The SGA Phase 2 Segment Breaks are points that indicate where the Phase 1 SGA reach was "segmented" into smaller Phase 2 segments. These segments are determined in the field and are based on changes in topography, slope and valley setting that were not found in phase 1, and on changes in condition found in the field. Where there found in the field a significant change in any of the above there is a segment break created.

GTRN_SEGMENT_PT: Contains points representating of the end points of Facility Asset Management System (FAMS) segments. Publication transportation dataset showing both BLM inventoried and non-inventoried roads in Oregon & Washington. This data does not include highways. For highway data see the citation in the Cross Reference Section.

The Advanced Very High Resolution Radiometer (AVHRR) 1-km Orbital Segments data set is a component of the National Aeronautics and Space Administration (NASA) AVHRR Pathfinder Program and contains global coverage of land masses at 1-kilometer resolution. The data set is the result of an international effort to acquire, process, and distribute AVHRR data of the entire global land surface to meet the needs of the international science community. The orbital segments are comprised of raw AVHRR scenes consisting of 5-channel, 10-bit, AVHRR data at 1.1-km resolution at nadir. The raw data are used to produce vegetation index composites; to support fire detection and cloud screening activities; to support research in atmospheric correction; to develop algorithms; and to support a host of research activities that may require the inclusion of raw AVHRR data.

This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

GisGeoDbPres.MAPBASE.RoadSegmentDetails

This deep learning model is used to detect and segment trees in high resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.This deep learning model is based on DeepForest and has been trained on data from the National Ecological Observatory Network (NEON). The model also uses Segment Anything Model (SAM) by Meta.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 cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band high-resolution (10-25 cm) imagery.OutputFeature class containing separate masks for each tree.Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model is based upon the DeepForest python package which uses the RetinaNet model architecture implemented in torchvision and open-source Segment Anything Model (SAM) by Meta.Accuracy metricsThis model has an precision score of 0.66 and recall of 0.79.Training dataThis model has been trained on NEON Tree Benchmark dataset, provided by the Weecology Lab at the University of Florida. The model also uses Segment Anything Model (SAM) by Meta that is trained on 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.CitationsWeinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071

This dataset shows polygon locations of the road segments inventoried by the City of Perth. The road segment data was collected from as constructed drawings and old survey maps. At least 95% of the locational data for the road segment is accurate to within a few meters. Some errors and/or duplicate data may exist.

the precise location and geometry of oceanic spreading centers and associated transform faults or discontinuities' boundary has fundamental implications in our understanding of oceanic accretion, the accommodation of deformation around rigid lithospheric blocks, and the distribution of magmatic and volcanic processes. the now widely used location of mid oceanic ridges worldwide, published by p. bird in 2003, can be updated based on recent publicly available and published ship-based multibeam swath bathymetry data (100-m resolution or better), now available to ~25% of the ocean seafloor, but covering a significant proportion of the mid-ocean ridge system (>70%).here we publish the mapridges database built under the coordination of cgmw (commission for the geological map of the world), with a first version v1.0 (06/2024) that provides high resolution and up-to-date datasets of mid-ocean ridge segments and associated transform faults, and follow-up updates that will also include non-transform offsets.the detailed mapping of individual mid oceanic ridge segments was conducted using gmrt (ryan et al., 2009) (version 4.2 for mapridges v1.0), other publicly available datasets (e.g., ncei, pangaea, awi), and existing literature. mapridges will be revised with the acquisition of additional datasets, new publications, and correction of any errors in the database.the mapridge database was built in a gis environment, where each feature holds several attributes specific to the dataset. we include three different georeferenced shapefile layers: 1) ridge segments, 2) transform faults, and 3) transform zones. the latest corresponds to zones of distributed strike-slip deformation that lack a well-defined fault localizing strain, but that are often treated as transform faults.1) the ridge segments layer contains 1461 segments with 9 attributes: area_loca: the name of the ridge system loc_short: the short form of the ridge system using 3 characters lat: the maximum latitude of the ridge segment long: the maximum longitude of the ridge segment length: the length of the ridge segment in meters confidence: the degree of confidence on digitization based on the availability of high-resolution bathymetry data: 1 = low to medium confidence, 2 = high confidence references: supporting references used for the digitization name_code: unique segment code constructed from the loc_short and lat attributes in degree, minute, second coordinate format name_lit: name of the segment from the literature if it exists2) the transform fault layer contains 260 segments with 4 attributes: name_tf: name of the transform fault according to the literature length: length of the transform fault in meters lat: the maximum latitude of the fault segment long: the maximum longitude of the fault segment3) the transform zone layer contains 10 segments with 4 attributes: name_tf: name of the transform zone according to the literature length: length of the transform fault in meters lat: the maximum latitude of the fault segment long: the maximum longitude of the fault segmentto facilitate revisions and updates of the database, relevant information, corrections, or data could be sent to b. sautter (benjamin.sautter@univ-ubs.fr) and j. escartín (escartin@geologie.ens.fr).

The fastest growing segment within the global motion control market will be electromechanical actuation technologies. This market segment is expected to grow at a compound annual growth rate (CAGR) of around 8.3 percent between 2017 and 2023.

In the fiscal year of 2021, Emerson reported sales of around 11.6 billion U.S. dollars from its automation solutions segment. This represents a decrease of some four percent compared with the previous year.

This statistic represents worldwide automotive revenue in 2030, by segment. Globally, shared mobility services are expected to generate around 1.4 trillion U.S. dollars in revenue by 2030. That year, the market for car data-enabled services is predicted to grow to between 450 billion and 750 billion U.S. dollars. The industry is expected to generate around four trillion U.S. dollars in revenue from vehicle sales, up from around 2.75 trillion U.S. dollars today.

The number of smart homes is forecast to experience significant growth in all segments in 2028. This reflects the overall trend throughout the entire forecast period from 2020 to 2028. It is estimated that the indicator is continuously rising in all segments. In this regard, the Control & Connectivity segment achieves the highest value of 115.07 million users in 2028. Find other insights concerning similar markets and segments, such as a comparison of user penetration in Norway and a comparison of average revenue per unit (ARPU) in France. The Statista Market Insights cover a broad range of additional markets.

Cable telecommunications company Charter Communications generated the majority of its revenue from its video and internet segments, which brought in 17.6 and 21.1 billion U.S. dollars respectively in 2021. Charter's mobile revenue grew significantly from 2019 to 2021, amounting to 2.18 billion U.S. dollars in 2021 compared to 1.36 million in the previous year.