The mapping of flow beds during periods of low water or in a retention basin in the City of Laval can only be used for information purposes. Any drainage bed present on a site must be analyzed, as part of an environmental study carried out by the applicant for a permit or authorization certificate, in order to confirm whether it is a ditch or a watercourse. The map can be modified at any time by the City of Laval to reflect the most up-to-date data it has.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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. To produce the digital map 38 map units (21 vegetated and 17 land use) were developed and directly cross-walked or matched to their 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.

This record is maintained in the National Geologic Map Database (NGMDB). The NGMDB is a Congressionally mandated national archive of geoscience maps, reports, and stratigraphic information, developed according to standards defined by the cooperators, i.e., the USGS and the Association of American State Geologists (AASG). Included in this system is a comprehensive set of publication citations, stratigraphic nomenclature, downloadable content, unpublished source information, and guidance on standards development. The NGMDB contains information on more than 90,000 maps and related geoscience reports published from the early 1800s to the present day, by more than 630 agencies, universities, associations, and private companies. For more information, please see http://ngmdb.usgs.gov/.

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.

For four of the map units – 3-SDF, 4-SDAF, 27-POHV, and 31-LBY – modeling using GIS principles was also employed. Modeling involves using environmental conditions of a map unit, such as elevation, slope, and aspect, which were determined by the field-collected ecological data. Data satisfying these conditions were obtained from ancillary data sources, such as USGS DEM data. These data were fed into a model that will result in locations (pixels) where all the desired conditions exist. For example, if a certain map unit was a shrubland that predominantly occurs above 8000 feet, on slopes of 3-10%, and on west-facing aspects, the correctly-constructed model will output only locations where this combination of conditions will be found. The resulting areas were then examined manually with the traditional photo interpretation process to confirm that they indeed could be accepted as that map unit. If photo interpretation determines that the areas were not acceptable, then they were changed to a more appropriate map unit.

Observed Eelgrass Beds 1993-95 is 1:24,000-scale data. The purpose of this datalayer is to depict the locations of observed eelgrass along Connecticut's coast. The polygons in this layer were created from the Eelgrass Sample Points layer. This layer is not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Potential Eelgrass Beds 1993-95 is 1:24,000-scale data. The purpose of this datalayer is to depict the locations of potential eelgrass growth along Connecticut's coast. The polygons in this layer were created from two source layers: Eelgrass Sample Points and Observed Eelgrass Beds. This layer is not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Analysis of ‘Eelgrass Beds Historic Polygon’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/63eb4a2f-8d12-4913-8df4-4d73929e5b9e on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Eelgrass Beds Historic Set:

Historic Eelgrass Points is a 1:24,000-scale, point feature-based layer that depicts the locations of historic eelgrass beds (Zostera marina) in Long Island Sound, the Connecticut River, the Quinnipiac River and other bays, harbors and waterbodies in Connecticut's coastal area. It also includes several points located along the north shore of Long Island. There are a total of 131 point features, the majority of which are located east of the Connecticut River. Point features in this layer are compiled from two major sources: 1) the polygon feature label points in the Historic Eelgrass Beds polygon layer representing sources with a mapping component; and 2) additional points that were based on historic literature review that had no mapping component. Source information including source description and collection date for each point is described in the layer's table data. Feature locations are inexact. Because of the variety of source maps and methods used for their automation, this coverage should be considered to have limited spatial accuracy and is appropriate for general uses only. Actual data collection ranged from 1873 through 1996. This layer was published in 1997 and is not updated. It does not represent current conditions.

Historic Eelgrass Bed Polygons is a 1:24,000-scale, polygon feature-based layer that depicts the locations of historic eelgrass beds (Zostera marina) in Long Island Sound and the Niantic River, as well as in other bays, harbors and waterbodies in Connecticut's coastal area. It also includes several points located along the north shore of Long Island. There are a total of 52 polygon features, all of which (except the Long Island points), are located within or east of the Niantic River. This layer can be used with Historic Eelgrass Points. This layer does not represent current conditions. Rather, it depicts historic eelgrass bed locations that were observed and defined either cartographically or narratively over the course of many years and from various sources. The dates of each source's data collection are noted in the attribute table. Feature locations are inexact. Because of the variety of source maps and methods used for their automation, this information should be considered to have limited spatial accuracy and is appropriate for general uses only. The data was taken from maps of various scales and projections that were drawn between 1905 and 1996. These maps were reduced to approximately 1:24,000 scale and adjusted for best fit; eelgrass areas were redrafted onto USGS Topographic Quadrangle maps for digitizing. In order to create a single polygon coverage, areas were considered to represent a maximum extent of eelgrass beds. This layer was published in 1997 and is not updated.

--- Original source retains full ownership of the source dataset ---

description: The Digital Geologic Map of Agate Fossil Beds National Monument and vicinity, Nebraska is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Agate Fossil Beds National Monument.; abstract: The Digital Geologic Map of Agate Fossil Beds National Monument and vicinity, Nebraska is composed of GIS data layers, two ancillary GIS tables, a Windows Help File with ancillary map text, figures and tables, GIS data layer and table FGDC metadata and ArcView 3.X legend (.AVL) files. The data were completed as a component of the Geologic Resource Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). All GIS and ancillary tables were produced as per the NPS GIS-Geology Coverage/Shapefile Data Model (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as coverage and table export (.E00) files, and as a shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 13N. That data is within the area of interest of Agate Fossil Beds National Monument.

Echogram SLIC superpixel parameters.

Shellfish Beds Managed Set: The Connecticut Department of Environmental Protection cooperated with the Department of Agriculture, Bureau of Aquaculture to publish the Connecticut Mananged Shellfish Bed data. More recent information may now be available from Department of Agriculture since the time this information was originally published in 2004. Connecticut Shellfish Bed Mapping - The Town_Merge data layer is one of four layers that were created in the mapping of all managed shellfish beds in Connecticut waters. These beds, as defined below, include state managed beds, municipally managed beds, natural beds and recreational beds. These four bed types were mapped as separate data layers. This project was undertaken to assist three agencies, The National Oceanic and Atmospheric Administration (NOAA), Connecticut Department of Environmental Protection (CTDEP) and the Connecticut Department of Agriculture Bureau of Aquaculture (DA/BA). While the over all goal of all three was the same, namely the protection of natural resources, each had different specific needs. The project was originally undertaken without NOAA involvement. In 2001Public Act PA01-115 An Act Concerning Recreational Fishing in Connecticut was passed. Information on this act can be found through the Connecticut State Library at http://www.cslib.org/psaindex.htm. This act required DA/DB and CTDEP to determine the "effects of commercial and recreational fishing" on eel grass beds. Harry Yamalis from the Office of Long Island Sound Programs (OLISP) initiated gathering information and mapping the beds on a part time basis in response to this Public Act. Later, NOAA requested assistance in building a national database of Marine Managed Areas (MMA) in accordance with federal Executive Order 13158 concerning Marine Protected Areas (MPA). NOAA and CTDEP agreed that the shellfish beds met the criteria for MMA's. Tom Ouellette from OLISP was the liaison between CTDEP and NOAA and became the project supervisor. Todd Coniff was hired as an intern through Coastal State Organization,which is overseeing the MMA inventory collection program for NOAA, to continue the work on a full time basis. Several people from the Environmental and Geographic Information Center at CTDEP provided technical and other guidance. As noted earlier each agency had its on agenda for mapping the shellfish beds. The follow paragraphs outline the wants and needs of NOAA, DA/BA and CTDEP. The following is a description of the process and function of the MMA inventory for NOAA. The following excerpt was taken from the MPA web site http://www.mpa.gov/. The Marine Managed Areas Inventory Database and Data Collection Process The inventory will contain a wide range of information on each site to help the U.S. develop a comprehensive picture of the nation's marine managed areas (MMAs). The data collected include a general description and site characteristics such as location, purpose, and type of site, along with detailed information on natural and cultural resources, legal authorities, site management, regulations, and restrictions (see MMA Inventory Database Description at http://www.mpa.gov/inventory/database_description.html). The data collection process begins with agencies or authorities that manage marine and Great Lakes areas in U.S. waters. Each agency reviews sites in their programs to identify those that meet the MMA working criteria. Data collection is then conducted for each site by the managing agency with an electronic data entry form. The managing agencies also review and approve the data before submission to the NOAA/Department of the Interior Inventory team. The data are then reviewed and made public on MPA.GOV. A data update and revision process is being developed to ensure that the information in the inventory is kept current over time Purposes of MMA Inventory The national inventory provides a range of data on all types of MMAs in the U.S. This database can help federal, regional, state, a

The Python code provided generates polygonal maps resembling geographical landscapes, where certain areas may represent features like lakes or inaccessible regions. These maps are generated with specified characteristics such as regularity, gap density, and gap scale.

Features:

1. Polygon Generation:

   • The code utilizes the Shapely library to generate polygonal shapes within specified bounding boxes. These polygons serve as the primary representation of the map.

2. Gap Generation:

   • Within the generated polygons, the code introduces gaps to simulate features like lakes or inaccessible areas. These gaps are represented as holes within the central polygon.
3. Forest Generation
   • Within the generated polygons, the code introduces different forest areas. These forest are added like a new Feature inside the GEOJSON.

4. Parameterized Generation:

   • The generation process is parameterized, allowing control over features such as regularity (shape uniformity), gap density (homogeneity of gaps), and gap scale (size of gaps relative to the polygon).

Components:

1. PolygonGenerator Class:

   • Responsible for generating the outer polygon shape and introducing gaps to simulate features.
   • Offers methods to generate individual polygons with specified characteristics.

2. Parameter Ranges and Experimentation:

   • The code includes predefined ranges for regularity, gap density, vertex number, bounding box, forest density and forest scale range in 3 different CSV.
   • It conducts experiments by generating maps with different parameter combinations, offering insights into how these parameters affect the map's appearance.

Usage:

1. Map Generation:

   • Users can instantiate the PolygonGenerator class to generate individual polygons representing maps with specific features.
   • Parameters such as regularity, gap density, and gap scale can be adjusted to customize the map generation process.

2. Experimentation:

   • Users can experiment with different parameter combinations to observe the effects on map generation.
   • This allows for exploration and understanding of how different parameters influence the characteristics of generated maps.

Potential Applications:

• The code can be used in various applications requiring the generation of simulated landscapes, such as in gaming, geographical analysis, or educational tools.
• It provides a flexible and customizable framework for creating maps with specific features, allowing users to tailor the generated maps to their requirements.
• Can be applied to generate maps for drone scanning operations, facilitating optimized area division and efficient data collection.

Eelgrass Beds 93-95 Set:

Eelgrass Sample Points is a 1:24,000-scale, point feature-based layer that depicts the locations where eelgrass (Zostera marina) was either observed or where a location would be potentially favorable for future eelgrass growth. Sample points were taken along Connecticut's coast in Long Island Sound, and in major bays, harbors and rivers along the shoreline. The point features in this layer were compiled from field research using global positioning system (GPS) equipment. Feature locations were not always exact due to equipment failure or lack of satellite reception. In those cases, points were estimated from field notes. Some point locations were corrected based on field notes or hydrography and bathymetry conditions at the sample point location. The number of field points that were altered were as follows: In 1993, 32 of 290 points (11%); in 1994, 93 of 454 points (20%); in 1995, 37 of 105 points (35%). Data compilation occurred on 17 days between 7/21/1993 and 11/16/1995; exact dates of each source's data collection are noted in the attribute table. A total of 849 point locations were surveyed. The westernmost point is Frash Pond in Stratford, Connecticut and the easternmost point is the Pawcatuck River on the Connecticut/Rhode Island Border. Eelgrass was found at 484 locations and was described as either high, medium, or low density, or simply as present or absent. Eelgrass was absent at 365 locations. Publication of the datalayer was in 1997. This layer is not updated. This layer does not represent current conditions.

Observed Eelgrass Beds is a 1:24,000 scale, polygon feature-based layer that depicts the locations of observed eelgrass beds in Long Island Sound, in major rivers, and within bays, harbors and other waterbodies along Connecticut's coast. The layer is based on information from the Eelgrass Sample Points layer. It represents conditions at a particular point in time (1993 to 1995). During the 1993-95 field seasons a team of researchers from the University of Connecticut Dept. of Ecology and Evolutionary Biology led by Charles Yarish, equipped with a Global Positioning System (GPS), SCUBA, and a 20' boat surveyed over 800 potential eelgrass locations. Their GPS coordinates and field notes were used to create a point coverage entitled Eelgrass Sample Points, which was plotted and checked on a 1:24000 scale base map of the Connecticut shore. These point locations, observations, and the nearshore bathymetry were then used to delineate areas representing both observed and potential eelgrass beds. Eelgrass beds were initially digitized at 1:24,000 scale, but have been edited and revised on screen at higher resolution. Keeping in mind the temporal and spatial variability of eelgrass, beds may vary in size, shape, and density from year to year. Feature locations may not always be exact due to equipment failure or lack of satellite reception. In those cases, points were estimated from field notes. It should be noted that the Observed Eelgrass Beds layer is not a complete dataset of all observed eelgrass sites in Connecticut and/or Long Island Sound. This layer was published in 1997 and is not updated. It does not represent current conditions. There are 101 polygon features representing observed eelgrass beds in this layer. Geographic locations are as follows: westernmost areas: Clinton Harbor; easternmost areas: Little Narragansett Bay, Rhode Island waters. Observed beds range in size from .003 acre (131 sq. ft.) to 49 acres (2,136,608 sq. ft.). The total area of observed beds is 632.6 acres.

Potential Eelgrass Beds is a 1:24,000 scale, polygon feature-based layer that depicts the locations of potential eelgrass beds in Long Island Sound, in major rivers, and within bays, harbors and other waterbodies along Connecticut's coast. The layer is based on information from the Observed Eelgrass Beds and Eelgrass Sample Points layers. It represents conditions at a particular point in time (1993 to 1995). During the 1993-95 field seasons a team of researchers from the University of Connecticut Dept. of Ecology and Evolutionary Biology led by Charles Yarish, equipped with a Global Positioning System (GPS), SCUBA, and a 20' boat surveyed over 800 potential eelgrass locations. Their GPS coordinates and field notes were used to create a point coverage entitled Eelgrass Sample Points, which was plotted and checked on a 1:24000 scale base map of the Connecticut shore. These point locations, observations, and the nearshore bathymetry were then used to delineate areas representing both observed and potential eelgrass beds. Eelgrass beds were initially digitized at 1:24,000 scale, but have been edited and revised on screen at higher resolution. Potential beds, where not individually delineated, were created by buffering observed beds a distance of 33 feet (10 meters). These buffered polygons were intersected with buffered (distance of 5 ft.) shoreline arcs to keep potential polygons a minimum distance off the shoreline. These potential beds are considered to be areas where eelgrass is likely to spread to under ideal conditions, where eelgrass may exist in small isolated patches, where eelgrass may exhibit high temporal variability, or perhaps where restoration projects could be undertaken. Feature locations may not always be exact due to equipment failure or lack of satellite reception. In those cases, points were estimated from field notes. It should be noted that the Potential Eelgrass Beds layer is not a complete dataset of all potential eelgrass sites in Connecticut and/or Long Island Sound. This layer was published in 1997 and is not updated. It does not represent current conditions. There are 67 polygon features representing potential eelgrass beds in this layer. Geographic locations are as follows: westernmost areas: Clinton Harbor; easternmost areas: Little Narragansett Bay, Rhode Island waters. Potential beds range in size from .179 acre (318 sq. ft.) to 471 acres (20,551,582 sq. ft.). Potential beds contain 0 to 9 observed beds, and from 0 to 88.1% area covered by observed beds. The total area of potential beds is 2,196 acres.

On the perimeter of the SAGE, the watercourses are not very mobile (plain watercourses). Only the Ill (between Maison-Rouge and Illhaeusern) and the Bornen downstream have mobile or potentially mobile sections. The mobility spindle corresponds to the part of the major bed in which the meandering and displacement of the bed are active. to the rambling space in which the watercourse

Download a map document showing nearshore habitat in the Strait of Georgia. This map includes the following datasets: a kelp beds layer from GeoBC available herean eelgrass layer available from GeoBC herean eelgrass layer from Islands Trust Conservancy available hereand the BC ShoreZone dataset (available here) which has been filtered down to show shore-types classified as 'estuary, marsh or lagoon'.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).The file is georeferenced to earth's surface using the State Plane projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12,000.

An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. However, due to partial observability, agents often struggle to determine optimal strategies. Thus, developing a robust information fusion method is crucial for addressing these challenges. Information fusion expands the observation range of each agent, thereby enhancing the overall performance of the MAPF system. This paper explores a fusion approach in both temporal and spatial dimensions based on Graph Attention Networks (GAT). Since MAPF is a long-horizon, continuous task, leveraging historical observation dependencies is key for predicting future actions. Initially, historical observations are fused by incorporating a Gated Recurrent Unit (GRU) with a Convolutional Neural Network (CNN), extracting local observations to form an encoder. Next, GAT is used to enable inter-agent communication, utilizing the stability of the scaled dot-product aggregation to merge agents’ information. Finally, the aggregated data is decoded into the agent’s final action strategy, effectively solving the partial observability problem. Experimental results show that the proposed method improves accuracy and time efficiency by 24.5%, 47%, and 37.5%, 73% over GNN and GAT, respectively, under varying map sizes and agent densities. Notably, the performance enhancement is more pronounced in larger maps, highlighting the algorithm’s scalability.

This is a dataset download, not a document. The Open button will start the download.In 2015, the Oregon Biodiversity Information Center at Portland State University worked with the Oregon Department of Fish and Wildlife (ODFW), to assist in their 2015 conservation strategy update. This work involved updating the maps of each of ODFW’s conservation strategy habitats originally created for the first strategy in 2006,and integrating these into a 2015 strategy habitat map. The updated maps took advantage of new data and spatial modeling tools. However, strategy habitats only represent only 11 of the approximately 77 Oregon habitats, and are only mapped in the ecoregions in which they are conservation priorities. As a result, there was a strong interest in using this 2015 data to create a statewide, comprehensive habitat map. In 2017, the Oregon Department of Administrative Services, Geographic Enterprise Office (DAS-GEO), through their Framework Implementation program, with additional support from ODFW, funded the completion of a statewide habitat map, which was completed at the end of 2018. The habitat map is a compilation of a number of recent regional and ecosystem focused vegetation-mapping efforts. It includes the best available data for each of the habitat types. As a result, different parts of the map rely on varied methods and data. For detailed methodology please see the enclosed PDF document.

3D Map System Market Outlook

The global 3D map system market size was valued at approximately $4.2 billion in 2023 and is projected to reach around $11.3 billion by 2032, growing at a robust CAGR of 11.5% during the forecast period. The increasing demand for advanced mapping solutions across various sectors such as automotive, urban planning, and infrastructure development is a significant growth factor propelling this market. The adoption of 3D maps, driven by technological advancements and the need for precise spatial data, is transforming how industries manage and utilize geospatial information.

One of the primary growth factors of the 3D map system market is the burgeoning demand within the automotive industry. The rise of autonomous and connected vehicles relies heavily on high-precision 3D mapping systems to ensure safety and efficiency. As vehicles become increasingly sophisticated, the need for accurate terrain and environmental data becomes paramount, driving the integration of these systems into modern automobiles. Additionally, the evolution of smart cities and infrastructure projects around the globe has necessitated the use of 3D maps for planning and management, further fueling market growth.

The aerospace and defense sectors are also major proponents of 3D map systems, utilizing them for navigation, simulation, and mission planning. The accuracy and detailed visualization provided by these maps are indispensable in military applications, where precise terrain understanding can critically impact operations and strategy development. Furthermore, the expansion of drone technology has increased the demand for 3D mapping solutions, as these aerial vehicles increasingly rely on detailed geospatial data to perform a variety of tasks ranging from surveillance to environmental monitoring.

In urban planning, the use of 3D mapping systems has gained significant traction due to their ability to provide a comprehensive view of urban landscapes, aiding in efficient planning and decision-making. These systems enable planners to visualize and simulate different developmental scenarios, assessing their impact on the environment and city infrastructure. Such capabilities are invaluable in developing sustainable urban areas that can accommodate growing populations while minimizing ecological footprints. Moreover, as environmental concerns and regulatory pressures increase, the use of 3D maps is becoming more prevalent in infrastructure planning and development.

Regionally, North America dominates the 3D map system market, driven by technological innovation and high adoption rates across various industries. The presence of key market players and substantial investment in research and development further bolster the region's dominance. Meanwhile, the Asia Pacific is experiencing the fastest growth, attributed to rapid urbanization and infrastructure development, particularly in countries like China and India. The implementation of smart city initiatives and the expansion of automotive and defense sectors are significant factors contributing to the region's market expansion.

Component Analysis

The component segment of the 3D map system market is subdivided into software, hardware, and services, each playing a pivotal role in the overall functionality and utilization of 3D mapping technologies. Software components are at the core of the 3D map system market, offering essential functionalities for creating, editing, and managing 3D spatial data. The demand for sophisticated software solutions is rising as users seek advanced features such as real-time data processing, analytics, and augmented reality integration. These software solutions enable various applications, from navigation and simulation to geospatial data analysis, making them indispensable across multiple industries.

Hardware components include the physical devices and infrastructure required to capture, store, and process 3D mapping data. This includes GPS devices, LiDAR systems, and high-resolution cameras, which are critical for accurate data acquisition. The hardware segment is experiencing growth due to technological advances that enhance data capture accuracy and efficiency. The integration of artificial intelligence and machine learning with hardware components further improves the capability of 3D mapping systems, enabling automated data processing and real-time applications.

The services component encompasses the various support and maintenance services essential for the optimal functioning of 3D map systems. These services include system integration,

This web map depicts GIS data for known Stormwater Infrastructure in the City of SeaTac, Washington. The information is based on the best available knowledge collected from construction as-builts and field inspections, with a focus on mapping features in the public right-of-way. The stormwater infrastructure contains the following datasets: discharge points, catch basins and manholes, pipes and ditches, misc structures, water quality facilities points and polygons, and access risers. The data is being continually updated as newer information becomes available.Incorporated in February 1990, the City of SeaTac is located in the Pacific Northwest, approximately midway between the cities of Seattle and Tacoma in the State of Washington. SeaTac is a vibrant community, economically strong, environmentally sensitive, and people-oriented. The City boundaries surround the Seattle-Tacoma International Airport, (approximately 3 square miles in area) which is owned and operated by the Port of Seattle. For additional information regarding the City of SeaTac, its people, or services, please visit https://www.seatacwa.gov. For additional information regarding City GIS data or maps, please visit https://www.seatacwa.gov/our-city/maps-and-gis.

This web map is for the main City Base Map application for general city use.