The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. This layer is a partial view of the Information Sales System (ISS) extract, a report of property characteristics taken from the County’s Megabyte Property Tax System (MPTS). This layer may be missing some attributes (e.g., Owner Name) which may not be published to the Internet due to privacy conditions under the California Public Records Act (CPRA). Please contact the Clerk-Recorder-Assessor (CRA) office at (707) 565-1888 for information on availability, associated fees, and access to other versions of Sonoma County parcels containing additional property characteristics.The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

This file contains European countries in a shapefile format that can be used in python, R or matlab. The file has been created by Drin Marmullaku based on GADM version 4.1 (https://gadm.org/) and distributed according to their license (https://gadm.org/license.html).

Please cite as: Sevdari, Kristian; Marmullaku, Drin (2023). Shapefile of European countries. Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.23686383 This dataset is distributed under a CCBY-NC-SA 4.0 license

Using the data to create maps for publishing of academic research articles is allowed. Thus you can use the maps you made with GADM data for figures in articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are allowed (but not required) to publish these articles (and the maps they contain) under an open license such as CC-BY as is the case with PLoS journals and may be the case with other open access articles. Data for the following countries is covered by a a different license Austria: Creative Commons Attribution-ShareAlike 2.0 (source: Government of Austria)

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).

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points).

All of the Digital City Map (DCM) datasets are featured on the Streets App

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Updates for this dataset, along with other multilayered maps on NYC Open Data, are temporarily paused while they are moved to a new mapping format. Please visit https://www.nyc.gov/site/planning/data-maps/open-data/dwn-digital-city-map.page to utilize this data in the meantime.

Download .zipThis is a statewide digital version of the Hydrography layer of the published USGS 1:24OOO-scale topographic map series. It was created from DLG files of each scanned 7.5-minute quad map, using custom ARC/INFO software routines which did the following steps: convert from DLG format to coverage format project from UTM to StatePlane map projection rubbersheet map sheet corners to exact computed quad-corner coordinates run semi-automated Edgematching procedure which joins hydrography lines along the quad edges, using distance-offset (maximum of 100 feet) and attribute-match criteria to determine which lines to join. merge the individual quad coverages and dissolve the quad-edge lines Processing was done using Double Precision coordinates and math, with processing tolerance (Fuzzy) of 1 foot.

In the Database/Dataset Section the items CMAJOR and CMINOR represent coded pairs and are documented together. Up to five pairs in the pat and four pairs in the aat may be present. These will be appear in the tables as CMAJOR1 CMINOR1 CMAJOR2 CMINOR2, etc.

The layer has been provided in both shape file and coverage format. In the case of the shape file separate shape files are provided for point, line, and polygon data. In the coverage format the line and polygon data is combined in one coverage. The shape files will be the choice of most users due to speed of drawing issues. Those users desiring to manipulate the original data may want to use the coverage format. All shapefiles have been combined for simplicities sake into one self extracting zip file which expands to about 500 megabytes. However, the value listed in the file size parameter of the metadata represents only the size of the particular shapefile being documented. Also it should be noted that the coverages contain redefined items which of necessity had to be split into separate items or omitted in the shape files because this option isn't available in shapefiles.

This layer documentation is for the Shapefile which includes line features.Contact Information:GIS Support, ODNR GIS ServicesOhio Department of Natural ResourcesReal Estate & Land ManagementReal Estate and Lands Management2045 Morse Rd, Bldg I-2Columbus, OH, 43229Telephone: 614-265-6462Email: gis.support@dnr.ohio.gov

Abstract This dataset and its metadata statement were developed for the Bioregional Assessment Programme and are presented here as originally supplied. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. This dataset has been superseded by Cartographic masks for map products GIP 120 v03. Purpose Cart…Show full descriptionAbstract This dataset and its metadata statement were developed for the Bioregional Assessment Programme and are presented here as originally supplied. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. This dataset has been superseded by Cartographic masks for map products GIP 120 v03. Purpose Cartographic masks for map products GIP_120, used for clear annotation and masking unwanted features from report maps. Dataset History Rectangular polygon shapefile masks were created around selected feature labels from the following datasets: GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) - GUID: 96ebf889-f726-4967-9964-714fb57d679b Victoria Mining Licences - 13 May 2015 - GUID: c9c1dff4-01c7-4669-a033-d8a9f674cd5a A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (XXXX) Cartographic masks for map products GIP 120 v02. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/39945fcc-d1a7-49c4-a011-ca595c42ec51. Dataset Ancestors Derived From GEODATA TOPO 250K Series 3 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Victoria Mining Licences - 13 May 2015

The NOAA Office for Coastal Management (OCM) requested the creation of benthic habitat data along the southern Texas coast to support the Texas Seagrass Monitoring Program.The benthic habitat map was created from 1m UltraCam digital airborne imagery collected in November 2007. The imagery was processed into 4-band DOQQs. The benthic habitat map was created from resampled 2m mosaicked orthos. Habitat classification was performed through segmentation of the imagery using Definiens Professional and habitat labeling through Classification and Regression Tree (CART) Analysis. The minimum mapping unit is100m2. This map covers San Antonio and Espiritu Santo Bays which is approximately 370mi2. Original contact information: Contact Name: Becky Jordan Contact Org: Fugro EarthData, Inc. Title: Project Manager Phone: 301-948-8550 Email: bjordan@earthdata.com

These data-sets are polygon shapefiles that represent flood inundation boundaries for 157 flooding scenarios in an 8-mile reach of the Papillion Creek near Offutt Air Force Base. These shapefiles were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base for use within the USGS Flood Inundation Mapping program. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages on the Papillion Creek at Fort Crook, Nebr. (station 06610795) and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System web interface at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https:/water.weather.gov/ahps/. Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The model was calibrated using the current (2021) stage-discharge relation at the Papillion Creek at Fort Crook, Nebr. streamgage. The hydraulic model then was used to compute 157 water-surface profiles for scenarios where combination of stage values in 1-foot (ft) stage intervals, that ranged between 27 and 39 ft at the Papillion Creek at Fort Crook streamgage and 13.9 and 30.9 ft at the Papillion Creek at Harlan Lewis Road streamgage as referenced to the local datum. The simulated water-surface profiles then were combined with a geographic information system digital elevation model (DEM) with a 3.281-ft grid to delineate polygon shapefiles, and depth grids of inundated areas. Along with the inundated area maps, polygon shapefiles and depth grids of areas behind the levees were created to display the uncertainty of these areas, if a levee breech were to occur. These 'areas of uncertainty' files have '_breach' and '_breachgrid' appended to the file names in the data release. The availability of these maps, along with information regarding current stage from the USGS streamgage, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

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. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census, the MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The boundaries of most legal MCDs are as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CCDs are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL_120, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no contentShow full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL_120, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2014) Cartographic masks for map products GAL120. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/77b92796-9501-4829-9b41-598dd455ca93.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. James W. Sewall Company developed a complete GIS coverage for the park and revised the preliminary vegetation map classes to better match the results from the cluster analysis and NMS ordination. Polygons representing vegetation stands were digitized on-screen in ArcGIS 8.3, and later in ArcMap 9.1 and 9.2, using lines drawn on the acetate overlays, base layers of 1:8,000 CIR aerial photography, orthorectified photo composite image, and plot location and data. The minimum map unit used was 0.5 ha (1.24 ac). Stereo pairs were used to double check stand signatures during the digitizing process. Photo interpretation and polygon digitization extended outside the NPS boundary, especially where vegetation units were arbitrarily truncated by the boundary. Each polygon was attributed with the name of a vegetation map class or an Anderson Level II land use category based on plot data, field observations, aerial photography signatures, and topographic maps. Data fields identifying the USNVC association inclusions within the vegetation map class were attributed to the vegetation polygons in the shapefile. The GIS coverages and shapefiles were projected to Universal Transverse Mercator (UTM) Zone 19 North American Datum 1983 (NAD83). FGDC compliant metadata (FGDC 1998a) were created with the NPS-MP ESRI extension and included with the vegetation map shapefile. A photointerpretation key to the map classes for the 2006 draft vegetation map is included as Appendix A. The composite vegetation coverage was clipped to the NPS 2002 MIMA boundary shapefile for accuracy assessment (AA). After the 2006 vegetation map was completed, the thematic accuracy of this map was assessed.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

Cartographic masks for map products GAL230, used for clearing annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

* A new polygon shapefile was created with no content

* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Cartographic masks for map products GAL230. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/353ff2a7-7dd8-4f7e-9dd4-42f907910f1d.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.4. The processes undertaken to produce this dataset are described in the History field in this metadata statement.

Purpose

Cartographic masks for map products GIP 114, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

* A new polygon shapefile was created with no content

* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 114. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/cf55e8a5-5543-4284-8ef8-121059ea59b2.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL2623, used for clearing annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no …Show full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL2623, used for clearing annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2016) Cartographic mask for map product GAL2623. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/62db647f-07e2-459c-a096-848edbea4ee4.

Download high-quality, up-to-date United Arab Emirates shapefile boundaries (SHP, projection system SRID 4326). Our United Arab Emirates Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. The processes undertaken to produce this dataset are described in the History field in this metadata statement.

The dataset cartographic masks for use in cartographic outputs in the Gippsland Basin bioregion product 1.1.1.

Purpose

Cartographic masks for map products GIP 111, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

* A new polygon shapefile was created with no content

* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 111. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/4cac5615-e818-46a6-a21d-13ee042adf0d.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL group 2 products, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created …Show full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL group 2 products, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2015) Cartographic masks for water level trend map products GAL213. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/c714da92-0434-423d-85d0-f48242825fb9.

Watersheds in Eaton County, Michigan, USA. These watersheds were created using a hydro-enforced 10ft DEM derived from 2010 Lidar in conjunction with ECGIS hydrology vector layers. The watersheds are simply elevation-based and pay no heed to man-made drainage that may run counter-grade. Delineation occurs confluence to confluence along the flowlines and also around lakes that are 4 hectares or larger.

This polygon shapefile is an index to 1:50,000 scale maps of Japan, titled '集成五万分一地形圖 - Shūsei gomanbun no ichi chikeizu.’ This map series was originally produced by the Japanese Land Survey Department of the General Staff Headquarters in 1945. Stanford University Libraries holds a large collection of Japanese military and imperial maps, referred to as gaihōzu, or "maps of outer lands." These maps were produced starting in the early Meiji (1868-1912) era and the end of World War II by the Land Survey Department of the General Staff Headquarters, the former Japanese Army. The Library is in the process of scanning and making available all of the maps in the collection. To create this index, footprints were generated using the fishnet tool, and metadata were supplied for the digitized paper maps by Stanford University Libraries. After the footprints were created, the shapefile was trimmed and labeled according to the sources. Relief is shown by contours and spot heights. Index to adjoining sheets are in the margin, however, the set is not complete.

This polygon shapefile is an index to 1:500,000 scale maps of East Asia, titled '東亞五十万分一圖 -- Tōa 1:500,000 Map of East Asia.’ This map series was originally produced by the Japanese Land Survey Department of the General Staff Headquarters between 1923 and 1944. Stanford University Libraries holds a large collection of Japanese military and imperial maps, referred to as gaihōzu, or "maps of outer lands." These maps were produced starting in the early Meiji (1868-1912) era and the end of World War II by the Land Survey Department of the General Staff Headquarters, the former Japanese Army. The Library is in the process of scanning and making available all of the maps in the collection. To create this index, footprints were generated using the fishnet tool, and metadata were supplied for the digitized paper maps by Stanford University Libraries. After the footprints were created, the shapefile was trimmed and labeled according to the sources.