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.

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.

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.

This dataset shows the footprints of all structures within the City of Melbourne. A building footprint is a 2D polygon (or multi-polygon) representation of the base of a building or structure. The footprint is defined as the boundary of the structure where the walls intersect with the ground plane or podium, rather than an outline of the roof area (roofprint).

Where a building has a significant change in built form, multiple footprint polygons are ‘stacked’ vertically to define shape of the built form. This includes, and is not limited to:

• Tower
• Podium
• Setbacks/offsets

The Australian Height Datum (AHD) is the national vertical datum for Australia. The National Mapping Council adopted the AHD in May 1971 as the datum to which all vertical control mapping would be referred

The data was captured in May 2023.

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

Updated building footprint data for Columbia County NY.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 future.Address Point Count: the number of address points that fall within that building footprint.Address 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 GS.Municipality Name: Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GS.Source: 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 tile.Source 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.md

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 2015 byEarth Sciences and Resources Institute (ESRI). This feature class contains features extracted from LiDAR captured by GPI in 2018. 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: Unknown The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

ps-places-metadata-v1.01

SUMMARY

This dataset comprises a pair of layers, (points and polys) which attempt to better locate "populated places" in NZ. Populated places are defined here as settled areas, either urban or rural where densitys of around 20 persons per hectare exist, and something is able to be seen from the air.

RATIONALE

The only liberally licensed placename dataset is currently LINZ geographic placenames, which has the following drawbacks: - coordinates are not place centers but left most label on 260 series map - the attributes are outdated

METHODOLOGY

This dataset necessarily involves cleaving the linz placenames set into two, those places that are poplulated, and those unpopulated. Work was carried out in four steps. First placenames were shortlisted according to the following criterion: - all places that rated at least POPL in the linz geographic places layer, ie POPL, METR or TOWN or USAT were adopted. - Then many additional points were added from a statnz meshblock density analysis.
- Finally remaining points were added from a check against linz residential polys, and zenbu poi clusters.

Spelling is broadly as per linz placenames, but there are differences for no particular reason. Instances of LINZ all upper case have been converted to sentance case. Some places not presently in the linz dataset are included in this set, usually new places, or those otherwise unnamed. They appear with no linz id, and are not authoritative, in some cases just wild guesses.

Density was derived from the 06 meshblock boundarys (level 2, geometry fixed), multipart conversion, merging in 06 usually resident MB population then using the formula pop/area*10000. An initial urban/rural threshold level of 0.6 persons per hectare was used.

Step two was to trace the approx extent of each populated place. The main purpose of this step was to determine the relative area of each place, and to create an intersection with meshblocks for population. Step 3 involved determining the political center of each place, broadly defined as the commercial center.

Tracing was carried out at 1:9000 for small places, and 1:18000 for large places using either bing or google satellite views. No attempt was made to relate to actual town 'boundarys'. For example large parks or raceways on the urban fringe were not generally included. Outlying industrial areas were included somewhat erratically depending on their connection to urban areas.

Step 3 involved determining the centers of each place. Points were overlaid over the following layers by way of a base reference:

a. original linz placenames b. OSM nz-locations points layer c. zenbu pois, latest set as of 5/4/11 d. zenbu AllSuburbsRegions dataset (a heavily hand modified) LINZ BDE extract derived dataset courtesy Zenbu. e. LINZ road-centerlines, sealed and highway f. LINZ residential areas, g. LINZ building-locations and building footprints h. Olivier and Co nz-urban-north and south

Therefore in practice, sources c and e, form the effective basis of the point coordinates in this dataset. Be aware that e, f and g are referenced to the LINZ topo data, while c and d are likely referenced to whatever roading dataset google possesses. As such minor discrepencys may occur when moving from one to the other.

Regardless of the above, this place centers dataset was created using the following criteria, in order of priority:

• attempts to represent the present (2011) subjective 'center' of each place as defined by its commercial/retail center ie. mainstreets where they exist, any kind of central retail cluster, even a single shop in very small places.
• the coordinate is almost always at the junction of two or more roads.
• most of the time the coordinate is at or near the centroid of the poi cluster
• failing any significant retail presence, the coordinate tends to be placed near the main road junction to the community.
• when the above criteria fail to yield a definitive answer, the final criteria involves the centroids of: . the urban polygons . the clusters of building footprints/locations.

To be clear the coordinates are manually produced by eye without any kind of computation. As such the points are placed approximately perhaps plus or minus 10m, but given that the roads layers are not that flash, no attempt was made to actually snap the coordinates to the road junctions themselves.

The final step involved merging in population from SNZ meshblocks (merge+sum by location) of popl polys). Be aware that due to the inconsistent way that meshblocks are defined this will result in inaccurate populations, particular small places will collect population from their surrounding area. In any case the population will generally always overestimate by including meshblocks that just nicked the place poly. Also there are a couple of dozen cases of overlapping meshblocks between two place polys and these will double count. Which i have so far made no attempt to fix.

Merged in also tla and regions from SNZ shapes, a few of the original linz atrributes, and lastly grading the size of urban areas according to SNZ 'urban areas" criteria. Ie: class codes:

1. Not used.
2. main urban area 30K+
3. secondary urban area 10k-30K
4. minor urban area 1k-10k
5. rural center 300-1K
6. village -300

Note that while this terminology is shared with SNZ the actual places differ owing to different decisions being made about where one area ends an another starts, and what constiutes a suburb or satellite. I expect some discussion around this issue. For example i have included tinwald and washdyke as part of ashburton and timaru, but not richmond or waikawa as part of nelson and picton. Im open to discussion on these.

No attempt has or will likely ever be made to locate the entire LOC and SBRB data subsets. We will just have to wait for NZFS to release what is thought to be an authoritative set.

PROJECTION

Shapefiles are all nztm. Orig data from SNZ and LINZ was all sourced in nztm, via koordinates, or SNZ. Satellite tracings were in spherical mercator/wgs84 and converted to nztm by Qgis. Zenbu POIS were also similarly converted.

ATTRIBUTES

Shapefile: Points id : integer unique to dataset name : name of popl place, string class : urban area size as above. integer tcode : SNZ tla code, integer rcode : SNZ region code, 1-16, integer area : area of poly place features, integer in square meters. pop : 2006 usually resident popluation, being the sum of meshblocks that intersect the place poly features. Integer lid : linz geog places id desc_code : linz geog places place type code

Shapefile: Polygons gid : integer unique to dataset, shared by points and polys name : name of popl place, string, where spelling conflicts occur points wins area : place poly area, m2 Integer

LICENSE

Clarification about the minorly derived nature of LINZ and google data needs to be sought. But pending these copyright complications, the actual points data is essentially an original work, released as public domain. I retain no copyright, nor any responsibility for data accuracy, either as is, or regardless of any changes that are subsequently made to it.

Peter Scott 16/6/2011

v1.01 minor spelling and grammar edits 17/6/11

Great Smoky Mountains National Park strives to Balance Cultural and Natural Values on Federal Lands, which encourages Federal land managers to recognize that cultural and natural values should be considered in an integrated manner to ensure that cultural values are afforded equal consideration. The List of Classified Structures (LCS) is an evaluated inventory of all historic and prehistoric structures that have historical,architectural, and/or engineering significance within parks of the National Park System in which the National Park Service has, or plans to acquire, any legally enforceable interest. The list is evaluated or classified by the National Register of Historic Places criteria. Structures are constructed works that serve some form of human activity and are generally immovable. They include buildings and monuments, dams, millraces and canals,nautical vessels, bridges,tunnels and roads,railroad locomotives,rolling stock and track, stockades and fences,defensive works,temple mounds and kivas, ruins of all structural types that still have integrity as structures, and outdoor sculpture. This map service contains only: Structures are a functional construction made for purposes other than creating shelter, such as a bridge. These resources would include features such as: fortifications, earthworks, roads, fences, canals, dams, engineering features, barns, outbuildings, arsenals, ships, manufacturing facilities, etc. These resources represent sites that do not function primarily as dwellings, however they may serve temporarily to house humans, although their primarily purpose is not a permanent shelter. The point may represent the location of a culvert, while a line may represent a fence or road, and a polygon may represent the circumscribed boundary of a manufacturing plant. Historic buildings are a resource created principally to shelter any form of human activity, such as a house. These resources would include features such as: farmhouses, homesites, mansions, churches, museums (if the building is historic), courthouses, offices, prisons, train depots, etc.Historic buildings most often function primarily as dwellings. The point may represent the center of the building, an entrance, a corner, etc., while the polygon may represent the building footprint. This map service does not currently contain LCS assets that do not fall within the CRGIS definition of a Building or a Structure, however, do note that there may be many other LCS assets in the park that are publicly accessible yet are not classified as a building or a structure and therefore not included in this map service.

Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. For example, quantifying population statistics is fundamental to 67 of the 232 United Nations Sustainable Development Goals, but the World Bank estimates that more than 100 countries currently lack effective Civil Registration systems. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. The dataset will comprise over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization.

The City of Doral boundary Building Footprint 2D from the Miami Dade County.A collection of polygon features for all buildings within the Urban Development Boundary (UDB) and outside the UDB. The planimetric layer for Miami-Dade County was previously updated in 2015 by Earth Sciences and Resources Institute (ESRI). This feature class contains features extracted from LiDAR captured by GPI in 2018.Definition of particular fields in the Buildings Footprint 2D feature class:Source = {L, P} where L = LiDAR, P = MDC PlanimetricBld_type = {S, L} where S = Small Buildings, L = Large BuildingsCredits (Attribution): Miami-Dade County, ESRI

It is an FC (polygon) of buildings located in Miami-Dade County that will use the UBID number for the application of the BE305 program in order to have a unique identifier for each building that participates in the program.UBID numbers was extracted from an existing Feature class BuildingFootprint_UBID, which is 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 Cartographic of America, Inc. (ACA). This feature class contains features extracted from LiDAR captured by ACA in 2015.Definition of particular fields in the Buildings Footprint 2D feature class:Source = {L, P} where L = LiDAR, P = MDC PlanimetricBld_type = {S, L} where S = Small Buildings, L = Large BuildingsUpdated: Weekly The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

Polygon file to map building footprints of buildings on Parks Canada properties. Footprints may be derived by tracing the roof outline (for example from an airphoto) or using more detailed measurements of the ground floor.Fichier de polygones pour cartographier les empreintes de construction desbâtiments sur les propriétés de Parcs Canada. Les empreintes peuvent être obtenues en traçant le contour dutoit (par exemple à partir d'une photo aérienne) ou en utilisant des mesures plus détaillées du rez-de-chaussée.

This polygon representation of built rooftop features is intended for quick mapping and impervious surface documentation. This data is static and may not represent current conditions and will be different than other sources such as basemaps. Each building has an associated elevation value. Omissions have been found throughout dataset, especially in industrial areas. Breaks between streets and driveways are not consistent. Objects coded as Unclassified include swimming pools, tennis courts, playgrounds, running tracks, etc. An Impervious Surface definition from the City of Tacoma:A hard surface area that either prevents or retards the entry of water into the soil mantle as under natural conditions prior to development. A hard surface area which causes water to run off the surface in greater quantities or at an increased rate of flow from the flow present under natural conditions prior to development. Common impervious surfaces include, but are not limited to, roof tops, walkways, patios, driveways, parking lots or storage areas, concrete or asphalt paving, gravel roads, packed earthen materials, and oiled, macadam or other surfaces which similarly impede the natural infiltration of stormwater. Open, uncovered retention/detention facilities shall not be considered as impervious surfaces for purposes of determining whether the thresholds for application of minimum requirements are exceeded. Open, uncovered retention/detention facilities shall be considered impervious surfaces for purposes of runoff modeling.Also available for download as CAD. From Tacoma Fire Service Area and Narrows Airport Planimetrics from July 2005 Ortho Photos.

The Building Footprints were derived from the Gwinnett County building enterprise dataset. These were originally developed by using heads-up digitizing to trace the outlines of buildings from aerial photography. This process was repeated later using 2014 and 2017 imagery. This dataset is current as of April 2020, and contains a series of attributes: including the unique building identifier, building floors and the source of the footprint polygon.

The purpose of this document is to provide an explanation of the Geo-database

that is being designed by NovaLIS Technologies for Gwinnett County Georgia. A

geo-database is an object-oriented geographic database that provides services for

managing geographic data. These services include validation rules, relationships,

and topological associations. A geodatabase contains feature datasets and is

hosted inside of a relational database management system.

Datasets

This selection discusses the different types datasets that exist within the geo

database for Gwinnett County. Generally speaking there are two major types of

datasets with in a geo-database, tabular and spatial.

A spatial data set is a set of spatial layers that share a common geographic

definition. Optionally these layers may participate in some sort of topological

relationship. For the purposes of data modeling a spatial dataset my also be a set

of layers that represent objects of similar logical nature for example a dataset

made up of highways, major roads, and minor roads

A tabular dataset is essentially a table within the geo-database. It has columns and

rows but no explicit spatial records.

Spatial datasets

All spatial datasets in the Gwinnett geodatabase have the following spatial

reference

Projection: Transverse_Mercator

Parameters:

False_Easting: 2296583.333333

False_Northing: 0.000000

Central_Meridian: -84.166667

Scale_Factor: 0.999900

Latitude_Of_Origin: 30.000000

Linear Unit: Foot_US (0.304801)

Geographic Coordinate System:

Name: GCS_North_American_1983

Alias:

Gwinnett County Cadastral Geo-Database Description

rgrob Page 3 5/12/2005

P:\Documentation\GIS\Parcel Migration to SDE\Gwinnett Geodatabase Description June2003 GCEdits.doc

Abbreviation:

Remarks:

Angular Unit: Degree (0.017453292519943295)

Prime Meridian: Greenwich (0.000000000000000000)

Datum: D_North_American_1983

Spheroid: GRS_1980

Semimajor Axis: 6378137.000000000000000000

Semiminor Axis: 6356752.314140356100000000

Inverse Flattening: 298.257222101000020000