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 is a folder for storing the data used for the lab exercises in the geomatics courses on HydroLearn.org. These courses are based off of the Brigham Young University Geomatics class, CEEn 214, taught by Dan Ames, PhD. They are modified to more effectively fit the online formatting, while conveying the same information.

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Customer Segmentation' (including Market Segmentation), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding this fundamental marketing strategy. Contextual Overview of Customer Segmentation: 1. Definition and Context: Customer Segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into sub-groups of consumers (known as segments) based on shared characteristics. Its purpose is to enable more targeted and effective marketing strategies. A cornerstone of marketing theory and practice for decades, its application has become increasingly sophisticated with the advent of data analytics and digital marketing channels. 2. Strengths and Weaknesses: Strengths include improved marketing ROI through targeted messaging, enhanced customer understanding and satisfaction, better product development, and increased competitiveness. Weaknesses can involve the cost and complexity of data collection and analysis, difficulty in identifying meaningful and actionable segments, risk of over-segmentation or stereotyping, and challenges in implementing differentiated strategies across segments. The stability and relevance of segments can also change over time. 3. Relevance and Research Potential: Customer Segmentation remains highly relevant for personalization, targeted advertising, and value proposition design in both B2C and B2B markets. It is a foundational concept in marketing strategy and consumer behavior research. Current research opportunities include AI-driven and dynamic segmentation, behavioral segmentation based on digital footprints, ethical considerations in data-driven segmentation (e.g., fairness, privacy), and the integration of segmentation with customer journey mapping and experience design across omnichannel environments. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for Customer Segmentation. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to Customer Segmentation. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., ("customer segmentation" OR "market segmentation") AND ("marketing" OR ...)) and exact phrase matching to refine relevance. The exact query used is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Customer Segmentation' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on Customer Segmentation and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for Customer Segmentation should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.

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.

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. As of July 2007, approximately 87% of the state system network was matched to either imagery, gps or other improved data source. 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. As of July 2007, approximately 87% of the state system network was matched to either imagery, gps or other improved data source. All Census related fields have been removed, as they are not utilized nor updated by MoDOT.

model parameter

A study series on event segmentation of dynamic maps.

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

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

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 was generated by Monte Carlo simulations and contains the multimodal geometric features (24 geometric morphological variables, 3 sky model state features, and 5 dynamic threshold features corresponding to different orientations) and multidimensional utilization potential indicators (TSE, MSR, MRA, and MRE across five orientations, yielding 20 metrics in total, with TTA and TTE as process variables for calculating MRA and MRE), totaling 52 columns (approximately 198 MB). Due to the large size of the process files, direct provision is challenging. These data include the geometric information dictionaries from the 3D-GIS multi-scale dynamic segmentation (GEO+CTX, totaling about 26,000 entries and 0.53 GB), 3D building models constructed from the information dictionaries (about 26,000 models, 2 GB), a substantial volume of numerically computed high-precision, multidimensional solar radiation distributions on geometric surfaces (about 120 GB), and multidimensional metric calculation files (about 0.5 GB).

Connect with consumers more effectively using Success.ai’s Real-Time B2C Contact Data API. This API delivers continuously updated consumer data, allowing you to adapt your marketing strategies with the latest information on demographics, behaviors, and purchasing patterns. Best price guaranteed!

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.

This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.

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.

The Dynamic Lighting Products market has emerged as a pivotal segment within the lighting industry, revolutionizing the way both commercial and residential spaces are illuminated. These advanced lighting solutions, which include smart bulbs, LED fixtures, and programmable lighting systems, provide versatility and ad

It represents a common national database for the sharing of geographical data considered a priority. The priority information contained is limited to hydrography (courses and stretches of water) and traffic conditions (all main roads and railways) present on the Regional Technical Map 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 characteristics are: - hydrographic network, SiBaPo coding and coherence between information layers; - road viability, graph structure with mixed standard between GDF-1 and GDF-2 and dynamic segmentation.

This data is the road section attribute data for HPMS. The HPMS Field Manual and HPMS 8.0 identifies a record by its Data Item. This data contains approximately 70 data items that is linked to ARNOLD through a Dynamic Segmentation process using the linear referencing components. Table 4.2 contains a list of the current Data Items.

This is a feature class of measured stream routes and is a core feature class within the DNR Hydrography Dataset.

A "route" is an ESRI linear feature type created through a process called Dynamic Segmentation. Each stream route is labeled with a unique stream ID (i.e., DNR Fisheries Kittle Number) and has mile measures extending from the stream mouth (FROM_MEAS, mile=0) to headwaters (TO_MEAS, mile=total stream length). DNR Kittle Numbers follow a drainage-branching format (e.g., X-000-000-000...) and are assigned to all Fisheries-managed streams (see attribute field [VALID] = 'Y'). Additional streams have been labeled with "dummy" kittle numbers in order to provide a unique ID (VALID = 'N').

For more information about the DNR Kittle Number stream identification system, see the Fisheries Stream Survey Manual - DNR Special Report #165, Appendix 3 - page 234 (or page 251 in Adobe PDF Reader) at: http://files.dnr.state.mn.us/publications/fisheries/special_reports/165.pdf

The function of the brain is unlikely to be understood without an accurate description of its output, yet the nature of movement elements and their organization remains an open problem. Here, movement elements are identified from dynamics of walking in flies, using unbiased criteria. On one time scale, dynamics of walking are consistent over hundreds of milliseconds, allowing elementary features to be defined. Over longer periods, walking is well described by a stochastic process composed of these elementary features, and a generative model of this process reproduces individual behavior sequences accurately over seconds or longer. Within elementary features, velocities diverge, suggesting that dynamical stability of movement elements is a weak behavioral constraint. Rather, long-term instability can be limited by the finite memory between these elementary features. This structure suggests how complex dynamics may arise in biological systems from elements whose combination need not be tuned for dynamic stability.