Building footprint polygons are updated weekly by ECGIS. They provide a general reference of where buildings in Eaton County are located. These are not survey-grade.

Building structures include parking garages, ruins, monuments, and buildings under construction along with residential, commercial, industrial, apartment, townhouses, duplexes, etc. Buildings equal to or larger than 9.29 square meters (100 square feet) are captured. Buildings are delineated around the roof line showing the building "footprint." Roof breaks and rooflines, such as between individual residences in row houses or separate spaces in office structures, are captured to partition building footprints. This includes capturing all sheds, garages, or other non-addressable buildings over 100 square feet throughout the city. Atriums, courtyards, and other “holes” in buildings created as part of demarcating the building outline are not part of the building capture. This includes construction trailers greater than 100 square feet. Memorials are delineated around a roof line showing the building "footprint."Bleachers are delineated around the base of connected sets of bleachers. Parking Garages are delineated at the perimeter of the parking garage including ramps. Parking garages sharing a common boundary with linear features must have the common segment captured once. A parking garage is only attributed as such if there is rooftop parking. Not all rooftop parking is a parking garage, however. There are structures that only have rooftop parking but serve as a business. Those are captured as buildings. Fountains are delineated around the base of fountain structures.

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values a ...

Download In State Plane Projection Here. The pavement boundaries were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from photography taken between March 15 and April 25, 2018. This dataset should meet National Map Accuracy Standards for a 1:1200 product. Lake County staff reviewed this dataset to ensure completeness and correct classification. In the case of a divided highway, the pavement on each side is captured separately. Island features in cul-de-sacs and in roads are included as a separate polygon.These building outlines were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from successive years of photography. The most recent aerial photography was flown between March 11 and April 12, 2017. This dataset should meet National Map Accuracy Standards for a 1:1200 product. All the enclosed structures in Lake County with an area larger than 100 square feet as of April 2014 should be represented in this coverage. It should also be noted that a single polygon in this dataset could be composed of many structures that share walls or are otherwise touching. For example, a shopping mall may be captured as one polygon. Note that the roof area boundary is often not identical to the building footprint at ground level. Contributors to this dataset include: Municipal GIS Partners, Inc., Village of Gurnee, Village of Vernon Hills.

Area layers of US, Australia, and Canada building footprints for use with GIS mapping software, databases, and web applications.

Data in this layer is compiled from a variety of sources. Attributes have been added to distinguish the sources."LeePA Building Footprints" are created and maintained by the Lee County Property Appraiser's GIS. The geometry and attributes are extracted from their databases and combined based on the unique building key."LeePA Condo Buildings" are created from features in the Lee County Property Appraiser's parcel fabric. The geometry and attributes are extracted from their databases and combined using a variety of methods.Other buildings have been added by Lee County GIS. These are typically mobile/manufactured homes or time shares. Most mobile/manufactured homes were created using Esri's Building Footprint Extraction deep learning package and Regularize Building Footprint geoprocessing tool from 2024 aerial imagery. Additional attributes were added by Lee County GIS.

All buildings over 64 square feet in City of Los Angeles captured through LARIAC4 4" and 1' imagery. LARIAC4 guide: https://lariac-lacounty.hub.arcgis.com/pages/lariac4-documents-dataCountywide Building Outlines download available from LA County at: https://data.lacounty.gov/maps/57f5fc977d6a427a978003a6229ab5e7/aboutData is from 2014.

Footprint outlines of buildings in New York City. Please see the following link for additional documentation: https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md P Layers are the centroid layers for the Building and Building Historic layers. They contain the same data as those layers but are represented as points instead of polygons. For additional resources, please refer to https://nycmaps-nyc.hub.arcgis.com/search?tags=building&type=feature%2520service%2Cfeature%2520layer

An Esri File Geodatabase containing 2023 footprints for buildings in Cuyahoga County, Ohio.The features were created using orthophotography captured during the spring of 2023. It includes all identified structures with a footprint of at least 100 square feet.Please note that buildings in dense areas (such as Downtown Cleveland) may be combined with neighboring buildings to form one footprint.A hosted feature service containing this data is also available.

Shapefile of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md

For additional resources, please refer to https://nycmaps-nyc.hub.arcgis.com/search?tags=building&type=feature%2520service%2Cfeature%2520layer

To outline the locations of buildings on Parks Canada sites, buildings that Parks Canada manages, and other buildings of interest to Parks Canada. Polygon file to map building footprints of buildings on Parks Canada sites. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor. Data is not necessarily complete - updates will occur weekly.

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

The purpose of this dataset is to show the building shape and building locations within the state of Oregon. The building footprints contain attributes to document source information and for ease of updates. https://ftp.gis.oregon.gov/framework/Preparedness/SBFO_v1.zip

This feature class GIS dataset contains building footprints depicting building shape and location in the state of Oregon. All contributing datasets were compiled into the stateside dataset. Static datasets or infrequently maintained datasets were reviewed for quality. New building footprint data were reviewed and digitized from the Oregon Statewide Imagery Program 2017 and 2018.

Building footprints (roof corners) were originally imported from a statewide dataset made around 2010. Major updates were made by ToFV GIS in 2016 and again in Feb. 2018 to add newer developments and remove demolished buildings. Updates will continue to be performed regularly as developments occur and data is available. The buildings are generally not highly accurate and should NOT be used as a basis for surveying or any other measurement. These polygons are meant to be used for visual reference and cartographic purposes.

Polygon file depicting building footprints for all buildings over 100 square feet (includes non-addressed building such as outbuildings) in Indianapolis and Marion County, Indiana along with the associated address attribute information. Data projection: NAD 1983 StatePlane Indiana East FIPS 1301 (US Feet)

A collection of polygon features for all buildings within the Urban Development Boundary (UDB) and outside the UDB, approximately 938 square miles. The planimetric layer for Miami-Dade County was previously updated in 2012 by Aerial Cartographics of America, Inc. (ACA). This feature class contains features extracted from LiDAR captured by ACA in 2015.

On June 2019, BuildingFootprint2D was dissolved on Unique_ID to acquire one polygon per unique_id and resolved overlaps, slivers and duplicated polygon errors to create BuildingFootprintUBID. This layer was created for Building Resilency project that needed to identify abuilding footprint by its Building Unique ID (UBID).

Please contact the GIS Technical Support Team at gis@miamidade.gov for additional information.

Definition of particular fields in the Buildings Footprint 2D feature class:

Source = {L, P} where L = LiDAR, P = MDC Planimetric

Bld_type = {S, L} where S = Small Buildings, L = Large BuildingsUpdated: As Needed The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

This data set is a collection of polygons representing the roof line of built structures wholly or partially within the State of North Carolina political boundary. The building footprints are closed polygons with a unique identifier and have the square footage calculated. The polygons were not required to be rectilinear (i.e. interior angles = 90 degrees), but they should give an accurate representation of the building when viewed at a scale of 1:1500 in ArcGIS.

These data were derived by the North Carolina Floodplain Mapping Program (fris.nc.gov) as part of its effort to modernize FEMA Flood Insurance Rate Maps (FIRM) statewide. Previous structure specific geospatial data (where it existed) was typically shown spatially as a point at the center of a structure or parcel boundary. With a building centroid (or center) as a location, much of a building may be within a vulnerable zone of a hazard yet not be included in an evaluation. Good data is extremely important to the hazard assessment. This need for accuracy enhances the need for building footprints to evaluate the hazard. The Statewide Building Footprint Layer was developed to meet that need. The North Carolina Floodplain Mapping Program was established in response to the extensive damage caused by Hurricane Floyd in 1999

This dataset contains photogrammetrically compiled roof outlines of buildings. All near orthogonal corners are square. Buildings that are less than 400 square feet are not captured. Special consideration is given to garages that are less than 400 square feet and will be digitized when greater than 200 square feet. Interim rooflines, such as dormers and party walls, as well as minor structures, such as carports, decks, patios, stairs, etc., and impermanent structures, such as sheds, are not shown. Large buildings which appear to house activities that are commercial or industrial in nature are shown as commercial/industrial. Structures that appear to be primarily residential in nature, including hotels and apartment buildings are shown as residential buildings. Structures which appear to be used or owned primarily by governmental, nonprofit, religious, or charitable organizations, or which serve a public function are shown as public buildings. Structures which are closely associated with a larger building, such as a garage, are shown as an out building. Structures which cannot be clearly defined as Industrial/Commercial; Residential; Public; or Out Buildings are flagged as such for later categorization. The classification of buildings is subject to the interpretation from the aerial photography and may not reflect the building’s actual use. Buildings that have an area less than the minimum required size for data capture will occasionally be present in the Geodatabase. Buildings are not removed after they have been digitized and determined to be less than the minimum required size.

Development Notes: Data meets or exceeds map accuracy standards in effect during the spring of 1992 and updated as a result of a flyover in the spring of 2004 and 2015. Original data was derived from aerial photography flown in the spring of 1992 for the eastern half of the County and the spring of 1993 for the western half of the County. Photography was produced at a scale of 1"=1500'. Mapping was stereo digitized at a scale of 1"=200'.

GRANT OF LICENSE. Subject to the Distributor’s compliance with the End User License Agreement in Schedule B, and the Agreement, Ecopia grants Distributor a non-exclusive, non- transferrable license to distribute the Product to End User. Distributor will distribute the Products to the End User under the following terms:(a) Type. Internal Use License(b) Term. Perpetual(c) End Users. The “End User” for the purposes of the End User License Agreement ofSchedule B shall be interpreted as: Local, and State government organizations of the State Alaska, and Federal government organizationsEcopia will maintain ownership and all associated right, title, and interest in the Products and of all technology used for the generation of the Products. Conditional upon End User’s compliance with these License Terms and the applicable Single Distribution Agreement, during the Term, Ecopia grants to End User a non-exclusive, non- transferable, limited license, to allow an unlimited number of its Authorized Users to:(a) store, access, evaluate, reproduce, and use the Product solely for End User’s Internal Use;(b) create Derivatives of the Product, and store, evaluate, reproduce, and use those Derivatives, all solely for End User’s Internal Use;(c) display Derivatives on a public-facing platform, in a view only, non-downloadable format; and(d) submit point-based challenges, in example, the address may be included, but not the building footprint coordinates.Customer is responsible for ensuring that its Authorized Users comply with these License Terms, and Customer is liable for the acts and omissions of its Authorized Users.DERIVATIVES. A derivative of the Product is any addition, improvement, update, modification, transformation, adaptation, or derivative work of or to the Product, including, for example, any addition or extraction of data or content to or from the Product. Distributor may create a derivative of the Product and provide such derivative solely to the End User. Distributor shall not and shall not permit any third party, except the End User, to access or use the Product or any derivatives. For the sake of clarity, Distributor is expressly forbidden from using the Product, or any derivative derived from the Product, for the purpose of supporting multiple end users.