Damper Labeling and GIS Mapping Market Outlook

According to our latest research, the global damper labeling and GIS mapping market size reached USD 1.13 billion in 2024, with a robust growth trajectory driven by the increasing integration of digital solutions in building management and infrastructure development. The market is currently expanding at a CAGR of 8.2% and is forecasted to achieve a value of USD 2.22 billion by 2033. This growth is primarily attributed to the surging demand for precise asset tracking, enhanced regulatory compliance, and the adoption of advanced Geographic Information System (GIS) technologies across various industrial and commercial sectors.

One of the primary growth factors propelling the damper labeling and GIS mapping market is the intensifying focus on building automation and smart infrastructure development. As cities worldwide embrace smart building initiatives, the need for accurate damper labeling and real-time GIS mapping becomes indispensable for efficient facility management and safety compliance. Modern HVAC systems, fire safety mechanisms, and industrial ventilation systems rely heavily on precise damper identification and location tracking. This digital transformation is further supported by stringent regulatory frameworks that mandate clear asset labeling and documentation, ensuring safety and operational efficiency. The integration of IoT and AI-driven analytics within GIS mapping platforms is also enhancing operational visibility, thereby reducing maintenance costs and downtime.

Additionally, the rising adoption of cloud-based solutions is significantly influencing market dynamics. Cloud deployment offers scalability, remote accessibility, and seamless data sharing, which are crucial for large-scale commercial and industrial projects. Organizations are increasingly leveraging cloud-enabled GIS mapping to centralize asset data, streamline workflows, and facilitate real-time collaboration among stakeholders. This shift is particularly valuable in multi-site operations, where centralized control and standardized labeling protocols are essential for regulatory compliance and effective risk management. As a result, service providers are investing heavily in cloud infrastructure and cybersecurity, which is expected to further accelerate market growth.

Another compelling driver for the damper labeling and GIS mapping market is the growing emphasis on fire safety and disaster preparedness. With the escalation of fire incidents in commercial and industrial facilities, regulatory bodies are enforcing stricter codes for damper identification and maintenance. GIS mapping, when integrated with advanced labeling systems, provides a comprehensive overview of damper locations, enabling swift response during emergencies. This capability is particularly critical for large-scale facilities such as hospitals, educational institutions, and manufacturing plants, where rapid evacuation and risk mitigation are paramount. Furthermore, the ongoing trend of retrofitting aging infrastructure with modern labeling and mapping solutions is opening new avenues for market expansion, as facility managers seek to enhance safety and operational transparency.

From a regional perspective, North America continues to dominate the damper labeling and GIS mapping market, owing to its early adoption of advanced building automation technologies and stringent regulatory standards. The presence of leading technology providers, coupled with significant investments in smart city projects, is fostering innovation and market penetration. In contrast, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, infrastructure modernization, and government-led initiatives to enhance building safety and energy efficiency. Europe, with its mature construction sector and strong focus on sustainability, is also contributing significantly to market development. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, fueled by increasing awareness of safety regulations and the adoption of digital asset management practices.

Component Analysis

The damper labeling and GIS mapping market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment is experiencing substantial growth, driven by the increasing demand for advanced GIS platforms that offer real-time data visualization, asset tr

Damper Labeling and GIS Mapping Market Outlook

According to our latest research, the Global Damper Labeling and GIS Mapping market size was valued at $1.2 billion in 2024 and is projected to reach $2.7 billion by 2033, expanding at a robust CAGR of 9.1% during the forecast period of 2025–2033. The primary driver behind this substantial growth is the increasing integration of advanced building automation and fire safety systems across commercial, industrial, and government infrastructures worldwide. This trend is further bolstered by stringent regulatory mandates for safety compliance, and the growing adoption of smart technologies that require precise asset mapping and real-time damper status monitoring. As organizations prioritize operational efficiency and safety, the demand for comprehensive damper labeling and GIS mapping solutions is anticipated to surge, underpinning the market’s positive outlook.

Regional Outlook

North America currently commands the largest share of the Damper Labeling and GIS Mapping market, accounting for approximately 38% of the global revenue in 2024. This dominance can be attributed to the region’s mature infrastructure landscape, early adoption of building automation technologies, and a highly regulated environment that enforces strict safety and compliance standards. The United States, in particular, leads the market due to significant investments in smart buildings and infrastructure modernization, alongside active involvement of key industry players and technology innovators. Moreover, the prevalence of large-scale commercial and industrial facilities in North America necessitates advanced damper labeling and GIS mapping solutions for effective asset management and regulatory adherence, further cementing the region’s leadership position.

In contrast, the Asia Pacific region is poised to be the fastest-growing market, projected to expand at a remarkable CAGR of 12.6% from 2025 to 2033. This rapid growth is fueled by accelerated urbanization, burgeoning construction activities, and substantial government investments in smart city initiatives across countries such as China, India, and Southeast Asian nations. The increasing adoption of cloud-based GIS mapping solutions and advanced damper labeling technologies is also being driven by a heightened focus on fire safety and building automation in both new and retrofitted structures. Additionally, the influx of foreign direct investment and the presence of a young, tech-savvy workforce are catalyzing market expansion, making Asia Pacific a hotspot for innovation and adoption in this sector.

Emerging economies in Latin America and the Middle East & Africa are gradually recognizing the importance of damper labeling and GIS mapping, particularly as they strive to enhance infrastructure resilience and safety standards. However, these regions face unique challenges such as limited technical expertise, budgetary constraints, and fragmented regulatory frameworks, which can impede widespread adoption. Despite these hurdles, localized demand is rising, especially in sectors like oil and gas, mining, and government infrastructure, where asset tracking and safety compliance are critical. With targeted policy reforms and international partnerships, these regions have the potential to unlock significant market opportunities over the forecast period.

Report Scope

Damper Labeling and GIS Mapping Market Outlook

As per our latest research, the global Damper Labeling and GIS Mapping market size reached USD 1.24 billion in 2024, reflecting a robust expansion driven by the integration of digital mapping technologies and regulatory compliance needs across industries. The market is exhibiting a healthy compound annual growth rate (CAGR) of 8.5% from 2025 to 2033, with the forecasted market size expected to reach approximately USD 2.57 billion by 2033. This growth is primarily fueled by the increasing adoption of smart building solutions, stringent fire safety regulations, and the rising need for accurate infrastructure management in both developed and developing regions.

One of the primary growth factors for the Damper Labeling and GIS Mapping market is the escalating demand for building automation and fire safety compliance across commercial, industrial, and residential sectors. As urbanization accelerates globally, there is a significant surge in the construction of smart buildings, where effective damper labeling and precise GIS mapping play a crucial role in ensuring safety, operational efficiency, and regulatory adherence. The integration of these solutions enhances real-time monitoring, maintenance scheduling, and emergency response, which are critical for modern infrastructure. Furthermore, government mandates and international standards such as ISO and NFPA are compelling property owners and facility managers to invest in advanced labeling and mapping technologies, thereby contributing to the market’s sustained growth.

Another pivotal driver is the rapid technological advancements in GIS mapping software and hardware components, which are making damper labeling more accurate, scalable, and user-friendly. The proliferation of IoT-enabled sensors, cloud-based platforms, and AI-driven analytics is transforming the landscape of facility management and infrastructure monitoring. These innovations enable stakeholders to visualize, analyze, and manage damper locations and conditions with unprecedented precision. Moreover, the growing emphasis on predictive maintenance and asset lifecycle management is pushing organizations to adopt comprehensive GIS mapping systems that seamlessly integrate with other building management solutions, further accelerating market expansion.

The increasing focus on sustainability and energy efficiency in building operations is also boosting the adoption of damper labeling and GIS mapping solutions. Efficient damper management is essential for optimizing HVAC systems, reducing energy consumption, and ensuring indoor air quality, all of which are critical for green building certifications such as LEED and BREEAM. As organizations strive to meet these sustainability targets, the need for accurate labeling and mapping becomes indispensable. Additionally, the integration of GIS mapping with mobile and cloud technologies allows for remote monitoring and real-time updates, enhancing operational agility and supporting the global trend towards digital transformation in facility management.

Regionally, North America dominates the Damper Labeling and GIS Mapping market due to its advanced infrastructure, stringent safety regulations, and high adoption of smart building technologies. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid urbanization, increasing construction activities, and government initiatives to modernize public infrastructure. Europe also holds a significant share, driven by strong regulatory frameworks and the widespread implementation of energy efficiency measures. The Middle East & Africa and Latin America are witnessing steady growth, supported by infrastructure development and rising awareness about building safety and compliance.

Component Analysis

The Component segment of the Damper Labeling and GIS Mapping market is composed of software, hardware, and services, each playing a vital role in the ecosystem. Software solutions are at the forefront, enabling users to design, manag

This map contains two layers, more information on the layers can be found here:NZ ImageryNZ Hybrid ReferenceProjectionNew Zealand Transverse Mercator 2000 (NZTM2000).Scale/zoom levels:Available scale levelsLevel 0 - 23 (1:591,657,527 - 1:70)About NZ ImageryThe New Zealand Imagery map is created by Eagle Technology and uses the best available publicly owned high resolution imagery.The map combines high resolution imagery (0.075m - 1.25m) that covers around 95% of New Zealand with the New Zealand 10m Aerial Imagery. The 10m imagery is used for the smaller scales for a more consistent map and for areas where no high resolution imagery is available. This map is updated regularly with the latest high resolution imagery.A layer with the imagery footprints and metadata is available here.About NZ Hybrid ReferenceThis vector tile layer provides a detailed reference layer for New Zealand in the NZ Transverse Mercator projection. The style is based on the Esri World Hybrid Imagery style. This vector tile layer provides unique capabilities for customization and high-resolution display. This map includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, and administrative boundaries for added context.This layer is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers services that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or remarks about the content, please let us know at livingatlas@eagle.co.nz

The Terrain with Labels (US Edition) web map includes populated places, admin areas, boundary lines and roads overlaying multidirectional hillshade. The minimal features and styling is designed to draw attention to your thematic content.This basemap is available in the United States Vector Basemaps gallery and uses the World Terrain Reference (US Edition) and World Terrain Base vector tile layers and World Hillshade.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This vector tile layer presents the Environment Surface Water and Label style (World Edition) and provides a global reference of surface water features and their respective labels. This layer is designed for use with the Environment Base, Environment Detail and Label, and Environment Watersheds to combine into one unified basemap. This vector tile layer provides unique capabilities for customization and high-resolution display. This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Environment Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

The Terrain with Labels (World Edition) web map includes populated places, admin areas, boundary lines and roads overlaying an artistic multidirectional hillshade. The minimal features and styling is designed to draw attention to your thematic content. This basemap, included in the ArcGIS Living Atlas of the World, the World Terrain Reference and World Terrain Base vector tile layers and World Hillshade.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

This vector tile layer presents the Human Geography Dark Label style (World Edition) and provides a detailed vector basemap for the world with a dark monochromatic style and content adjusted to support Human Geography information. The map includes labels for highways, major roads, minor roads, railways, water features, building footprints, and administrative boundaries. It is designed to be used with the Human Geography Dark Detail and Human Geography Dark Base layers. Learn more about this basemap's design from the cartographic designer in this blog. This vector tile layer provides unique capabilities for customization, high-resolution display, and use in mobile devices.This vector tile layer is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.This layer is used in the Human Geography Dark Map web map included in ArcGIS Living Atlas of the World.See the Vector Basemaps group for other vector tile layers. Customize this StyleLearn more about customizing this vector basemap style using the Vector Tile Style Editor. Additional details are available in ArcGIS Online Blogs and the Esri Vector Basemaps Reference Document.

The MassDEP Wetlands datalayer comprises two feature types, polygons and arcs (lines). The attribute codes in the polygon layer describe different types of wetland environments and the arc attributes describe line types based on adjacent polygon types or arcs defined as hydrologic connections.In this service the wetland areas are displayed using outlines and labeled with wetland code abbreviation. This symbology is useful for displaying the data atop aerial imagery.Please see https://www.mass.gov/info-details/massgis-data-massdep-wetlands-2005 for more details.

A general-purpose basemap designed to support a wide variety of web-based mapping needs. Through its countywide display of highways and streets, waterbodies, incorporated cities, and parks, the vector tile of labels is suitable as a stand-alone, general-labeling and to be used for thematic data display over operational map overlays. The map was designed specifically for use in ArcGIS Online, with scale-dependent layers and label classes customized for the Google/Bing Web Mercator tiling scheme.

Formerly known as the Massachusetts Highway Department (MHD) Roads, then the Executive Office of Transportation - Office of Transportation Planning (EOT-OTP) Roads, the MassGIS-MassDOT Roads layer includes linework from the 1:5,000 road and rail centerlines data that were interpreted as part of the 1990's Aerial Imagery project. The Massachusetts Department of Transportation - Office of Transportation Planning, which maintains the primary source for this layer, continues to add linework from municipal and other sources and update existing linework using the most recent aerial ortho imagery as a base. The attribute table includes many "road inventory" fields maintained in MassDOT's linear referencing system.The current MassGIS-MassDOT hybrid data layer was first published in November 2018, based on the MassDOT 2017 year-end Road Inventory layer and results of a 2014-2015 MassDOT-Central Transportation Planning Staff project to conflate street names and other attributes from MassGIS' "base streets" to the MassDOT Road Inventory linework. The base streets are continually maintained by MassGIS as part of the NextGen 911 and Master Address Database (MAD) projects. MassGIS staff reviewed the conflated layer and added many base street arcs digitized after the completion of the conflation work. MassGIS added several fields to support legacy symbology and labeling. Other edits included modifying some linework in areas of recent construction and roadway reconfiguration to align to 2017-2018 Google ortho imagery, and making minor fixes to attributes and linework. MassGIS continues to modify the layer as needed, modifying the linework using the latest aerial imagery and adding line features from the base street arcs.From this data layer MassGIS extracted Major Roads and Major Highway Routes layers.See full metadata

Authority In the 1963 general session, the Utah State Legislature charged the Division of Water Resources with the responsibility of developing a State Water Plan. This plan is to coordinate and direct the activities of state and federal agencies concerned with Utah’s water resources. As a part of this objective, the Division of Water Resources collects water-related land use data for the entire state. This data includes the types and extent of irrigated crops as well as information concerning phreatophytes, wet/open water areas, dry land agriculture and urban areas. The data produced by the water-related land use program are used for various planning purposes. Some of these include: determining cropland water use, evaluating irrigated land losses and conversion to urban uses, planning for new water development, estimating irrigated acreages for any area, and developing water budgets. Additionally, the data are used by many other state and federal agencies. Previous Methods The land use inventory methods used by the division in conducting water-related land use studies have varied with regard to the procedures used and the precision obtained. During the 1960s and 70s, inventories were prepared using large format vertical-aerial photographs supplemented with field surveys to label boundaries, vegetation types, and other water use information. After identifying crops and labeling photographs, the information was transferred onto a base map and then planimetered or "dot-counted" to determine the acreage. Tables for individual townships and ranges were prepared showing the amount of land in each land use category within each section. Data were then available for use in preparing water budgets. In the early 1980s, the division began updating its methodology for collecting water-related land use data to take advantage of the rapidly growing fields of Remote Sensing and computerized Geographic Information Systems (GIS). For several years during the early 1980’s, the division contracted with the University of Utah Research Institute, Center for Remote Sensing and Cartography (CRSC), to prepare water-related land use inventories. During this period, water-related land use data was obtained by using high altitude color infrared photography and laboratory interpretation, with field checking. In March 1984, several division staff members visited the California Department of Water Resources to observe its methodology for collecting water-related land use data for state water planning purposes. Based on its review of the California methodology and its own experience, the division developed a water-related land use inventory program. This program included the use of 35mm slides, United States Geological Survey (USGS) 7-1/2 minute quadrangle maps, field-mapping using base maps produced from the 35mm photography and a computerized GIS to process, store and retrieve land use data. Areas for survey were first identified from previous land use studies and any other available information. The identified areas were then photographed using an aircraft carrying a high quality 35mm single lens reflex camera mounted to focus along a vertical axis to the earth. Photos were taken between 6,000 and 6,500 feet above the ground using a 24mm lens. This procedure allowed each slide to cover a little more than one square mile with approximately 30 percent overlap on the wide side of the slide and 5 percent on the slide's narrow side. The slides were then indexed according to a flight-line number, slide number, latitude and longitude. All 35mm slides were stored in files at the division offices and cataloged according to township, range and section, and quadrangle map location. Water-related land use areas were then transferred from the slide to USGS 7-1/2 minute quadrangle maps using a standard slide projector with a 100-200mm zoom lens. This step allowed the technician to project the slide onto the back of a quadrangle map. The image showing through the map was adjusted to the map scale with the zoom lens. Field boundaries and other water-use boundaries were then traced on the 7-1/2 minute quadrangle map. Next, a team was sent to use the map in the field to check the boundaries and current year land use field data on the 7-1/2 minute quadrangles. The final step was to digitize and process the field data using ARC/INFO software developed by Environmental Systems Research Institute (ESRI). Starting in 2000 with the land use survey of the Uintah Basin, the division further improved its land use program by using digital data for the purposes of outlining agricultural and other land cover boundaries. The division used satellite data, USGS Digital Orthophoto Quadrangles (DOQs), National Agricultural Imagery Program (NAIP), and other digital images in a heads-up digitizing mode for this process. This allowed the division to use multiple technicians for the digitizing process. Digitizing was done as line and polygon files using ArcView 3.2 with a satellite image, DOQ or NAIP image as a background with other layers added for reference. Boundary files were created in logical groups so that the process of edge-matching along quad lines was eliminated and precision increased. Subsequent inventories were digitized in the ArcMap 9.x software versions. Present Methodology Using the latest statewide NAIP Imagery and ArcGIS 10, all boundaries of individual agricultural fields, urban areas, and significant riparian areas are precisely digitized. Once the process of boundary digitizing is done, the polygons are loaded onto tablet PCs. Field crews are then sent to field check the crop and irrigation type for each agricultural polygon and label the shapefiles accordingly. Each tablet PC is attached to a GPS unit for real-time tracking to continuously update the field crew’s location during the field labeling process. This improved process has saved the division much time and money and even greater savings will be realized as the new statewide field boundaries are completed. Once processed and quality checked, the data is filed in the State Geographic Information Database (SGID) maintained by the State Automated Geographic Reference Center (AGRC). Once in the SGID, the data becomes available to the public. At this point, the data is also ready for use in preparing various planning studies. In conducting water-related land use inventories, the division attempts to inventory all lands or areas that consume or evaporate water other than natural precipitation. Areas not inventoried are mainly desert, rangeland and forested areas. Wet/open water areas and dry land agriculture areas are mapped if they are within or border irrigated lands. As a result, the numbers of acres of wet/open water areas and dry land agriculture reported by the division may not represent all such areas in a basin or county. During land use inventories, the division uses 11 hydrologic basins as the basic collection units. County data is obtained from the basin data. The water-related land use data collected statewide covers more than 4.3 million acres of dry and irrigated agricultural land. This represents about 8 percent of the total land area in the state. Due to changes in methodology, improvements in imagery, and upgrades in software and hardware, increasingly more refined inventories have been made in each succeeding year of the Water-Related Land Use Inventory. While this improves the data we report, it also makes comparisons to past years difficult. Making comparisons between datasets is still useful; however, increases or decreases in acres reported should not be construed to represent definite trends or total amounts of change up or down. To estimate such trends or change, more analysis is required.

This dataset contains raw GIS data sourced from the BAG (Basisregistratie Adressen en Gebouwen; Registry of Addresses and Buildings). It provides comprehensive information on buildings, including advanced height data and administrative details. It also contains geographic divisions within The Hague. Additionally, the dataset incorporates energy label data, offering insights into the energy efficiency and performance of these buildings. This combined dataset serves as the backbone of a Master's thesis in Industrial Ecology, analysing residential and office cooling and its environmental impacts in The Hague, Netherlands. The codebase of this analysis can be found in this Github repository: https://github.com/simonvanlierde/msc-thesis-ie

The dataset includes a background research spreadsheet containing supporting calculations. It also presents geopackages with results from the cooling demand model (CDM) for various scenarios: Status quo (SQ), 2030, and 2050 scenarios (Low, Medium, and High)

Background research data

The background_research_data.xlsx spreadsheet contains comprehensive background research calculations supporting the shaping of input parameters used in the model. It contains several sheets:

• Cooling Technologies: Details the various cooling technologies examined in the study, summarizing their characteristics and the market penetration mixes used in the analysis.
• LCA Results of Ventilation Systems: Provides an overview of the ecoinvent processes serving as proxies for the life-cycle impacts of cooling equipment, along with calculations of the weight of cooling systems and contribution tables from the LCA-based assessment.
• Material Scarcity: A detailed examination of the critical raw material content in the material footprint of ecoinvent processes, representing cooling equipment.
• Heat Plans per Neighbourhood: Forecasts of future heating solutions for each neighbourhood in The Hague.
• Building Stock: Analysis of the projected growth trends in residential and office building stocks in The Hague. AC Market: Market analysis covering air conditioner sales in the Netherlands from 2002 to 2022.
• Climate Change: Computations of climate-related parameters based on KNMI climate scenarios.
• Electricity Mix Analysis: Analysis of future projections for the Dutch electricity grid and calculations of life-cycle carbon intensities of the grid.

Input data

Geographic divisions

• The outline of The Hague municipality through the Municipal boundaries (Gemeenten) layer, sourced from the Administrative boundaries (Bestuurlijke Gemeenten) dataset on the PDOK WFS service.
• District (Wijken) and Neighbourhood (Buurten) layers were downloaded from the PDOK WFS service (from the CBS Wijken en Buurten 2022 data package) and clipped to the outline of The Hague.
• The 4-digit postcodes layer was downloaded from PDOK WFS service (CBS Postcode4 statistieken 2020) and clipped to The Hague's outline. The postcodes within The Hague were subsequently stored in a csv file.
• The census block layer was downloaded from the PDOK WFS service (from the CBS Vierkantstatistieken 100m 2021 data package) and also clipped to the outline of The Hague.
• These layers have been combined in the GeographicDivisions_TheHague GeoPackage.

BAG data

• BAG data was acquired through the download of a BAG GeoPackage from the BAG ATOM download page.
• In the resulting GeoPackage, the Residences (Verblijfsobject) and Building (Pand) layers were clipped to match The Hague's outline.
• The resulting residence data can be found in the BAG_buildings_TheHague GeoPackage.

3D BAG

• Due to limitations imposed by the PDOK WFS service, which restricts the number of downloadable buildings to 10,000, it was necessary to acquire 145 individual GeoPackages for tiles covering The Hague from the 3D BAG website.
• These GeoPackages were merged using the ogr2ogr append function from the GDAL library in bash.
• Roof elevation data was extracted from the LoD 1.2 2D layer from the resulting GeoPackage.
• Ground elevation data was obtained from the Pand layer.
• Both of these layers were clipped to match The Hague's outline.
• Roof and ground elevation data from the LoD 1.2 2D and Pand layers were joined to the Pand layer in the BAG dataset using the BAG ID of each building.
• The resulting data can be found in the BAG_buildings_TheHague GeoPackage.

Energy labels

• Energy labels were downloaded from the Energy label registry (EP-online) and stored in energy_labels_TheNetherlands.csv.

UHI effect data

• A bitmap with the UHI effect intensity in The Hague was retrieved from the from the Dutch Natural Capital Atlas (Atlas Natuurlijk Kapitaal) and stored in UHI_effect_TheHague.tiff.

Output data

• The residence-level data joined to the building layer is contained in the BAG_buildings_with_residence_data_full GeoPackage.
• The results for each building, according to different scenarios, are compiled in the buildings_with_CDM_results_[scenario]_full GeoPackages. The scenarios are abbreviated as follows:
  • SQ: Status Quo, covering the 2018-2022 reference period.
  • 2030: An average scenario projected for the year 2030.
  • 2050_L: A low-impact, best-case scenario for 2050.
  • 2050_M: A medium-impact, moderate scenario for 2050.
  • 2050_H: A high-impact, worst-case scenario for 2050.

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.

National Geospatial Data Asset

This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee.

Dataset Source Details

Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Cartographic Visualization

The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below.

Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html

Contact

Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip

Attribute Structure

The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension

These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE

The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB.

Core Attributes

The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields.

County Code and Country Name Fields

“CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard.

The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.

Descriptive Fields

The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes

Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line.

ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line

A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively.

The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps.

The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line.

Use of Core Attributes in Cartographic Visualization

Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between:

• International Boundaries (Rank 1);
• Other Lines of International Separation (Rank 2); and
• Special Lines (Rank 3).

Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction.

The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling.

Use of

Fuquay-Varina Streets in a simplified format, meaning Wake County Street segments were dissolved based on road name to create single road features for each unique road name, instead of segments between each road intersection. This is sometimes useful for cartography and labeling purposes. Wake County GIS creates and maintains the official street transportation data for the whole county. ToFV GIS has "clipped" this data to our Urban Service Area for convenience. Data is automatically updated nightly from Wake County's Open Data source, and so should always be the same data as from Wake County.

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

This dataset supports the development of machine learning models for vegetation segmentation in Antarctic ecosystems and enables reproducible remote sensing analyses by providing both imagery and associated ground-truth labels in standard GIS-compatible formats.

1. Ground Truth Labels This record contains manually and semi-automatically generated pixelwise shapefiles, used to annotate vegetation types with high confidence. This component contains both manually and semi-automatically generated pixel-wise shapefiles classifying four vegetation classes: Usnea spp., black lichen, moss, and non-vegetation. The purpose of these labels is to provide high-quality reference data for training and validating machine learning models for vegetation segmentation.

Manual labelling was conducted using ground truth points gathered during field campaigns. Polygons were drawn around confidently identified vegetation patches, focusing on central areas to minimise edge misclassification. (Files: .shp, .dbf, .prj, .shx, and readme.txt)

Semi-automatic labelling was introduced to address the limitations of manual annotation at a GSD of 2.93 cm/pixel. A suite of 28 vegetation indices (VIs) were evaluated, and three (MSAVI and GNDVI) were selected for their spectral separability between classes. (Files: .shp, .dbf, .prj, .shx, and readme.txt)

2. Orthomosaic The RGB and multispectral (MS) images captured by UAVs were processed into high-resolution orthomosaics using Agisoft Metashape 1.6.6 and georeferenced via QGIS 3.2.0. These orthomosaics serve as the base imagery for generating labels and training machine learning models. Output formats include .tif for orthomosaics, .ovr for overviews, and .pdf reports documenting the image processing steps.

Data Collection and Analysis Imagery was captured in January 2023 at Robinson Ridge, Antarctica, using a BMR3.9RTK UAV equipped with a MicaSense Altum sensor and Sony Alpha 5100 camera, flown at 70 m altitude (GSD ≈ 2.93 cm/pixel). Over 2,800 images were collected over ~5.15 ha.

Usage Notes · Embargoed files require permission for access. · Refer to the included readme.txt files in each record for file structure, formats, and usage instructions. · The dataset is optimised for developing and validating deep learning models for remote sensing classification in polar environments.

The Environment Map (US Edition) web map consists of vector tile layers that form a detailed basemap for the world, featuring a neutral style with content adjusted to support environment, landscape, natural resources, hydrologic and physical geography layers. The layers in this map provide unique capabilities for customization, high-resolution display and offline use in mobile devices. They are built using the same data sources used for other Esri basemaps.This basemap is available in the United States Vector Basemaps gallery and consists of 4 vector tile layers and one raster tile layer: The Environment Detail and Label (US Edition) vector tile reference layer for the world with administrative boundaries and labels; populated places with names; ocean names; topographic features; and rail, road, park, school, and hospital labels. The Environment Surface Water and Label vector tile surface water layer for the world with rivers, lakes, streams, and canals with respective labels. The Environment Watersheds vector tile layer that provides watersheds boundaries. The Environment Base multisource base layer for the world with vegetation, parks, farming areas, open space, indigenous lands, military bases, bathymetry, large scale contours, elevation values, airports, zoos, golf courses, cemeteries, hospitals, schools, urban areas, and building footprints. World Hillshade raster tile layerThe vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Levee stations, usually in feet but in some cases miles, snapped to 2017 Delta levee centerlines (derived from the 2017 Delta LiDAR). Base source for station locations are surveyed field markers on the levees or distance-derived CAD files, in either case as supplied by local maintaining agency's engineers. DWR collected station location data and snapped the stations into the levee centerline file from 2012. After updated levee centerlines were created, the existing points were snapped to the new lines. So there is some small difference between the supplied station locations, previous station locations and these station locations. In some cases, multiple series of stations exist for a district, generally associated with distinct waterways. Also, district levees may be demarked in feet or in miles. The label fields are simply cartographic support, the label data are identical in all cases, but are provided to support fast labeling at more infrequent intervals as needed. Stationing is not as simple as it may seem. In some cases, multiple sets of stationing exist for a district's levees (see Sherman Island for example). What this dataset intends to represent is the current stationing used by District engineers for that District on levee maintenance and improvement projects. As changes are made to the stationing, and the new stationing data become available to the Levee Program, they will be added to this database. Some islands also have separate groups of stations for various parts of the district. This version is current as of 03/24/2020. Source of the original levee stationing is DWR Delta Levees Program, compiled from data provided by internal files, from CSU Chico State, MBK Engineers, KSN Engineers, Siegfried Engineers, Malani & Associates, Green Mountain Engineers, and DCC Engineers. Processing work done by CA DWR, Division of Engineering, Geodetic Branch, Geospatial Data Support Section, specifically by Arina Ushakova (Research Data Analyst I), and initial QC by Joel Dudas (Senior Engineer, Water Resources).

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.