Residential Property Attribute data provides the most current building attributes available for residential properties as captured within Landgate's Valuation Database. Attribute information is captured as part of the Valuation process and is maintained via a range of sources including building and sub division approval notifications. This data set should not be confused with Sales Evidence data which is based on property attributes as at the time of last sale. This dataset has been spatially enabled by linking cadastral land parcel polygons, sourced from Landgatge's Spatial Cadastral Database (SCDB), to the Residential Property Attribute data sourced from the Valuation database. Customers wishing to access this data set should contact Landgate on +61 (0)8 9273 7683 or email businesssolutions@landgate.wa.gov.au © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions. Changes will be applied to this dataset resulting from the implementation of the Community Titles Act 2018 please refer to the Data Dictionary below.

The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityFactsCommonAttributes_01/MapServer/0 Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

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.

This dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report:

http://imap.vic.gov.au/uploads/Meeting%20Agendas/2014%20August/Att%207a_IMAP_Energy_Map_-_CSIRO_-_Energy_Use_2011-2026_Report_-_2014June30_-Final_pdf_11.2MB.pdf

The dataset outlines modelled energy consumption across the City of Melbourne municipality. It is not energy consumption data captured by a meter, but modelled data based on building attributes such as building age, floor area etc. This data was provided by the CSIRO as a result of a study commissioned by IMAP Councils. The study was governed by a Grant Agreement between Councils and the CSIRO, which stated an intent for the data to be published. This specific dataset is presented at a property level scale. It includes both commercial and residential buildings and projections for energy consumption have been made for between 2016 and 2026, based on a business-as-usual scenario. It does not include the industrial sector.

Data and code for the paper "Co-citation and Co-authorhship Networks of Statisticians" (https://www.tandfonline.com/doi/full/10.1080/07350015.2021.1978469).

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Current building datasets suffer from incomplete coverage of building multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, and 23.6 billion m² of rooftops with an F1-Score of 89.93% in OCRNet-based extraction, totaling 363 billion m³ of building stock. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating morphology, location, and function features. Using multi-source data, including billions of remote sensing images and 60 million street view images (SVIs), we generated rooftop, height, structure, function, style, age, and quality attributes for each building with machine learning and large multimodal models. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation, mostly above 80%. Our dataset and results are crucial for global SDGs and urban planning.Data records: A building dataset with a total rooftop area of 23.6 billion square meters in 3,667 natural cities in China, including the attribute of building rooftop, height, structure, function, age, style, colour and quality, as well as the code files used to calculate these data. The deep learning models used are OCRNet, XGBoost, fine-tuned CLIP and Yolo-v8.Reference Format：Zhang, Y., Zhao, H. & Long, Y. CMAB: A Multi-Attribute Building Dataset of China. Sci Data 12, 430 (2025). https://doi.org/10.1038/s41597-025-04730-5.

Satellite TV Channel Attribute Statistical Data Set

Decision strategies in multi-attribute choice experiments are investigated using eye-tracking. The visual attention towards, and attendance of, attributes is examined. Stated attendance is found to diverge substantively from visual attendance of attributes. However, stated and visual attendance are shown to be informative, non-overlapping sources of information about respondent utility functions when incorporated into model estimation. Eye-tracking also reveals systematic nonattendance of attributes only by a minority of respondents. Most respondents visually attend most attributes most of the time. We find no compelling evidence that the level of attention is related to respondent certainty, or that higher or lower value attributes receive more or less attention.

This Excel spreadsheet contains a qualitative set of data collected from a questionnaire administered to 583 secondary school students across New Zealand in 2016. Students were surveyed about their perceptions of the attributes of academically successful students and their best and worst secondary school teachers. Demographic data were also collected which included (age, sex, ethnicity, parent education).

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD program annually collects administrative and fiscal data about all public schools, school districts, and state education agencies in the United States. The data are supplied by state education agency officials and include basic directory and contact information for schools and school districts, as well as characteristics about student demographics, number of teachers, school grade span, and various other administrative conditions. CCD school and agency point locations are derived from reported information about the physical location of schools and agency administrative offices. The point locations and administrative attributes in this data layer represent the most current CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations. For more information about these CCD attributes, as well as additional attributes not included, see: https://nces.ed.gov/ccd/files.asp.Notes:-1 or MIndicates that the data are missing.-2 or NIndicates that the data are not applicable.-9Indicates that the data do not meet NCES data quality standards.Collections are available for the following years:2022-232021-222020-212019-202018-192017-18All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data. Collections are available for the following years:

This file contains Real Property Tax Receivables File layout and descriptions of codes used in that file.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of identities, - 202,599 number of face images, and - 5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, and landmark (or facial part) localization.

Note: CelebA dataset may contain potential bias. The fairness indicators example goes into detail about several considerations to keep in mind while using the CelebA dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('celeb_a', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a-2.1.0.png" alt="Visualization" width="500px">

This dataset is a compilation of facility attributes from different sources of HCAI data (license, financial, and building safety data). The primary use of this dataset is to give an overview of common attributes for each facility that HCAI manages data and information from.

This dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report: Show full descriptionThis dataset should be read alongside other energy consumption datasets on the City of Melbourne open data platform as well as the following report: http://imap.vic.gov.au/uploads/Meeting%20Agendas/2014%20August/Att%207a_IMAP_Energy_Map_-_CSIRO_-_Energy_Use_2011-2026_Report_-_2014June30_-Final_pdf_11.2MB.pdf The dataset outlines modelled energy consumption across the City of Melbourne municipality. It is not energy consumption data captured by a meter, but modelled data based on building attributes such as building age, floor area etc. This data was provided by the CSIRO as a result of a study commissioned by IMAP Councils. The study was governed by a Grant Agreement between Councils and the CSIRO, which stated an intent for the data to be published. This specific dataset is presented at a block level scale. It includes both commercial and residential buildings and is a 2011 baseline. It does not include the industrial sector.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, 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: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the 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, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.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 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.

This repository provides aggregated subzone statistics of global cities. Each dataset is computed via the Urbanity Python package. We include indicators spanning domains such as building morphology, street view imagery, urban population, and points of interest.

This dataset omits geometry information and only contains attribute properties. We release an accompanying dataset that includes geometry information.

This layer has been updated, the new version can be found here: FSL North Island v1.1 (all attributes) - Informatics Team | | Environment and Land GIS | LRIS Portal (scinfo.org.nz) The New Zealand Fundamental Soil Layer originates from a relational join of features from two databases: the New Zealand Land Resource Inventory (NZLRI), and the National Soils Database (NSD). The NZLRI is a national polygon database of physical land resource information, including a soil unit. Soil is one in an inventory of five physical factors (including rock, slope, erosion, and vegetation) delineated by physiographic polygons at approximately 1:50,000 scale. The NSD is a point database of soil physical, chemical, and mineralological characteristics for over 1500 soil profiles nationally. A relational join between the NZLRI dominant soil and derivative tables from the NSD was the means by which 14 important soil attributes were attached to the NZLRI polygons. Some if these attributes originate from exact matches with NSD records, while others derive from matches to similar soils or professional estimates.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level. Data and Resources

This tabular data set represents the presence of six National Hydrography Dataset (NHD) high resolution waterbody types compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The six types of waterbodies presented here are: playa, ice mass, lake/pond, reservoir, swamp/marsh, and estuary. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the NHDPlus high resolution waterbodies produced by USGS , 2015. Units are percent. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

This cadastral polygon dataset is a simplified digital representation of all land parcel boundaries within Western Australia. The dataset covers the State of Western Australia and the Commonwealth jurisdictions of Cocos Keeling Island and Christmas Island. For the full cadastral dataset see Cadastre Polygons (LGATE-217). NOTE: Information contained in this dataset is for informational purposes only and should not be relied upon or referred to for legal purposes. Original survey documentation should be referred to for all legal purposes. © Western Australian Land Information Authority (Landgate). Use of Landgate data is subject to Personal Use License terms and conditions unless otherwise authorised under approved License terms and conditions.