hzy/20250319-math-train-upward-shape dataset hosted on Hugging Face and contributed by the HF Datasets community

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Version 1

Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels.

  Version 2

Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels.

  Version 3

Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels.

  Version 4

Same weight to all transformations. The image sizes are between 1 and 30 pixels.… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-shape-v5.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2020 Census.

Since 2002, NASA’s GRACE Satellite mission has allowed scientists of various disciplines to analyze and map the changes in Earth’s total water storage on a global scale. Although the raw data is available to the public, the process of viewing, manipulating, and analyzing the GRACE data can be tedious and difficult for those without strong technological backgrounds in programming or other related fields. The GRACE web app helps bridge the technical gap for decision makers by providing a user interface to visualize (in both map and time series format), not only the data collected from the GRACE mission, but the individual components of water storage as well. Using the GLDAS Land Surface model, the application allows the user to isolate and identify the changes in surface water and groundwater storage that makeup the total water storage quantities measured by the raw GRACE data. The application also includes the capability to upload a custom shapefile in order to perform a regional analysis of these changes allowing decision makers to aggregate and analyze the change in groundwater, surface water, and total water storage within their own personal regions of interest.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Public view of the parcel layer. This view is limited to only the attributes that can be seen by the general public.The data table includes the following fields: Shape Type (Shape), Shape.STArea() (Shape_Area), Shape.STLength() (Shape_Area), Name (APN), Created By Record (CreatedbyR), Retired By Record (RetiredbyR), Stated Area, Stated Area Unit (StatedAr_1), Calculated Area (Calculated), Misclose Ratio (MiscloseRa), Misclose Distance (MiscloseDi), Is Seed (IsSeed), Created By (created_us), Created Date (created_da), Modified By (last_edite), Modified Date (last_edi_1), Validation Status (VALIDATION), APN Dashed (APN_Dashed), Map Page (Map_Page), Municipality (Municipali), FloorOrder, HideThere are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.

These two shapefiles represent New Mexico NHD High Resolution stream segments and waterbodies, merged and clipped to the state boundary. RAW NHD High Resolution data, including additional layer files, is available from: https://viewer.nationalmap.gov/basic/

This geographical dataset consists of a series of four shapefiles. The main one, EGY_SEC, provides the delineation for the villages (Yahya), cities (Madina), and neighbourhoods (Shiyakha) of Egypt's larger towns. This shapefile comprises 5410 geocoded geographical units linked with the 1996 census data.

The EGY_SEC geographical layer of Egypt's smallest administrative units has been sourced from various paper map series (notably Egyptian administrative military maps and the 1/25.000 series). The layer is georeferenced using Egypt's datum (Egypt 1907 / Blue Belt).

The previous work of Sylvie Fanchette¹ on the Nile Delta population mapping has been instrumental in the making of this geodataset.

It was created in the frame of the programme EGIPTE “Explorations Géographiques Informatisées de la Population et du Territoire de l’Égypte » funded by CNRS from 2004 to 2006 (PIR-Ville). It was coordinated by François Moriconi and Eric Denis with the support of Hala Bayoumi and Ahmed Wagih, and hosted during the 1990s’ by the CEDEJ in Cairo.

An updated version of this map is used and can be viewed on the join website: https://www.cedejcapmas.org

The other 3 shapefiles are:

NILE for the Nile River layer

EGYPT for the governorate/muhafaza level layer

EGYPT_GOV for the district-level layer

The dataset is notably in the special issue of the Journal Geocarrefour (Revue de Géographie de Lyon), vol. 73, n°3, 1998 titled Géographie sociale de l'Egypte.

See also:

Denis, E. (1998). Croissance urbaine et dynamique socio-spatiale Le Caire de 1950 à 1990. L'espace géographique, 129-142.

Denis, É., & Moriconi-Ébrard, F. (1998). La population de l'Egypte 1897-1996. L'information Géographique, 62(1), 12-23.

1. S. Fanchette. 1997. Le delta du Nil. Densités de population et urbanisation des campagnes. Fascicule de Recherches n°32. Urbama-Orstom. Tours. 389 p.

description: The USGS Elevation Contours service from The National Map (TNM) consists of contours generated for the conterminous United States from 1- and 1/3 arc-second elevation data. Small scale contours derived from 1 arc-second data are displayed at scales ranging from 1:577K to 1:72K in The National Map viewer. Contour intervals are 100 foot between 1:577K and 1:144K, and 50 foot at 1:72K. Large scale contours derived from 1/3 arc-second data are displayed at 1:50K (and larger). Large scale contour intervals are variable across the United States depending on complexity of topography. The National Map viewer allows free downloads of public domain contour data in either Esri File Geodatabase or Shapefile formats. The 3D Elevation Program (3DEP) provides elevation data for The National Map and basic elevation information for earth science studies and mapping applications. Scientists and resource managers use elevation data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. For additional information on 3DEP, go to https://nationalmap.gov/3DEP/.; abstract: The USGS Elevation Contours service from The National Map (TNM) consists of contours generated for the conterminous United States from 1- and 1/3 arc-second elevation data. Small scale contours derived from 1 arc-second data are displayed at scales ranging from 1:577K to 1:72K in The National Map viewer. Contour intervals are 100 foot between 1:577K and 1:144K, and 50 foot at 1:72K. Large scale contours derived from 1/3 arc-second data are displayed at 1:50K (and larger). Large scale contour intervals are variable across the United States depending on complexity of topography. The National Map viewer allows free downloads of public domain contour data in either Esri File Geodatabase or Shapefile formats. The 3D Elevation Program (3DEP) provides elevation data for The National Map and basic elevation information for earth science studies and mapping applications. Scientists and resource managers use elevation data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. For additional information on 3DEP, go to https://nationalmap.gov/3DEP/.

This resource contains a shapefile of HUC-8 (eight digit Hydrologic Unit Codes) for the Continental United States (CONUS).

The Watershed Boundary Dataset (WBD) is a comprehensive aggregated collection of hydrologic unit data consistent with the national criteria for delineation and resolution. It defines the areal extent of surface water drainage to a point except in coastal or lake front areas where there could be multiple outlets as stated by the "Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD)" “Standard” (http://pubs.usgs.gov/tm/11/a3/). Watershed boundaries are determined solely upon science-based hydrologic principles, not favoring any administrative boundaries or special projects, nor particular program or agency. This dataset represents the hydrologic unit boundaries to the 12-digit (6th level) for the entire United States. Some areas may also include additional subdivisions representing the 14- and 16-digit hydrologic unit (HU). At a minimum, the HUs are delineated at 1:24,000-scale in the conterminous United States, 1:25,000-scale in Hawaii, Pacific basin and the Caribbean, and 1:63,360-scale in Alaska, meeting the National Map Accuracy Standards (NMAS). Higher resolution boundaries are being developed where partners and data exist and will be incorporated back into the WBD. WBD data are delivered as a dataset of polygons and corresponding lines that define the boundary of the polygon. WBD polygon attributes include hydrologic unit codes (HUC), size (in the form of acres and square kilometers), name, downstream hydrologic unit code, type of watershed, non-contributing areas, and flow modifications. The HUC describes where the unit is in the country and the level of the unit. WBD line attributes contain the highest level of hydrologic unit for each boundary, line source information and flow modifications.

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding.The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population. New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA products which provide coverage for those areas.In the NFHL Viewer, you can use the address search or map navigation to locate an area of interest and the NFHL Print Tool to download and print a full Flood Insurance Rate Map (FIRM) or FIRMette (a smaller, printable version of a FIRM) where modernized data exists. Technical GIS users can also utilize a series of dedicated GIS web services that allow the NFHL database to be incorporated into websites and GIS applications. For more information on available services, go to the NFHL GIS Services User Guide.You can also use the address search on the FEMA Flood Map Service Center (MSC) to view the NFHL data or download a FIRMette. Using the “Search All Products” on the MSC, you can download the NFHL data for a County or State in a GIS file format. This data can be used in most GIS applications to perform spatial analyses and for integration into custom maps and reports. To do so, you will need GIS or mapping software that can read data in shapefile format.FEMA also offers a download of a KMZ (keyhole markup file zipped) file, which overlays the data in Google Earth™. For more information on using the data in Google Earth™, please see Using the National Flood Hazard Layer Web Map Service (WMS) in Google Earth™.

This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

The Sonoma County fine scale vegetation and habitat map is an 82-class vegetation map of Sonoma County with 212,391 polygons. The fine scale vegetation and habitat map represents the state of the landscape in 2013 and adheres to the National Vegetation Classification System (NVC). The map was designed to be used at scales of 1:5,000 and smaller. This dataset is also available as a layer package and a file geodatabase.The full datasheet for this product is available here: https://sonomaopenspace.egnyte.com/dl/qOm3JEb3tD The final report for the fine scale vegetation map, containing methods and an accuracy assessment, is available here: https://sonomaopenspace.egnyte.com/dl/1SWyCSirE9Class definitions, as well as a dichotomous key for the map classes, can be found in the Sonoma Vegetation and Habitat Map Key (https://sonomaopenspace.egnyte.com/dl/xObbaG6lF8)The fine scale vegetation and habitat map was created using semi-automated methods that include field work, computer-based machine learning, and manual aerial photo interpretation. The vegetation and habitat map was developed by first creating a lifeform map, an 18-class map that served as a foundation for the fine-scale map. The lifeform map was created using “expert systems” rulesets in Trimble Ecognition. These rulesets combine automated image segmentation (stand delineation) with object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for lifeform included: orthophotography (’11 and ’13), the LiDAR derived Canopy Height Model (CHM), and other LiDAR derived landscape metrics. After it was produced using Ecognition, the preliminary lifeform map product was manually edited by photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results. Edits were made to correct two types of errors: 1) unsatisfactory polygon (stand) delineations and 2) incorrect polygon labels.The mapping team used the lifeform map as the foundation for the finer scale and more floristically detailed Fine Scale Vegetation and Habitat map. For example, a single polygon mapped in the lifeform map as forest might be divided into four polygons in the in the fine scale map including redwood forest, Douglas-fir forest, Oregon white oak forest, and bay forest. The fine scale vegetation and habitat map was developed using a semi-automated approach. The approach combines Ecognition segmentation, extensive field data collection, machine learning, manual editing, and expert review. Ecognition segmentation results in a refinement of the lifeform polygons. Field data collection results in a large number of training polygons labeled with their field-validated map class. Machine learning relies on the field collected data as training data and a stack of GIS datasets as predictor variables. The resulting model is used to create automated fine-scale labels countywide. Machine learning algorithms for this project included both Random Forests and Support Vector Machines (SVMs). Machine learning is followed by extensive manual editing, which is used to 1) edit segment (polygon) labels when they are incorrect and 2) edit segment (polygon) shape when necessary.The map classes in the fine scale vegetation and habitat map generally correspond to the alliance level of the National Vegetation Classification, but some map classes - especially riparian vegetation and herbaceous types - correspond to higher levels of the hierarchy (such as group or macrogroup).

TRCA GIS Open data on ArcGIS online. This link will take you to an external site URL: https://trca-camaps.opendata.arcgis.com/

USGS Structures from The National Map consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently being collected are: School, Technical/Trade School, College/University, Fire Station/EMS Station, Law Enforcement/Police Station, Prison/Correctional Facility, State Capitol, Hospital/Medical Center, Ambulance Service, Cemetery, Post Office, Campground, Trailhead, and Visitor/Information Center. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/core-science-systems/ngp/tnm-corps/structures. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Search Monmouth County tax parcel data by shape, block and lot, or buffer. The Monmouth County Property Viewer contains a complete set of tools used to query, select properties, and review property information. Each tool can be collapsed and expanded to provide more room for viewing the map at the user preference. Please review the Help (?) section for instructions on how to use this tool.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

This data set consists of a detailed digital map of individual irrigated fields and a summary of the irrigated acreage for the 2017 growing season developed for Highlands County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.