This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

This is a guide that describes how to interact with pop ups and the attribute tables in web maps where that functionality is available. Not all widgets or functionality is available in every web map.

Resilience attribute table.

biosample attributes table (metadata)Sequencing raw data deposited at SRA of NCBI (SRP308455)

Building polygons were created in February 2013 by Geoscience Australia by manually digitising the outline of each building off the 2011 orthophotography. Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was extremely time consuming - only the height_max field was significantly improved. Disclaimer

Plot attribute table of the QGIS database of the Kunbaja online resource model as dataset in the .xlsx file format

List of the main variables characterizing synthetic populations as well as their type and the categories created for their implementation in POPGEN.

Attribute tables are an essential part of working with GIS. In addition to the spatial element, feature classes will have additional data associated to them which can be viewed within the attribute table.To open an attribute table...Right click a layer within the contents paneClick 'Attribute Table'.

Product Attribute Extraction Challenge 🚀

Challenge Overview

Transform unstructured product descriptions into structured, searchable data using NLP and machine learning techniques. This challenge focuses on building robust systems that can automatically extract key product attributes from text descriptions, enabling better e-commerce search experiences and product recommendations.

🎯 Challenge Objective

Develop an automated system that can extract structured attribute-value pairs from product descriptions. Your solution should handle various product categories and maintain high accuracy across different writing styles and formats.

Core Attributes to Extract

📊 Dataset Details

• Training Dataset: data.csv (20,000 product entries)
• Test Dataset: 100 hidden product descriptions
• File Format: CSV with the following columns:
  • product_id: Unique identifier
  • product_name: Product title
  • description: Detailed product description
  • brand_name: Brand/manufacturer name

Sample Data Entry json { "product_id": "P1234", "product_name": "Premium Athletic Running Shoes", "description": "Experience ultimate comfort with these premium running shoes. Features breathable mesh upper and responsive cushioning. Available in midnight blue color. Size: UK 8, US 9. Material: Synthetic mesh with rubber sole. Weight: 280g per shoe. Ideal for: Professional runners, Price: Rs. 3499", "brand_name": "SportFlex" }

Expected Output: json { "color": "midnight blue", "size": { "uk": "8", "us": "9" }, "material": ["synthetic mesh", "rubber"], "weight": { "value": 280, "unit": "g" }, "price": { "currency": "Rs", "value": 3499 }, "target_audience": "professional runners", "category": "running shoes" }

🔍 Evaluation Metrics

1. Primary Metrics

• **Attribute Extraction Accuracy **

  • Precision: Correctness of extracted attributes
  • Recall: Completeness of extraction
  • F1 Score: Harmonic mean of precision and recall

• **Value Normalization **

  • Unit standardization (e.g., converting "2.2 pounds" to "1 kg")
  • Value format consistency (e.g., color names, size formats)

2. Secondary Metrics

• **Coverage Score **

  • Percentage of mandatory attributes extracted
  • Handling of missing or implicit attributes

• **Processing Efficiency **

  • Time taken to process the test dataset
  • Resource utilization

🛠️ Submission Guidelines

1. Submit a Github link for the code.
2. Include documentation explaining your approach
3. Provide requirements.txt or environment.yml

A route feature stores the spatial locations (geography) of the road. These feature classes have an (M) value or measure on their vertices. A route system depicts all roads within or in close proximity to an administrative unit. A road is a motor vehicle travel way over 50 inches wide, unless classified and managed as a trail. This feature is only SPATIAL ROAD DATA, other data (open, closed, jurisdiction, maintenance level) is stored in INFRA. Used to link spatial roads to INFRA data, ROAD NO., BMP, EMP and Calibration. Routed roads are a single spatial line, all have data in INFRA and this data must be attached. Routed roads need to have INFRA data table attached by use of R10 Geospatial Interface (GI) tool and Visualization named Roads with Core Attributes RSW - This creates an output roads layer and adds the following fields from the INFRA database at NITC: name, lanes, service life, system, surface type, jurisdiction, objective maintenance level, operational maintenance level, route status, functional class and primary maintainer. Routed ROADS CAN HAVE OTHER DATA TABLES ATTACHED, (R10 Stream Data Point-RSW, Road Points -RSW, Bridges-RSW, MVUM Roads and Transportation Atlas. A road may be classified or unclassified. Classified roads are roads within the National Forest System lands planned and managed for motor vehicle access including State roads, county roads, private roads, permitted roads, and Forest Service roads. Unclassified roads are roads not intended to be a part of nor managed as a part of the forests transportation system, such as temporary roads, and unplanned, unengineered, unauthorized off-road vehicle tracks and abandoned travel ways. Route measurements and route directions must correspond to those stored in the INFRA Oracle table RTE_BASICS. Associated National Application: INFRA Travel Routes. IWeb Infra Roads webpage http://basenet.fs.fed.us/support/help/roads/. All routed roads are required to have data in INFRA and all roads having data in INFRA are required to be routed.Note: Extracted from GI on August 27,2012

Terrain Inventory Mapping (TIM) contains polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. TIM is multi-purpose and divides the landscape into units according to surficial material, surface expression and geomorphological process using the Terrain Classification System for British Columbia. Most of the inventory mapping was completed in the 1970s and 1980s on 1 to 50,000 scale base maps using air photo interpretation with selective field checking, and manual transfer and drafting of paper maps. These maps were later digitized and have been converted from IGDS or CAPAMP to ArcInfo to Geodatabase. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: TIM and TIMSOI. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

The dataset consists of a table, with building footprint features as rows and attributes as columns, split by country and administrative areas available in the Global ADMinistrative layer version 4.1 (GADM4.1). The attributes include unique identifiers to link to Overture buildings 2024-07-22.0, their centroid coordinates, country and administrative unit location, and building characteristics such as height, shape factor (compactness), functional use, construction year, area, and perimeter. These characteristics are derived from the Global Human Settlement Layer (GHSL) global datasets GHS R2023 and R2024. The dataset is available both as CSV and as SQLite-based geopackages, which incorporate the centroids of building footprints as geometry features.

Bioterrain (TBT) contains polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. TBT divides the landscape into units using the Terrain Classification System for British Columbia and ecological criteria. Polygon attributes include (but are not limited to) surficial material, surface expression, geomorphological processes, drainage class and aspect. TBT methods include manual air photo interpretation supported by selective field checking. Bioterrain mapping is integral to ecosystem mapping and its derivative products. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: TEM, TEMNSS, TEMPRE, TEMSEI, TEMSET, TEMTSM, TBS, TBT, TEMWHR, TEMSDM, TEMPRW, and TEMSEW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

This dataset includes a table with space counts, number of meters, rates, and parking types for each zone, as sampled on particular dates. A second table includes lease counts by lot, also with the dates that the counts were made.

Terrain Stability Mapping (TSM) contains polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. TSM uses air photo interpretation and select field checking to divides the landscape into units using the Terrain Classification System for British Columbia and stability criteria. Polygon attributes include (but are not limited to) surficial material, surface expression, geomorphological processes, drainage class, slope range and stability class. TSM methods include manual air photo interpretation and setting stability criteria supported by selective field checking. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: TEMSET, TEMTSM, TSM, TSMREC, TSMDET, TBW, and TBS. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

Combined with literature knowledge and CSD weight, comprehensive attributes were obtained

The Travel Monitoring Analysis System (TMAS) - Stations Table dataset was compiled on December 31, 2024 and was published on September 25, 2025 from the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data included in this table have been collected by the FHWA from State DOTs through 24/7 permanent count data. The attributes are used by FHWA for its Travel Monitoring Analysis System and external agencies and have been intentionally limited to location referencing attributes since the core station description attribute data are contained within TMAS. The attributes in the Station data correspond with the Station file format found in Chapter 6 of the 2001 Traffic Monitoring Guide (https://doi.org/10.21949/1519109). A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529084

Outdoor Coffee Table

## Overview

Outdoor Coffee Table is a dataset for instance segmentation tasks - it contains Outdoor Coffee Table annotations for 786 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 National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

SEI_Polygons contains Sensitive Ecosystems Inventory polygons with key and amalgamated (concatenated) attributes derived from the RISC (Resource Inventory Standards Committee) standard attributes. SEI identifies and maps rare and fragile terrestrial ecosystems. Ecosystems mapped may include (but are not limited to) older forests, woodlands, coastal bluffs, herbaceous and sparsely vegetated ecosystems, grasslands, riparian ecosystems and wetlands. SEI methods include manual air photo interpretation or theming of other Ecosystem Mapping, each supported by selective field checking. This layer is derived from the STE_TEI_ATTRIBUTE_POLYS_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: SEI, TEMSEI, TEMSET, and SEIWHR. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)