GoalsSymbolize dense point features.Add and label reference data.Configure a layout for print maps.

The ArcGIS Online USGS Topographic Maps image service contains over 181,000 historical topographic quadrangle maps (quads) dating from 1879 to 2006. These maps are part of the USGS Historical Topographic Map Collection (HTMC) which includes all the historical quads that had been printed since the USGS topographic mapping program was initiated in 1879. Previously available only as printed lithographic copies, the historical maps were scanned “as is” to create high-resolution images that capture the content and condition of each map sheet. All maps were georeferenced, and map metadata was captured as part of the process.

For the Esri collection, the scanned maps were published as this ArcGIS Online image service which can be viewed on the web and allows users to download individual scanned images. Esri’s collection contains historical quads (excluding orthophotos) dating from 1879 to 2006 with scales ranging from 1:10,000 to 1:250,000. The scanned maps can be used in ArcGIS Pro, ArcGIS Online, and ArcGIS Enterprise. They can also be downloaded as georeferenced TIFs for use in these and other applications.

We make it easy for you to explore and download these maps, or quickly create an ArcGIS Online map, using our Historical Topo Map Explorer app. The app provides a visual interface to search and explore the historical maps by geographic extent, publication year, and map scale. And you can overlay the historical maps on a satellite image or 3D hillshade and add labels for current geographic features.

Tutorial Audience: GIS / Technology SpecialistsEnd User Audience: Emergency Management Planning and Operations StaffProblem: Your County Emergency Management Agency is planning a training exercise and wants to make use of “Web GIS.” Typically, they have you print out a new wall map each operational period and the status of facilities (e.g. shelters) are maintained in spreadsheets. This time they want to coordinate planning and operations across multiple locations, with everyone having the most up to date information on a live map. For example, they want to be able update the status of evacuation zones and shelters without requiring GIS expertise. Can you provide them with a web app that gives them some simple tools and just the layers they need to get started? Use a simulated flood or any other incident type to guide you through this process.Solution: Operations Response AppRequirements: You will need a license for ArcGIS Pro and ArcGIS Online to complete this tutorial.Note: This application is used with the Public Information Application Tutorial.

1955 aerial photos of Douglas County NE belonging to the City of Omaha and/or Douglas County. This aerial photography was scanned and georeferenced from Mylar prints. This hosted tile service was created from a mosaic dataset in an ArcGIS Pro map, both projected to NE State Plane NAD83 Feet. The tile package was created using the ESRI tiling scheme down to the 1:1128 scale with a Mixed tiling format and a 75 compression ratio.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

A 1942 blueline print map of farm and experimental plots on the campus of Michigan State College identifying department ownership of plots. The map also displays railroads, roads, building footprints and other topographic features.General Disclaimer: This map was scanned as a 600 dpi TIFF and georeferenced manually using ArcGIS Pro desktop software. Georeferencing was done using 51 GCPs and a spline transformation.Historic maps are of varying accuracy and meant to be used for general historical comparison only. They may not be perfectly geographically accurate but are a perspective of the map authors about the subject contained within. Georeferencing accuracy reflects these considerations.Citation:Michigan State University. “Michigan State College Farm and Experimental Plots.” 1:7,200. East Lansing, MI: Michigan State University, 1942. https://catalog.lib.msu.edu/Record/folio.in00002190892.

Introduction: The Situational Awareness Viewer is used by planning teams for managing spatial data on incidents. It also helps watch officers and situation unit leaders analyze and understand potential impacts to the community while planning for an impending incident.

For more details, see the ArcGIS for Emergency Management Solution: Situational Awareness Viewer. This website will provide requirements, an update on what is new, a "Get Started" section, an overview of workflows, a preview video and a "Try It Now" example that you can test.

Watch the video and then try out the sample application (MapSAR Online Training) to gain an understanding of how this app works with sample data.

In this exercise, we will prepare a web app designed for use with the MapSAR Online data model. At this point, you should have already completed two important steps 1) Create a web map 2) Create a feature layer

Audience: This is meant for the mapping / GIS Specialist on a SAR Team who will setup the web mapping application. This person should be comfortable using web technology and preferably has met or knows the local GIS Team who can provide more detailed base data.

Capability: To provide a web mapping application that can be used for situational awareness and with the following capabilities for search and rescue:

· Plot the initial planning point

· Prepare the statistical search area or buffered ring for your initial search extent

· Edit incident data (assignments, assets, team status, etc)

· Print a basic map

· Add GPS Data to your map

· Manage a Clue Log

· Interoperability - Integrate with other applications including: ArcGIS Pro - advanced map production, spatial analyses, data management, Survey123 for ArcGIS - for a live clue log or crowdsourcing, or any other apps that provide feeds of data (e.g. KMLs from SARTopo).

Named Landforms of the World version 2 (NLWv2) contains four sub-layers representing geomorphological landforms, provinces, divisions, and their respective cartographic boundaries. The latter supports map making, while the first three represent basic units, such as landforms, which comprise provinces, and provinces comprise divisions. NLW is a substantial update to World Named Landforms in both compilation method and the attributes that describe each landform. For more details, please refer to our paper, Named Landforms of the World: A Geomorphological and Physiographic Compilation, in Annals of the American Association of Geographers. July 2, 2025: We have made Named Landforms of the World v3 (NLWv3) available. Please explore this group containing all of the layers and data. NLWv2 will remain available. Landforms are commonly defined as natural features on the surface of the Earth. The National Geographic Society specifies terrain as the basis for landforms and lists four major types: mountains, hills, plateaus, and plains. Here, however, we define landforms in a richer way that includes properties relating to underlying geologic structure, erosional and depositional character, and tectonic setting and processes. These characteristics were asserted by Dr. Richard E. Murphy in 1968 in his map, titled Landforms of the World. We blended Murphy"s definition for landforms with the work E.M. Bridges, who in his 1990 book, World Geomorphology, provided a globally consistent description of geomorphological divisions, provinces, and sections to give names to the landform regions of the world. AttributeDescriptionBridges Full NameFull name from E.M. Bridges" 1990 "World Geomorphology" Division and if present province and section - intended for labeling print maps of small extents. Bridges DivisionGeomorphological Division as described in E.M. Bridges" 1990 "World Geomorphology" - All Landforms have a division assigned, i.e., no nulls. Bridges ProvinceGeomorphological Province as described in E.M. Bridges" 1990 "World Geomorphology" - Not all divisions are subdivided into provinces. Bridges SectionGeomorphological Section as described in E.M. Bridges" 1990 "World Geomorphology" - Not all provinces are subdivided into sections.StructureLandform Structure as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Alpine Systems: Area of mountains formed by orogenic (collisions of tectonic plates) processes in the past 350 to 500 million years. - Caledonian/Hercynian Shield Remnants: Area of mountains formed by orogenic (collisions of tectonic plates) processes 350 to 500 million years ago. - Gondwana or Laurasian Shields: Area underlaid by mostly crystalline rock formations fromed one billion or more years ago and unbroken by tectonic processes. - Rifted Shield Areas: fractures or spreading along or adjacent to tectonic plate edges. - Isolated Volcanic Areas: volcanic activity occurring outside of Alpine Systems and Rifted Shields. - Sedimentary: Areas of deposition occurring within the past 2.5 million years Moist or DryLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Moist: where annual aridity index is 1.0 or higher, which implies precipitation is absorbed or lost via runoff. - Dry: where annual aridity index is less than 1.0, which implies more precipitation evaporates before it can be absorbed or lost via runoff. TopographicLandform Topographic type variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Karagulle et. al. 2017 - based on rich morphometric characteristics. Coded Value Domain. Values include: - Plains: Areas with less than 90-meters of relief and slopes under 20%. - Hills: Areas with 90- to 300-meters of local relief. - Mountains: Areas with over 300-meters of relief - High Tablelands: Areas with over 300-meters of relief and 50% of highest elevation areas are of gentle slope. - Depressions or Basins: Areas of land surrounded land of higher elevation. Glaciation TypeLandform Erosional/Depositional variable as described in Richard E. Murphy"s 1968 "Landforms of the World" map. Values include: - Wisconsin/Wurm Glacial Extent: Areas of most recent glaciation which formed 115,000 years ago and ended 11,000 years ago. - Pre-Wisconsin/Wurm Glacial Extent: Areas subjected only to glaciation prior to 140,000 years ago. ContinentAssigned by Author during data compilation. Bridges Short NameThe name of the smallest of Division, Province, or Section containing this landform feature. Murphy Landform CodeCombination of Richard E. Murphy"s 1968 "Landforms of the World" variables expressed as a 3- or 4- letter notation. Used to label medium scale maps. Area_GeoGeodesic area in km2. Primary PlateName of tectonic plate that either completely underlays this landform feature or underlays the largest portion of the landform"s area.Secondary PlateWhen a landform is underlaid by two or more tectonic plates, this is the plate that underlays the second largest area.3rd PlateWhen a landform is underlaid by three or more tectonic plates, this is the plate that underlays the third largest area.4th PlateWhen a landform is underlaid by four or more tectonic plates, this is the plate that underlays the fourth largest area.5th PlateWhen a landform is underlaid by five tectonic plates, this is the plate that underlays the fifth largest area.NotesContains standard text to convey additional tectonic process characteristics. Tectonic ProcessAssigns values of orogenic, rift zone, or above subducting plate. These data are also available as an ArcGIS Pro Map Package: Named_Landforms_of_the_World_v2.0.mpkx.These data supersede the earlier v1.0: World Named Landforms. Change Log:DateDescription of ChangeJuly 20, 2022Corrected spelling of Guiana from incorrect representation, "Guyana", used by Bridges.July 27, 2022Corrected Structure coded value domain value, changing "Caledonian/Hercynian Shield" to "Caledonian , Hercynian, or Appalachian Remnants". Cite as: Frye, C., Sayre R., Pippi, M., Karagulle, Murphy, A., D. Soller, D.R., Gilbert, M., and Richards, J., 2022. Named Landforms of the World. DOI: 10.13140/RG.2.2.33178.93129. Accessed on:

This scanned historical map from the University of Idaho Experimental Forest archives shows the principal cover types on Moscow Mountain and vicinity. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.

This scanned historical map from the University of Idaho Experimental Forest archives shows watersheds on Moscow Mountain and vicinity. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.

This scanned historical map from the University of Idaho Experimental Forest archives shows school district boundaries, schoolhouses, occupied houses, and vacant house on Moscow Mountain and vicinity. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.

Python Scripting for ArcGIS Pro stars with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. Leam how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, Custom scripts that will automate your ArcGIS Pro workflows.Some of the key topics you Will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasksExploring and manipulating spatal and tabular dataWorking With geometriesMap scriptingDebugging ard error handlingHelpful "points to remember," key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online.Whether want to learn python or already have some experience, Python Scripting for ArcGlS Pro is comprehensive, hands-on book for learning versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGlS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python.Don't forget to also check out Esri Press's other Python title:Advanced Python Scripting for ArcGIS ProAUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPaul A Zandbergen is an associate professor of geography at the University of New Mexico in Albuquerque. His areas of expertise include geographic information science; spatial and statistical analysis techniques using GIS; error and uncertainty in spatial data; GIS applications in criminology, economics, health, and spatial ecology; terrain analysis and modeling; and community-based mapping using GIS and GPS.Pub Date: Print 7/7/2020 Digital: 7/7/2020ISBN: Print 9781589484993 Digital: 9781589485006 Price: Print: $79.99 USD Digital: $79.99 USD Pages: 420 Trim: 8 x 10 in.Table of ContentsPrefaceAcknowledgmentsChapter 1. Introducing Py%onChapter 2. Working with Python editorsChapter 3. Geoprocessing in ArcGIS ProChapter 4. Leaming Python language fundamentalsChapter 5. Geoprocessing using PythonChapter 6. Exploring spatial dataChapter 7. Debugging and error handlingChapter 8. Manipulating spatial and tabular dataChapter 9. Working with geometriesChapter 10. Working with rastersChapter 11. Map scriptingIndexPython Scripting and Advanced Python Scripting for ArcGIS Pro | Official Trailer | 2020-07-12 | 01:04Paul Zandbergen | Interview with Esri Press | 2020-07-10 | 25:37 | Link.

This scanned historical map from the University of Idaho Experimental Forest archives shows burned areas and locations of sawmills and brick facilities on the Palouse Range. The printed paper map is dated April 23, 1934.This map was scanned on a Contex HD 5450 wide format scanner at 300 dpi with 24-bit color. The original file was scanned on April 7, 2023 and created as an uncompressed TIF file. Subsequently, a 300 dpi JPEG file was created from the archival TIF file using Adobe Photoshop 2023. The JPG file was rotated, de-skewed, and cropped to make the documents as usable as possible.The JPG file was georeferenced using georeferencing tools in ArcGIS Pro 3.0.1 in June, 2023. Twenty-two (22) control points were placed to align the image to the NAD 1983 UTM Zone 11N coordinate system. The public land survey system was used as the target layer. A first order polynomial transformation (affine) was selected to transform the image without deforming it and the georeferencing information was saved with the image. Total RMS error was less than 100.