Label placement and properties for identifying features are as important as the symbols that you use to represent the features. Like symbols, labels are included in both basemap and operational map layers. This course will show you how to add and customize labels for your maps.Goals Use ArcGIS Pro to label features in a map.

Labeling Map Objects

## Overview

Labeling Map Objects is a dataset for object detection tasks - it contains Cars People Trees annotations for 424 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).

Vector Map Label API is a XYZ Vector Map Tile Service which enables application developers to retrieve vector map tiles with place name labels for overlaying onto the vector map.

Map labels help to identify features, establish a visual hierarchy of important features, and focus the map user's attention on the purpose of the map.Estimated time: 25 minutesSoftware requirements: ArcGIS Pro

This web map contains the same layers as the Imagery with Labels basemap that is available in the basemap gallery of ArcGIS.com's map viewer, ArcGIS Explorer Online, ArcGIS Explorer Desktop, and the mobile clients. The Imagery with Labels basemap contains the World Imagery map service with the Boundaries and Places map service drawn on top. When you use this basemap in a web map, any map services that you add into the map get sandwiched between the imagery and the labels drawn on top, so this is a good basemap you use if you want to see services that don't contain their own labels with imagery drawn behind them and reference labels drawn on top.This web map also includes the World Transportation map service. This service shows streets, roads and highways and their names. When you zoom in to the highest level of detail the lines disappear and you just see the street names and road numbers.Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.Tip: This same web map is also available with transportation and road names turned on: Imagery with Labels and Transportation.Tip: Here are some famous locations as they appear in this web map, accessed by including their location in the URL that launches the map:Grand Canyon, Arizona, USAGolden Gate, California, USATaj Mahal, Agra, IndiaVatican CityBronze age white horse, Uffington, UKUluru (Ayres Rock), AustraliaMachu Picchu, Cusco, PeruOkavango Delta, Botswana

This feature class depicts Forest Service trails where motorized use is allowed. It contains information on the specific type of motor vehicle and their seasons of use. The feature class is consistent with the appropriate National Forest's Motor Vehicle Use Map (MVUM). Non-motorized trails are not included in this data. Trails in this feature class are legal for some motorized use for at least a portion of the year. Any reference to Open or Dates Open refers strictly to when it is legal to use that motor vehicle on the trail. It is not meant to describe when the conditions would be appropriate for that use. As an example, a trail may be designated open to motorcycles all year long but there may be periods of time when snow depth prevents the use of motorcycles on that trail. It is compiled from the GIS Data Dictionary data and tabular data that the administrative units have prepared for the creation of their MVUMs. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the Enterprise Data Warehouse (EDW).

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the Motor Vehicle Use Map (MVUM). Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Information for each individual unit must be verified as to be consistent with the published MVUMs prior to inclusion in this data. Not every National Forest has data included in this feature class.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 OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

This vector tile layer presents the Human Geography Label style (World Edition) and provides a detailed vector basemap for world labels designed to draw attention to your thematic content. This is similar in content and style to the popular Light Gray Canvas map. The map includes labels for highways, major roads, minor roads, water features, cities, landmarks, and administrative boundaries. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Human Geography Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

Multiple geographical feature label placement (MGFLP) has been a fundamental problem in cartographic visualization over the decades. The nature of label placement is proven NP-hard, where the complexity of such a problem is directly influenced by the volume of input datasets.

LandsD Map Label API

The map layer "Shipping Canal Labeling" shows the labeling of the shipping canals in North Rhine-Westphalia with the canal names.

The map layer "Labeling watercourses" shows the labeling of the large watercourses in North Rhine-Westphalia with the names of the water bodies.

Landscape map — Theme map 9a ‘Protected areas and objects’ of the city of Trier. Geometry type: Lines. Stand: 2010

This dataset contains files created, digitized, or georeferenced by Chris DeRolph for mapping the pre-urban renewal community within the boundaries of the Riverfront-Willow St. and Mountain View urban renewal projects in Knoxville TN. Detailed occupant information for properties within boundaries of these two urban renewal projects was extracted from the 1953 Knoxville City Directory. The year 1953 was chosen as a representative snapshot of the Black community before urban renewal projects were implemented. The first urban renewal project to be approved was the Riverfront-Willow Street project, which was approved in 1954 according to the University of Richmond Renewing Inequality project titled ‘Family Displacements through Urban Renewal, 1950-1966’ (link below in the 'Other shapefiles' section). For ArcGIS Online users, the shapefile and tiff layers are available in AGOL and can be found by clicking the ellipsis next to the layer name and selecting 'Show item details' for the layers in this webmap https://knoxatlas.maps.arcgis.com/apps/webappviewer/index.html?id=43a66c3cfcde4f5f8e7ab13af9bbcebecityDirectory1953 is a folder that contains:JPG images of 1953 City Directory for street segments within the urban renewal project boundaries; images collected at the McClung Historical CollectionTXT files of extracted text from each image that was used to join occupant information from directory to GIS address datashp is a folder that contains the following shapefiles:Residential:Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and property ownersBlack_rented_residential_1953.shp: residential entries in the 1953 City Directory identified as Black and non-owners of the propertyNon_Black_owned_residential_1953.shp: residential entries in the 1953 City Directory identified as property owners that were not listed as BlackNon_Black_rented_residential_1953.shp: residential entries in the 1953 City Directory not listed as Black or property ownersResidential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposeslastName: occupant's last namelabelShort: combines the Number and lastName fields for map labeling purposesNon-residential:Black_nonResidential_1953.shp: non-residential entries in the 1953 City Directory listed as Black-occupiedNonBlack_nonResidential_1953.shp: non-residential entries in the 1953 City Directory not listed as Black-occupiedNon-residential shapefile attributes:cityDrctryString: full text string from 1953 City Directory entryfileName: name of TXT file that contains the information for the street segmentsOccupant: the name of the occupant listed in the City Directory, enclosed in square brackets []Number: the address number listed in the 1953 City DirectoryBlackOccpt: flag for whether the occupant was identified in the City Directory as Black, designated by the (c) or (e) character string in the cityDrctryString fieldOwnerOccpd: flag for whether the occupant was identified in the City Directory as the property owner, designated by the @ character in the cityDrctryString fieldUnit: unit if listed (e.g. Apt 1, 2d fl, b'ment, etc)streetName: street name in ~1953Lat: latitude coordinate in decimal degrees for the property locationLon: longitude coordinate in decimal degrees for the property locationNAICS6: 2022 North American Industry Classification System (NAICS) six-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS6title: NAICS6 title/short descriptionNAICS3: 2022 North American Industry Classification System (NAICS) three-digit business code, designated by Chris DeRolph rapidly and without careful considerationNAICS3title: NAICS3 title/short descriptionflag: flags whether the occupant is part of the public sector or an NGO; a flag of '0' indicates the occupant is assumed to be a privately-owned businessrace_own: combines the BlackOccpt and OwnerOccpd fieldsmapLabel: combines the Number and Occupant fields for map labeling purposesOther shapefiles:razedArea_1972.shp: approximate area that appears to have been razed during urban renewal based on visual overlay of usgsImage_grayscale_1956.tif and usgsImage_colorinfrared_1972.tif; digitized by Chris DeRolphroadNetwork_preUrbanRenewal.shp: road network present in urban renewal area before razing occurred; removed attribute indicates whether road was removed or remains today; historically removed roads were digitized by Chris DeRolph; remaining roads sourced from TDOT GIS roads dataTheBottom.shp: the approximate extent of the razed neighborhood known as The Bottom; digitized by Chris DeRolphUrbanRenewalProjects.shp: boundaries of the East Knoxville urban renewal projects, as mapped by the University of Richmond's Digital Scholarship Lab https://dsl.richmond.edu/panorama/renewal/#view=0/0/1&viz=cartogram&city=knoxvilleTN&loc=15/35.9700/-83.9080tiff is a folder that contains the following images:streetMap_1952.tif: relevant section of 1952 map 'Knoxville Tennessee and Surrounding Area'; copyright by J.U.G. Rich and East Tenn Auto Club; drawn by R.G. Austin; full map accessed at McClung Historical Collection, 601 S Gay St, Knoxville, TN 37902; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphnewsSentinelRdMap_1958.tif: urban renewal area map from 1958 Knox News Sentinel article; used as reference for street names in roadNetwork_preUrbanRenewal.shp; georeferenced by Chris DeRolphusgsImage_grayscale_1956.tif: May 18, 1956 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/ARA550590030582/usgsImage_colorinfrared_1972.tif: April 18, 1972 color infrared USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR6197002600096/usgsImage_grayscale_1976.tif: November 8, 1976 black-and-white USGS aerial photograph, georeferenced by Chris DeRolph; accessed here https://earthexplorer.usgs.gov/scene/metadata/full/5e83d8e4870f4473/AR1VDUT00390010/

The toponymic features of the CanVec series include proper nouns designating places and representations of the territory. This data come from provincial, territorial and Canadian toponymic databases. They are used in the CanVec Series for cartographic reference purposes and vary according to the scale of display. The toponymic features of the CanVec series can differ from the Canada's official geographical names. The CanVec multiscale series is available as prepackaged downloadable files and by user-defined extent via a Geospatial data extraction tool. Related Products: Topographic Data of Canada - CanVec Series Users can obtain information about Canada's official toponyms at: Geographical names in Canada

Accurate, high-resolution maps of bedrock outcrops are extremely valuable. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock maps. This data release contains training data created for developing a machine learning model capable of identifying exposed bedrock across the entire Sierra Nevada Mountains (California, USA). The training data consist of 20 thematic rasters in GeoTIFF format, where image labels represent three categories: rock, not rock, and no data. These training data labels were created using 0.6-m imagery from the National Agriculture Imagery Program (NAIP) acquired in 2016. Eight existing labeled sites were available from Petliak et al. (2019), an earlier effort. We further revised those labels for improved accuracy and created additional 12 reference sites following the same protocol of semi-manual mapping in Petliak et al. (2019). A machine learning model (https://github.com/nasa/delta) was trained and tested based on these image labels as detailed in Shastry et al. (in review). The trained model was then used to map exposed bedrock across the entire Sierra Nevada region using 2016 NAIP imagery, and this data release also includes these model outputs. The model output gives the likelihood (from 0 to 255) that each pixel is bedrock, and not a direct binary classification. The associated publication used a threshold of 50%, or pixel value 127, where all pixel values 127 or higher are classified as rock and less than as not rock.

Important Note: This item is in mature support as of July 2021. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This web map contains the same layers as the 'Imagery with Labels' basemap that is available in the basemap gallery in the ArcGIS applications but also adds the World Transportation map serviceThe World Transportation map service shows streets, roads and highways and their names. When you zoom in to the highest level of detail the lines disappear and you just see the street names and road numbers.The 'Imagery with Labels' basemap contains the World Imagery map service and the World Boundaries and Places map service, so when you use that basemap you get boundaries and places, but you don't get streets and roads at small scales or street and road labels at large scale. So by adding the World Transportation map service into your map as well you get those too.Want to use this map as the basemap for your own web map? If you have not created your web map yet, simply open this map and then do Save As to save a copy of it as your own map, and then make changes to it like zooming in and adding more data. If you have already created your web map, open it and choose the Imagery With Labels basemap from the Basemap dropdown. Then add the World Transportation service into your map by searching for it. This 'Imagery with Labels and Transportation' web map shows you what this looks like. The World Transportation map service is designed to be drawn underneath the World Boundaries and Places map service, as you can see in this web map.In this web map, we have set the Transportation layer with partial transparency to make the transportation network less prominent relative to the imagery. You can manipulate the level of transparency that you use for the basemap and reference layers in the web maps that you create. You can do this in the layer properties of the layers in the map table of contents.Feedback: Have you ever seen a problem in the Esri World Imagery Map that you wanted to see fixed? You can use the Imagery Map Feedback web map to provide feedback on issues or errors that you see. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.Tip: This web map is a useful general purpose map that you can link to from web pages, emails, social media, etc, and embed in your own web page. Just open the map and then choose the Share option. Like with any public map in ArcGIS Online, you don't need to have an ArcGIS Online account in order to share this map by linking or embedding. In addition, by adding extent parameters in the URL you use to link or embed the map, you can take users directly to particular locations. So anyone can immediately take advantage of this map on the web to show any location in the world without even being signed in to ArcGIS Online. See this help topic for more information. For example, here are some links that use extent parameters to open this map at some famous locations. Some of these specify a rectangular extent on the map to zoom to. Others specify a center point and a zoom level to zoom to:Grand Canyon, Arizona, USAGolden Gate, California, USATaj Mahal, Agra, IndiaVatican CityBronze age white horse, Uffington, UKUluru (Ayres Rock), AustraliaMachu Picchu, Cusco, PeruOkavango Delta, Botswana

The Central Mojave Vegetation Polygons shapefile represents areas of the Mojave Desert classified into vegetation classes or alliances representative of the area from 1997-1999. The classification of these areas were derived from context gathered in the field data, photographs and additional satellite imagery that is not included in this data release. The original map coverage was preserved and released as a shapefile (mojave_veg_polygons.shp). In contrast to the Special Features Points vegetation classifications (described in the Special Features Points shapefile metadata record and ScienceBase item), the Central Mojave Vegetation Polygons were designated by vegetation alliances that extended 5 hectares or more. Map labels represent alliances and groups of alliances as described by the National Vegetation Classification, as it existed at that time. Each map unit is labeled by a primary land cover type and a secondary type where applicable. In addition, the source of data for labeling each map unit is also identified in the attribute table for each map unit. The metadata record (Mojave-Vegetation-Mapping_Mojave-Veg-Polygons-Metadata.xml), the Mojave vegetation polygons shapefile (zipped shapefile, mojave_veg_polygons.zip) and the label codes sheet that provides context for the vegetation classifications (LabelCodes.csv) are all included as attachments on the Mojave Vegetation Polygons ScienceBase item.

Landscape map – District map ‘Kernscheid’ of the city of Trier. Geometry type: Lines. Status: 2011

Structural characterization of small molecule binding site hotspots within the global proteome is uniquely enabled by photo-affinity labeling (PAL) coupled with chemical enrichment and unbiased analysis by mass spectrometry (MS). MS-based binding site hotspot maps provide structural resolution of interaction sites in conjunction with identification of target proteins. However, binding site hotspot mapping has been confined to relatively simple small molecules to date; extension to more complex compounds would enable the structural definition of new binding modes in the proteome. Here, we extend PAL and MS methods to derive a binding site hotspot map for the immunosuppressant rapamycin, a complex macrocyclic natural product that forms a ternary complex with the proteins FKBP12 and FRB. Photo-rapamycin was developed as a diazirine-based PAL probe for rapamycin, and the FKBP12–photo-rapamycin–FRB ternary complex formed readily in vitro. Photo-irradiation, digestion, and MS analysis of the ternary complex revealed a McLafferty rearrangement product of photo-rapamycin conjugated to specific surfaces on FKBP12 and FRB. Molecular modeling of the ternary complex based on the binding site map revealed a 5.0 Å minimum distance constraint between the conjugated residues and the diazirine carbon. Molecular dynamics further predicted a 9.0 Å labeling radius for the diazirine upon photo-activation that may be useful in the interpretation of binding site measurements from PAL more broadly. Thus, in characterizing the ternary complex of photo-rapamycin by MS, we applied binding site hotspot mapping to a macrocyclic natural product and extracted a precise structural measurement for interpretation of PAL products that may enable the discovery of new ligand space in the “undruggable” proteome.