Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution remote sensing imagery for dynamic monitoring of planet Earth, with a wide variety of uses. Crop monitoring is a typical application, which involves timely gathering of the information of crop types, boundaries, and dynamic changes during the whole crop growth period. However, most of the existing datasets and benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop type by using satellite image time series (SITS), where the individual boundaries (parcels) and the dynamic changes of the crop cannot be obtained, due to the limited spatial resolution and the lack of multi-season annotation. In this paper, a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1 m) crop semantic segmentation to instance segmentation and dynamic segmentation. Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS (10 m) images, with 45 crop types and 101k instance annotations, covering a 26,000 km2 area of California in the U.S. M3CropSeg has various challenges, including M3 data fusion, class imbalance, fine-grained classification, and multilabel classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain high-resolution pixel-level, parcel-level, and multi-season crop types, respectively. The corresponding benchmarks are also provided to address the above challenges, along with a variety of experimental analyses.

This dataset supports the study on "Dynamic Segmentation of the Sagaing Fault."

description: The MoDOT roadway coverage is based on a Dynamic Segmentation model. Dynamic Segmentation models linear features using routes and events, associating multiple sets of attributes to any portion of a linear feature. The ARC feature class forms the basic linear infrastructure from which Dynamic Segmentation models or route-systems are built. NODES indicate the endpoints and intersections of ARCS. The 'from-node' is the first vertex of the arc; the 'to-node' is the last vertex. Together, they define the direction of the arc. Arc-node topology defines connectivity of Dynamic Segmentation models or route-systems. Route systems are built without modifying the underlying arc-node topology. A route represents a single linear feature, such as a city street or highway. Routes are linear features composed of one or more arcs or parts of an arc; for example, a highway may be composed of a number of connected arcs. A route, for Dynamic Segmentation purposes, is simply a linear feature with measure values attached to it. Each route is associated with a measurement system, a linear method consisting of a starting value and other measures along the route, which describe distance along them. The measures are used to locate data that describe parts of the route. Data along routes is modeled using events. Event data, such as accidents, signs, functional classification, pavement condition, pavement type, is based on the Dynamic Segmentation model, the foundation of which is the route system. The state system network (IS, US, MO, RT) originated from 1980-DIME data, which was later updated with 1995 TIGER files from the US Census Bureau. Currently the coverage is updated monthly utilizing design plans, GPS, aerial imagery and county/city maps. All Census related fields have been removed, as they are not utilized nor updated by MoDOT.; abstract: The MoDOT roadway coverage is based on a Dynamic Segmentation model. Dynamic Segmentation models linear features using routes and events, associating multiple sets of attributes to any portion of a linear feature. The ARC feature class forms the basic linear infrastructure from which Dynamic Segmentation models or route-systems are built. NODES indicate the endpoints and intersections of ARCS. The 'from-node' is the first vertex of the arc; the 'to-node' is the last vertex. Together, they define the direction of the arc. Arc-node topology defines connectivity of Dynamic Segmentation models or route-systems. Route systems are built without modifying the underlying arc-node topology. A route represents a single linear feature, such as a city street or highway. Routes are linear features composed of one or more arcs or parts of an arc; for example, a highway may be composed of a number of connected arcs. A route, for Dynamic Segmentation purposes, is simply a linear feature with measure values attached to it. Each route is associated with a measurement system, a linear method consisting of a starting value and other measures along the route, which describe distance along them. The measures are used to locate data that describe parts of the route. Data along routes is modeled using events. Event data, such as accidents, signs, functional classification, pavement condition, pavement type, is based on the Dynamic Segmentation model, the foundation of which is the route system. The state system network (IS, US, MO, RT) originated from 1980-DIME data, which was later updated with 1995 TIGER files from the US Census Bureau. Currently the coverage is updated monthly utilizing design plans, GPS, aerial imagery and county/city maps. All Census related fields have been removed, as they are not utilized nor updated by MoDOT.

A study series on event segmentation of dynamic maps.

A collection of miscellaneous geospatial data from Utah to test GeoServer capabilities.

model parameter

This coverage contains points representing tenth of miles in the GCMRC river mile system. The points fall along the centerline of the Colorado River from Glen Canyon Dam to the headwaters of Lake Mead. The points were generated from exported nodes from the "centerline" tenth mile route. The GCMRC river mileage was cross-checked with the traditional river guides and always fell within one mile of the guides, usually cooresponding within a half mile. River Mile 0 was measured from the USGS concrete gage and cablewa at Lees Ferry with negative river mile numbers used in Glen Canyon and positive river mile numbers downstream in Marble and Grand Canyons as per the Colorado River Compact of 1922.

This Route feature class is CDOT's representation of state highways. It can be used as the basis for dynamic segmentation, general reference, geoprocessing, and cartography.

This dataset is an LRS event to be used in conjunction with the base LRS geometries at https://data.winnipeg.ca/d/jwfi-vjqw/data, the Location column provided is a dynamic segmentation of the event based on the LRS geometries.

This event provides information on the street classifications, address ranges, number of lanes, and direction of City of Winnipeg streets.

Values for the Oneway column are as follows: 0 – The street is a two-way street 1 – The one-way direction is opposite the digitized direction 2 – The one-way direction is the same as the digitized direction

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The Sky Segmentation Dataset is meticulously curated for the visual entertainment industry, featuring manually captured images with resolutions varying from 937 × 528 to 9961 × 3000. This collection is dedicated to the segmentation of skies across different times of the day and night, providing a dynamic range of outdoor sky scenarios for comprehensive mask segmentation tasks.

Supplemental Datasets for Dynamont: Segmentation of the ONT signal with Dynamic Programming and HMMs

Contains RNA002 datasets from (10,000 randomly selected reads each):

• H. sapiens
• E. coli
• SARS-CoV-2
• IVT

Contains RNA004 datasets from (10,000 randomly selected reads each):

• H. sapiens
• S. cerevisiae
• CEVd
• IVT

Contains DNA R10.4.1 5kHz datasets from (10,000 randomly selected reads each):

• H. sapiens
• Zymo HMW
• S. aureus
• P. anserina

Some datasets derived from external sources and studies, links can be found below or in the publication of Dynamont.

We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments S at a given error level, which serves as a tuning parameter. For a proper choice of this parameter, this probability tends exponentially fast to one, as sample size increases. We further show that the location and size of segments are estimated at minimax optimal rate (compared to a Gaussian setting) up to a log-factor. Thereby, our approach leads to (asymptotically) uniform confidence bands for the entire quantile regression function in a fully nonparametric setup. The procedure is efficiently implemented using dynamic programming techniques with double heap structures, and software is provided. Simulations and data examples from genetic sequencing and ion channel recordings confirm the robustness of the proposed procedure, which at the same time reliably detects changes in quantiles from arbitrary distributions with precise statistical guarantees. Supplementary materials for this article are available online.

Dynamic panel regression results.

SDE_VDOT_RTE_OVERLAP_LRS - Used directly within RNS for dynamic segmentation and on local maps to show overlapping route name labels. Abstract: This feature class consists of approximately 492,000 features representing over 70,000 miles of Interstate, Primary, Secondary and Urban roads throughout the State of Virginia. The Linear Referencing System is based on the Virginia Department of Transportation's Source System of Record for road inventory, Roadway Inventory Management System (RIMS).Geometry and Attribution: The Linear Referencing System (LRS) data contained within this feature class provides dissolved route segmentation (i.e. routes are not segmented when they intersect other routes), thus rendering one table record per route. In cases where routes are noncontiguous (e.g. a valid physical gap exists), multi-part geometry is created. The feature class also depicts overlapping routes (e.g. Interstate 64 and Interstate 95) as two separate collinear features, one on top of the other. Routes built in the prime and non-prime directions are included. Each road centerline record has a master route record assigned. This feature class is best suited for labeling roadways when overlap reference is needed.Measures: The linear reference is based on Official State Mileage (OSM) as derived from reference points at Roadway Inventory Management System (RIMS) roadway intersections (i.e. nodes/junctions). Purpose: This linear referenced data layer represents roadways that are maintained by the Virginia Department of Transportation and provides the underlying spatially enabled geometric network to which all "events" (e.g. potholes, pavement type, vehicle accidents, traffic counts, culverts, etc...) can be located.

It represents a common national database for the sharing of geographical data considered a priority. The priority information contained is limited to the road system (all main roads and railways) present on the Regional Technical Paper at a scale of 1:10,000. It is a database with regional coverage created by the Interregional Center according to the data model and the Technical Specifications (N1005) of the State-Regions Agreement. Produced in shp format, starting from CTRN 10k data, it is limited to only "urgent information levels" relating to traffic and hydrography. Structural features are: - road conditions, graph structure with mixed standard between GDF-1 and GDF-2 and dynamic segmentation

One of the important challenges faced by humans in developing countries is the decline of soil conditions. The soil is so poor that it creates landslides easily and is a significant danger to the citizens of these countries. These landslide zones due to poor soil affect more than 300 million hectares of land. Furthermore, the citizens of these countries do not have enough resources to invest in soil restoration. (D Flanagan, 2002). The soil degradation is highly affected by rainfall which triggers the overland flow and soil erosion processes. (de Lima and Singh, 2002). Considering the above, we are proposing to develop an APP to identify zones that consider slope, protected forested areas, and proximity to bodies of water. Through these inputs we can decide which areas in developing countries should have additional forests . Adding Forests can mitigate the problems with the soil previously described and help comply with basic environmental standards. Forestation can be a cost effective solution for the countries that cannot afford to remove / improve the soil with machinery or retaining walls.

This dataset presents different abdominal metrics including visceral area, abdominal muscles lengths, thicknesses, radial displacements, radial and circumferentials strains. The considered abdominal muscle groups are rectus abdominis and lateral muscles. This dataset was derived from the segmentation masks of dynamic MRI of twenty healthy participants performing three different exercises: breathing, coughing and the Valsalva maneuver (corresponding dataset: https://doi.org/10.57745/CTM9BO). Specific timepoints (corresponding dataset: https://doi.org/10.57745/JLI4DP) were used to compute the current dataset. Please look at the README file for more informations.

The image segmentation market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and deep learning across diverse sectors. The market, currently estimated at $10 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $50 billion by 2033. This substantial expansion is fueled by several key factors. The surging demand for advanced medical imaging techniques, enabling precise disease diagnosis and treatment planning, is a significant contributor. Furthermore, the autonomous vehicle industry's reliance on accurate object recognition and scene understanding necessitates high-performance image segmentation solutions. The growth in smart cities initiatives, coupled with advancements in satellite imaging for urban planning and environmental monitoring, further boosts market demand. Manufacturing and agriculture also benefit from image segmentation, with applications ranging from quality control and defect detection to precision farming and crop monitoring. Semantic, instance, and panoptic segmentation techniques cater to varying application needs, contributing to market diversification. While data privacy and security concerns present challenges, ongoing technological advancements in AI model optimization and improved computational efficiency are mitigating these restraints. Major players such as IBM, Google, Microsoft, NVIDIA, and others are actively investing in research and development, fueling innovation and competition within the image segmentation landscape. Geographic expansion is also a defining characteristic, with North America and Europe currently dominating the market due to early adoption and robust technological infrastructure. However, Asia Pacific is projected to witness the fastest growth rate in the coming years, driven by burgeoning economies and increasing government investments in AI-driven initiatives. This regional shift presents lucrative opportunities for market entrants and established players alike. The continuing evolution of deep learning architectures, coupled with the development of more efficient and robust algorithms, ensures that the image segmentation market will remain a dynamic and high-growth sector for the foreseeable future.