Discover the booming cadastral mapping market! This comprehensive analysis reveals an 8% CAGR, reaching $27.38 billion by 2033, driven by technological advancements and increasing government initiatives. Learn about key players, regional trends, and future growth opportunities in land administration.

Mapping application to support research related to City of Seattle land use, zoning, and other property related development regulations. Includes layers related to zoning, parking, tree regulation, and environmentally critical areas.

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.

Historic land uses on lots that were vacant, privately owned, and zoned for manufacturing in 2009. Information came from a review of several years of historical Sanborn maps over the past 100 years. When the SPEED 1.0 mapping application was created in 2009, OER had its vendor examine historic land use maps on vacant, privately-owned, industrially-zoned tax lots. Up to seven years of maps for each lot were examined, and information was recorded that indicated industrial uses or potential environmental contamination such as historic fill. Data for an additional 139 lots requested by community-based organizations was added in 2014. Each record represents the information from a map from a particular year on a particular tax lot at that time. Limitations of funding determined the number of lots included and entailed that not all years were examined for each lot.

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 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.

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.

Spatial Mapping Software Market Outlook

According to our latest research, the global spatial mapping software market size reached USD 6.2 billion in 2024, reflecting the sector’s robust expansion across industries. The market is expected to grow at a CAGR of 14.1% from 2025 to 2033, reaching an estimated USD 19.3 billion by 2033. The primary growth factor propelling this market is the increasing adoption of spatial data analytics and geospatial intelligence across urban planning, environmental monitoring, and asset management sectors, as organizations strive for enhanced decision-making and operational efficiency.

One of the most significant growth drivers for the spatial mapping software market is the rapid urbanization witnessed globally. Governments and private entities are investing heavily in smart city initiatives, which require advanced mapping tools for infrastructure planning, traffic management, and resource allocation. The integration of spatial mapping software with IoT devices and sensors is enabling real-time data collection and visualization, thus streamlining urban planning processes. Moreover, the growing need for sustainable development and efficient land use is pushing city planners to leverage spatial mapping solutions for accurate geospatial analysis, zoning, and resource optimization. This trend is expected to continue, with urban centers increasingly relying on spatial intelligence to tackle challenges related to population growth, environmental sustainability, and public safety.

Technological advancements in artificial intelligence, machine learning, and cloud computing are further accelerating the growth of the spatial mapping software market. Modern mapping platforms now offer sophisticated features such as 3D visualization, predictive analytics, and automated data processing, which significantly enhance the value proposition for end-users. These innovations are not only improving the accuracy and usability of spatial data but are also making it accessible to non-technical users through intuitive interfaces and seamless integrations with enterprise resource planning (ERP) and geographic information system (GIS) platforms. Additionally, the proliferation of mobile devices and high-speed internet connectivity has made spatial mapping tools more versatile, enabling field workers and remote teams to access, update, and share geospatial information in real time.

Another critical factor contributing to the market’s expansion is the rising importance of spatial mapping software in disaster management and environmental monitoring. Governments, NGOs, and emergency response teams are increasingly utilizing these platforms to assess risks, plan evacuations, and coordinate relief efforts in the wake of natural disasters such as floods, earthquakes, and wildfires. Spatial mapping software enables the integration of diverse datasets, including satellite imagery, sensor data, and historical records, to create comprehensive risk maps and predictive models. This capability is invaluable for proactive disaster preparedness and rapid response, helping to minimize loss of life and property. Similarly, environmental agencies are leveraging these tools to monitor deforestation, track wildlife movements, and manage natural resources, further boosting market demand.

From a regional perspective, North America currently leads the spatial mapping software market, driven by substantial investments in smart infrastructure, advanced technological adoption, and a mature ecosystem of geospatial solution providers. Europe follows closely, with strong government support for digital transformation in urban planning and environmental sustainability. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, infrastructure development, and increasing adoption of smart city solutions in countries like China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by government initiatives for modernization and improved disaster management capabilities. These regional dynamics are shaping the competitive landscape and driving innovation in the global spatial mapping software market.

Component Analysis

The spatial mapping software market is segmented by component into software and services. The software segment dominates the market, accounting for the largest share due to the widespread adoption of propriet

Mapping Software Market Outlook

According to our latest research, the global mapping software market size reached USD 8.2 billion in 2024. Driven by accelerating digital transformation across industries, the market is poised for robust expansion, with a projected CAGR of 13.7% from 2025 to 2033. By the end of 2033, the mapping software market is forecasted to attain a value of USD 25.2 billion. This remarkable growth trajectory is underpinned by the increasing integration of geospatial data analytics, the proliferation of smart city initiatives, and the surging demand for real-time location intelligence across sectors such as transportation, urban planning, and disaster management.

One of the primary growth drivers for the mapping software market is the rapid adoption of geospatial technologies in both public and private sectors. Organizations are leveraging mapping software to enhance operational efficiency, optimize resource allocation, and gain actionable insights from complex spatial datasets. For example, the transportation and logistics industry relies heavily on mapping solutions for route optimization, fleet management, and real-time tracking, which significantly reduces operational costs and improves delivery timelines. Additionally, government agencies utilize mapping software for urban planning, land administration, and disaster response, enabling data-driven decision-making and more effective public service delivery. The continuous evolution of mapping software, with features such as 3D visualization, artificial intelligence integration, and cloud-based collaboration, is further catalyzing market growth.

Another significant factor propelling the mapping software market is the proliferation of Internet of Things (IoT) devices and the exponential growth of location-based services. The integration of IoT with mapping software enables real-time data collection and visualization, which is critical for applications such as smart cities, environmental monitoring, and asset tracking. Enterprises are increasingly adopting mapping solutions to visualize IoT sensor data on interactive maps, facilitating predictive maintenance, energy management, and risk assessment. Moreover, the rise of mobile mapping applications and the widespread availability of high-speed internet connectivity have democratized access to mapping technologies, empowering small and medium enterprises (SMEs) to harness spatial intelligence for business growth and innovation.

The mapping software market is also benefiting from strong investments in infrastructure development and the rising need for disaster management solutions. Governments and urban planners are deploying advanced mapping tools to model urban growth, assess environmental impact, and plan resilient infrastructure. In regions prone to natural disasters, mapping software plays a crucial role in risk assessment, emergency response coordination, and post-disaster recovery. The integration of satellite imagery, drone data, and real-time analytics is enhancing the accuracy and timeliness of mapping outputs, making them indispensable for disaster preparedness and mitigation. As climate change and urbanization continue to pose complex challenges, the demand for sophisticated mapping software is expected to escalate further.

Mapping and Navigation Software is increasingly becoming an integral component of the geospatial technology landscape. These software solutions are designed to provide precise navigation and mapping capabilities, which are essential for a wide range of applications, from urban planning to autonomous vehicle navigation. The ability to integrate real-time data from multiple sources, such as GPS, IoT devices, and satellite imagery, allows for the creation of dynamic and interactive maps that enhance situational awareness and decision-making. As industries continue to adopt digital transformation strategies, the demand for advanced mapping and navigation software is expected to grow, driving innovation and competition in the market. These solutions not only improve operational efficiency but also enable organizations to gain a competitive edge by leveraging spatial intelligence.

Regionally, North America leads the mapping software market, accounting for the largest share due to its early adoption of advanced geospatial technologies and the presence of major industry players. However, Asia Pacific is emerging as the

Land use consists of reading and interpreting the municipal land cover through the use of photo cartographic documentation (orthophotos, cadastre, etc.) and cartography software (Google Maps, Maps Street View, Google Earth, etc.). It represents a polygon of the municipal soil in which each polygon is assigned a nomenclature according to the international codification standard of the European model CORINE Land Cover. The land use was carried out by the Systems, Distributed Information Technology and Territory Office in collaboration with the PRG Revision Project. 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

Land Cover Mapping Analysis And Urban Growth Modelling Using Remote Sensing Techniques In Greater Cairo RegionEgypt
This dataset falls under the category Traffic Generating Parameters Land Cover.
It contains the following data: This study modelled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modelled to predict the future scenarios for year 2025 using Land Change Modeller (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.
This dataset was scouted on 2022-02-03 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.researchgate.net/publication/282321895_Land_Cover_Mapping_Analysis_and_Urban_Growth_Modelling_Using_Remote_Sensing_Techniques_in_Greater_Cairo_Region-Egypt Please note: This link leads to an external resource. If you experience any issues with its availability, please try again later.

The global land survey software market is experiencing robust growth, driven by increasing adoption of advanced technologies like GIS, cloud computing, and AI in surveying operations. The market's size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising demand for precise and efficient land surveying in infrastructure development, urban planning, and construction projects is a significant driver. Secondly, the increasing availability of affordable and user-friendly software solutions is making land surveying technology more accessible to a broader range of users, from small surveying firms to individual professionals. Thirdly, the integration of advanced analytics capabilities within land survey software is enabling improved data analysis and decision-making, leading to better project outcomes and cost efficiencies. Finally, government initiatives promoting digitalization in land administration and mapping further contribute to market expansion. However, the market also faces certain restraints. The high initial investment cost associated with implementing advanced software and hardware can be a barrier for some smaller companies. Additionally, the need for specialized skills and training to effectively utilize these sophisticated tools can limit widespread adoption. Despite these challenges, the long-term outlook for the land survey software market remains positive, primarily due to the continuous advancements in technology and the growing demand for precise land information across various sectors. Key players such as Carlson Software, Microsurvey, GeoMax, Leica Geosystems, Topcon Positioning, Sokkia, Trimble, and Autodesk are actively contributing to market innovation through continuous product development and strategic partnerships. The market segmentation reflects the diverse applications of land survey software across different geographical regions and industries.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

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.

Global Land Surveying Software Market is segmented by Application (Real estate_ construction_ urban planning_ agriculture_ infrastructure), Type (GPS_ GIS_ 3D modeling_ land management_ mapping software), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

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%.

Mapping Software Market size was valued at USD 8.2 Billion in 2024 and is projected to reach USD 17.5 Billion by 2032, growing at a CAGR of 9.7% during the forecast period 2026-2032.Real-time location data is used for navigation, route planning, and traffic monitoring, resulting in increased efficiency in transportation and delivery operations.

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. For users without access to GIS software, the data are available for viewing at: http://harvardforest.fas.harvard.edu/research/1830instructions.html Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.

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