Cadastral Mapping Market Outlook

The global cadastral mapping market size was valued at approximately USD 4.2 billion in 2023 and is projected to reach around USD 7.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This market growth can be attributed to increasing urbanization, rapid advancements in geospatial technologies, and the growing need for efficient land management systems across various regions.

The expansion of urban areas and the corresponding increase in the need for effective land management infrastructure are significant growth factors driving the cadastral mapping market. As urbanization accelerates globally, local governments and planning agencies require sophisticated tools to manage and record land ownership, boundaries, and property information. Enhanced geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, are pivotal in facilitating accurate and efficient cadastral mapping, thus contributing to market growth.

Another key growth factor is the rising demand for infrastructure development. As nations invest in large-scale infrastructure projects such as roads, railways, and smart cities, there is an increased need for precise land data to ensure the proper allocation of resources and to avoid legal disputes. Cadastral mapping provides the critical data needed for these projects, hence its demand is surging. Additionally, governments worldwide are increasingly adopting digital platforms to streamline land administration processes, further propelling the market.

Furthermore, the agricultural sector is also significantly contributing to the growth of the cadastral mapping market. Modern agriculture relies heavily on accurate land parcel information for planning and optimizing crop production. By integrating cadastral maps with other geospatial data, farmers can improve land use efficiency, monitor crop health, and enhance yield predictions. This integration is particularly valuable in precision farming, which is becoming more prevalent as the world's population grows and the demand for food increases.

Regionally, Asia Pacific is expected to witness the highest growth in the cadastral mapping market. Factors such as rapid urbanization, extensive infrastructure development projects, and the need for improved land management are driving the demand in this region. Moreover, governments in countries like India and China are investing heavily in creating digital land records and implementing smart city initiatives, which further boosts the market. The North American and European markets are also substantial, driven by the advanced technological infrastructure and well-established land administration systems.

Component Analysis

The cadastral mapping market can be segmented by component into software, hardware, and services. The software segment holds a significant share in this market, driven by the increasing adoption of advanced GIS and mapping software solutions. These software solutions enable accurate land parcel mapping, data analysis, and integration with other geospatial data systems, making them indispensable tools for cadastral mapping. Companies are continuously innovating to provide more intuitive and comprehensive software solutions, which is expected to fuel growth in this segment.

Hardware components, including GPS devices, drones, and other surveying equipment, are also critical to the cadastral mapping market. The hardware segment is expected to grow steadily as technological advancements improve the accuracy and efficiency of these devices. Innovations such as high-resolution aerial imaging and LIDAR technology are enhancing the capabilities of cadastral mapping hardware, allowing for more detailed and precise data collection. This segment is particularly essential for field surveying and data acquisition, forming the backbone of cadastral mapping projects.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. Professional services are vital for the successful deployment and operation of cadastral mapping systems. Governments and private sector organizations often rely on specialized service providers to implement these systems, train personnel, and ensure ongoing support. As the complexity of cadastral mapping projects increases, the demand for expert services is also expected to rise, contributing to the growth of this segment.

Integration services are another critical component within the

Property map viewer for the State of Tennessee that covers 85 of the 95 counties in Tennessee.This application allows for searching and displaying property ownership and location information for 87 counties in Tennessee. It is designed to work in concert with the Real Estate Assessment Data site operated by the Comptroller of the Treasury. The following counties are not available in this application but can be found on their own internet sites: Bradley, Davidson (Metro Nashville), Hamilton (Chattanooga), Knox (Knoxville), Montgomery (Clarksville), Rutherford(Murfreesboro), Shelby (Memphis), Sumner, Unicoi, and Williamson.

The Land & Building Management System (LBMS) application serves as a vital resource for accessing and managing data related to land parcels, buildings, and associated spatial features. The data in this application is actively maintained and updated on a daily basis from Monday through Friday, ensuring that users have access to the most current and relevant information.Whenever new LBMS records are detected, corresponding spatial features are dynamically added to the dataset. This process ensures that the application reflects accurate and up-to-date geospatial representations of land and building assets.It’s important to note, however, that while the LBMS application provides valuable insights, users requiring the most authoritative and comprehensive LBMS data should refer to the production LBMS system. This production environment serves as the definitive source of record for all LBMS-related data.

These data depict the western United States Map Unit areas as defined by the USDA NRCS. Each Map Unit area contains information on a variety of soil properties and interpretations. The raster is to be joined to the .csv file by the field "mukey." We keep the raster and csv separate to preserve the full attribute names in the csv that would be truncated if attached to the raster. Once joined, the raster can be classified or analyzed by the columns which depict the properties and interpretations. It is important to note that each property has a corresponding component percent column to indicate how much of the map unit has the dominant property provided. For example, if the property "AASHTO Group Classification (Surface) 0 to 1cm" is recorded as "A-1" for a map unit, a user should also refer to the component percent field for this property (in this case 75). This means that an estimated 75% of the map unit has a "A-1" AASHTO group classification and that "A-1" is the dominant group. The property in the column is the dominant component, and so the other 25% of this map unit is comprised of other AASHTO Group Classifications. This raster attribute table was generated from the "Map Soil Properties and Interpretations" tool within the gSSURGO Mapping Toolset in the Soil Data Management Toolbox for ArcGIS™ User Guide Version 4.0 (https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd362255&ext=pdf) from GSSURGO that used their Map Unit Raster as the input feature (https://gdg.sc.egov.usda.gov/). The FY2018 Gridded SSURGO Map Unit Raster was created for use in national, regional, and state-wide resource planning and analysis of soils data. These data were created with guidance from the USDA NRCS. The fields named "*COMPPCT_R" can exceed 100% for some map units. The NRCS personnel are aware of and working on fixing this issue. Take caution when interpreting these areas, as they are the result of some data duplication in the master gSSURGO database. The data are considered valuable and required for timely science needs, and thus are released with this known error. The USDA NRCS are developing a data release which will replace this item when it is available. For the most up to date ssurgo releases that do not include the custom fields as this release does, see https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053628#tools For additional definitions, see https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053627.

The DC Office of the Chief Financial Officer (OCFO), Office of Tax and Revenue (OTR), Real Property Tax Administration (RPTA) values all real property in the District of Columbia. This public interactive Real Property Assessment map application accompanies the OCFO MyTax DC and OTR websites. Use this mapping application to search for and view all real property, assessment valuation data, assessment neighborhood areas and sub-areas, detailed assessment information, and many real property valuation reports by various political and administrative areas. View by other administrative areas such as DC Wards, ANCs, DC Squares, and by specific real property characteristics such as property type and/or sale date. If you have questions, comments, or suggestions regarding the Real Property Assessment Map, contact the Real Property Assessment Division GIS Program at (202) 442-6484 or maps.title@dc.gov.

Minnesota's original public land survey plat maps were created between 1848 and 1907 during the first government land survey of the state by the U.S. Surveyor General's Office. This collection of more than 3,600 maps includes later General Land Office (GLO) and Bureau of Land Management maps up through 2001. Scanned images of the maps are available in several digital formats and most have been georeferenced.

The survey plat maps, and the accompanying survey field notes, serve as the fundamental legal records for real estate in Minnesota; all property titles and descriptions stem from them. They also are an essential resource for surveyors and provide a record of the state's physical geography prior to European settlement. Finally, they testify to many years of hard work by the surveying community, often under very challenging conditions.

The deteriorating physical condition of the older maps (drawn on paper, linen, and other similar materials) and the need to provide wider public access to the maps, made handling the original records increasingly impractical. To meet this challenge, the Office of the Secretary of State (SOS), the State Archives of the Minnesota Historical Society (MHS), the Minnesota Department of Transportation (MnDOT), MnGeo (formerly the Land Management Information Center - LMIC) and the Minnesota Association of County Surveyors collaborated in a digitization project which produced high quality (800 dpi), 24-bit color images of the maps in standard TIFF, JPEG and PDF formats - nearly 1.5 terabytes of data. Funding was provided by MnDOT.

In 2010-11, most of the JPEG plat map images were georeferenced. The intent was to locate the plat images to coincide with statewide geographic data without appreciably altering (warping) the image. This increases the value of the images in mapping software where they can be used as a background layer.

This lookup tool has been developed to provide quick access to the property information stored in the City-Parish GIS database. Users can search by Address, Subdivision Name, Business Name, North American Industry Classification System (NAICS) Code, or Lot Identification Number to find detailed information about property. Enter the required information in one of the search fields as shown in the example below each field. Next, click the button adjacent to the search criteria, and the search results will be displayed beneath the lookup tools in tabular format. Clicking on one of the search result records will invoke a new window with details about the property. The property details may then be printed and/or exported to a comma-separated values (CSV) file.

Public Land Survey System (PLSS) Data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain boundaries for Townships, First Divisions, and Second Divisions.

This dataset visualises the spatial distribution of the rental value in Amsterdam between 1647 and 1652. The source of rental value comes from the Verponding registration in Amsterdam. The verponding or the ‘Verpondings-quohieren van den 8sten penning’ was a tax in the Netherlands on the 8^th penny of the rental value of immovable property that had to be paid annually. In Amsterdam, the citywide verponding registration started in 1647 and continued into the early 19^th century. With the introduction of the cadastre system in 1810, the verponding came to an end.

The original tax registration is kept in the Amsterdam City Archives (Archief nr. 5044) and the four registration books transcribed in this dataset are Archief 5044, inventory 255, 273, 281, 284. The verponding was collected by districts (wijken). The tax collectors documented their collecting route by writing down the street or street-section names as they proceed. For each property, the collector wrote down the names of the owner and, if applicable, the renter (after ‘per’), and the estimated rental value of the property (in guilders). Next to the rental value was the tax charged (in guilders and stuivers). Below the owner/renter names and rental value were the records of tax payments by year.

This dataset digitises four registration books of the verponding between 1647 and 1652 in two ways. First, it transcribes the rental value of all real estate properties listed in the registrations. The names of the owners/renters are transcribed only selectively, focusing on the properties that exceeded an annual rental value of 300 guilders. These transcriptions can be found in Verponding1647-1652.csv. For a detailed introduction to the data, see Verponding1647-1652_data_introduction.txt.

Second, it geo-references the registrations based on the street names and the reconstruction of tax collectors’ travel routes in the verponding. The tax records are then plotted on the historical map of Amsterdam using the first cadaster of 1832 as a reference. Since the geo-reference is based on the street or street sections, the location of each record/house may not be the exact location but rather a close proximation of the possible locations based on the street names and the sequence of the records on the same street or street section. Therefore, this geo-referenced verponding can be used to visualise the rental value distribution in Amsterdam between 1647 and 1652. The preview below shows an extrapolation of rental values in Amsterdam. And for the geo-referenced GIS files, see Verponding_wijken.shp.

GIS specifications:

Coordination Reference System (CRS): Amersfoort/RD New (ESPG:28992)

Historical map tiles URL (From Amsterdam Time Machine)

NB: This verponding dataset is a provisional version. The georeferenced points and the name transcriptions might contain errors and need to be treated with caution.

Contributors

• Historical and archival research: Weixuan Li, Bart Reuvekamp
• Plotting of geo-referenced points: Bart Reuvekamp
• Spatial analysis: Weixuan Li
• Mapping software: QGIS
• Acknowledgements: Virtual Interiors project, Daan de Groot

The global real estate surveying and mapping market is valued at USD XXX million in 2023 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market's growth is attributed to the rising demand for accurate land surveys and maps for real estate development, urban planning, and infrastructure projects. Furthermore, advancements in technology, such as the adoption of drone surveys, laser scanning, and GIS software, are driving market expansion by enhancing surveying and mapping efficiency and accuracy. The real estate surveying and mapping market is segmented by type into land surveying and mapping, house surveying and mapping, and others. Land surveying and mapping account for the largest market share due to the high demand for land surveys for property boundary demarcation, land use planning, and construction projects. The house surveying and mapping segment is also witnessing significant growth due to the increased need for pre-purchase surveys, structural inspections, and property renovations. Key industry players include Morris-Depew Associates, RM Towill Corporation, Trimble, PASCO Corporation, Fugro, AECOM, Stantec, AEI Consultants, Tuofeng Surveying and Mapping, Mucheng Surveying, Nanyang Spatial Mapping, Zhongjiao Road & Bridge, Okay Information Technology, Zhongke Testing Technology, Centre Testing International Group, and TIRAIN Science & Technology.

The Maine Geoparcel Viewer Application allows users to search and view available digital parcel data for Organized Townships and Unorganized Territories in the State of Maine. The Maine GeoLibrary and the Maine Office of GIS do not maintain parcel data for communities, cannot verify parcel ownership, and are not responsible for individual parcel data verification or updating emergency records concerning parcel addresses. If you have questions about a specific parcel, please contact the appropriate Town Office or County Registry of Deeds for the most up-to-date information.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. The "Maine Parcels Organized Towns Feature" layer and "Maine Parcels Organized Towns ADB" table are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, which affects the currency of Maine GeoLibrary parcels data; some data are more than ten years old. Please contact the appropriate Town Office or the County Registry of Deeds for more up-to-date parcel information. Organized Town data should very closely match registry information, except in the case of in-process property conveyance transactions.In Unorganized Territories (defined as those regions of the state without a local government that assesses real property and collects property tax), Maine Revenue Services is the authoritative source for parcel data. The "Maine Parcels Unorganized Territory" layer is the authoritative GIS data layer for the Unorganized Territories. However, it must always be used with auxiliary data obtained from the online resources of Maine Revenue Services to compile up-to-date parcel ownership information.

The main purposes of this online map are 1. to demonstrate the Web-Based Geographic Information System (GIS) in the District of Columbia Office of Tax and Revenue (OTR) Real Property Tax Administration (RPTA), and 2. to share detailed real property data and information to real property owners, the public, and other government entities. The rich map and interactive application include relevant real property valuation contributing map layers, links to original source agencies, and a variety of search, query, and analysis options to meet the needs of a wide user base. The location and links to the original DC Boundary Stones add a fun, historical,and educational component.The Office of the Chief Financial Officer, DC Office of Tax and Revenue (OTR), Real Property Assessment Division values all real property in the District of Columbia. The public interactive online DC Office of Tax and Revenue Real Property Assessment Lot Map Search application accompanies the OTR Tax Payer Service Center and may be used to search for and view all real property, related assessment areas, assessment data, and detailed assessment information.

Abstract: In this work we introduce an object-based method, applied to urban land cover mapping. The method is implemented with two open-source tools: SIPINA, a data mining software package; and InterIMAGE, an object-based image analysis system. Initially, segmentation, feature extraction and sample selection procedures are performed with InterIMAGE. In order to reduce the time and subjectivity involved to develop the decision rules in InterIMAGE, a data mining step is then carried out with SIPINA. In sequence, the decision trees delivered by SIPINA are analysed and encoded into InterIMAGE decision rules for the final classification step. Experiments were conducted using a subset of a GeoEye image, acquired in January 01, 2013, covering the urban portion of the municipality of Goianésia, Brazil. Five decision tree induction algorithms, available in SIPINA, were tested: ID3, C45, GID3, Assistant86 and CHAID. The TAU and Kappa coefficients were used to evaluate the results. The TAU values obtained were in the range of 0.66 and 0.70, while those for Kappa varied from 0.65 to 0.69.

2014 Coastal Land Use Data. Digital survey of aerial imagery and desktop mapping software. Carried out by the University of Leicester. Project details: https://www.nationaltrust.org.uk/documents/mapping-our-shores-fifty-years-of-land-use-change-at-the-coast.pdf

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

Land use consists of reading and interpreting municipal land cover through the use of photo-cartographic documentation (orthophoto, cadastre, etc.) and software for cartography (Google Maps, Maps Street View, Google Earth, etc.).

It represents a polygonisation of the municipal soil in which each polygon is assigned a nomenclature according to the international standard of codification of the European model CORINE Land Cover.

The land use has been carried out by the Department of Systems, distributed IT and territory in collaboration with the Project Revision of the PRG.
It is constantly updated and given the complexity of the data (more than 12000 polygons) are welcome reports of any inaccuracies or improvements by writing to infogis@comune.trento.it

Disaster Prevention and Rescue Application Award by the National Land Surveying and Mapping Center

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

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. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

Worcester Atlas is an interactive map viewer developed by the City of Worcester that gives the public access to city map layers and data, including property-specific assessor data.Users can search for property data by address, street, owner, or property ID, turn on/off map layers, get more information about certain layers in map popups, print maps, and more.More information: Visit the Introducing Worcester Atlas data story to get to know more about the City's map viewer.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost