Buildings and infrastructure data

Assets used to protect the coast from erosion or provide access. Asset Sub-Types include: Breakwater, Groyne, Ramp, Slipway, Steps See the Data Requirements Library for more detail: https://environment.data.gov.uk/asset-management/drl-app/asset-types PLEASE NOTE: This data is updated daily. Depending on the chosen download format, it may take 2-3 minutes to download the full national dataset. Data is generated from the Asset Information Management System which holds records of assets associated with flood defences. Click Here to go straight to the DSP Metadata Page for this Dataset.

City-owned and leased facilities maintained in the City's Facility System of Record (FSR). Does not include the following agencies which are not part of the City and County of San Francisco: SFHA, OCII and City College. This dataset was developed through a process involving some of the primary stewards for the facility data and their stakeholders. It is meant to be a living reference and will be maintained and updated through established workflows and processes over time. You can access the attached User Guide for more detail by clicking the red About button and scrolling to attachments. Data pushed to ArcGIS Online on October 19, 2026 at 3:15 AM by SFGIS. Data from: https://data.sfgov.org/d/nc68-ngbr Description of dataset columns: facility_id System generated unique facility identifier common_name Commonly known/used facility name address Address or location description of facility city City where facility is located. zip_code ZIP Code where facility is located. block_lot The block and lot number, also known as the Assessor Parcel Number or APN. For facilities with multiple APNs associated, the lowest numbered APN is used. This will blank if in the facility is the right-of-way or outside of San Francisco county. owned_leased Indicates if the facility is owned or leased by the City. dept_id_for_jurisdiction Department ID of the City department with jurisdiction over the facility. jurisdiction Jurisdiction: department name of the City department with jurisdiction over the facility. gross_sq_ft Gross square footage of facility (original source: FRRM). Note: Incomplete data, updating as values are determined. longitude The longitude of the facility's location. latitude The latitude of the facility's location. supervisor_district Board of Supervisors District in which the facility is located, if applicable. Note: there are city facilities that are outside the boundaries of the City and County of San Francisco. city_tenants City departments that are tenants of the facility, separated by a semi-colon. land_id Foreign key to City Lands table. geom The geometry/location column which allows the facility to be mapped in Socrata. data_last_updated Timestamp when the record (row) was last updated in the source system data_as_of Timestamp when the record (row) was last refreshed in the source system data_loaded_at Timestamp when the record (row) was was last updated here (in the data portal) Analysis Neighborhoods DELETE - Neighborhoods_from_fyvs_ahh9 DELETE - Supervisor Districts DELETE - Current Police Districts DELETE - Fire Prevention Districts DELETE - Zip Codes Neighborhoods SF Find Neighborhoods Current Police Districts Current Supervisor Districts Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

Tararua Wastewater Pumps sourced from the RAMM Asset Management System. Data maintained by the Tararua Alliance.

This layer is a subset of ITSPE and and UBIDs representing vacant and blighted property building footprints. The tax assessment roll public extract (ITSPE) is used for assessment and property analysis, to send property tax bills and notices, and stores comprehensive tax information such as ownership, mailing addresses, non-contiguous Air Rights lots (Multifamily or Development), Air Rights lots, possessory interest lots, record lots, tax lots, parcels, condominiums, and federally owned lands such as reservations and appropriations. The linkage from the Vector Property layers to this database is SSL (Square, Suffix, and Lot). The UBID data was originally created by spatially joining the 2019 building footprints published in DC Open Data with the Common Ownership Lots. The UBIDs were coded using US DOE’s Implementation code. Search for UBID and ITSPE in Open Data DC for individual documentation.

Permits are how the City regulates construction. There are several different types of permits, depending on the type of construction. This table includes all permits issued through the Development Services permit center: building, mechanical plumbing, garage sales and others. Data is updated hourly during business hours. For more about permitting, please visit the Development Services website.

Fields include sites, projects, project types, notes, addresses, and geographic coordinates. This dataset will display bond projects by Charlotte-Mecklenburg schools. There are a total of 30 Bond Projects listed across the 35 Project Sites.

Point locations of park furniture and fixtures such as rubbish bins and bike stands, with attributes for asset group, site and site description, local board, street name and number, and city. Published by OpenDataUser.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized

The location of water tanks for public water systems in the Commonwealth of Kentucky. A water tank is a feature that is used for water storage in a distribution system or at a water treatment plant.

Census Current (2026) Physical Features Web Map Service; January 1, 2026 vintage

Date of Image(s): January 9, 2025, January 13, 2025, January 21, 2025 Summary: This raster dataset (TIF) represents likely damaged/destroyed urban areas in Los Angeles, California, during the fires of January 2025. The map is the result of a coherent change detection analysis of interferometric Sentinel-1 radar (InSAR) images from January 2025 in comparison with pre-disaster reference images from 2022-2025. Coherent change detection analysis is used to show change across the entire domain, which includes vegetation and forested areas. This analysis is constrained to a snapshot of the FIRIS perimeter domain to highlight the damage to built up areas only. Potentially damaged areas denoted in orange represent data collected on January 9, 2025 from a Sentinel-1 pass over the Altadena/Eaton area. Potentially damaged areas denoted in red represent data collected on January 13, 2025 from a Sentinel-1 pass over the Palisades area. Potentially damaged structures denoted in red represent data collected on January 21, 2025. Date(s) and time(s) covered: Swaths from 9 January 2025, 14 January 2025, and January 21, 2025, with 188 images collected from 2022-12-20 to 2025-01-02 for a pre-disaster reference dataset. Suggested Usage: The map is meant for landscape-level geospatial overlay and visualization purposes of fire damage in urban/built-up regions. For example, the map is useful for tabulating estimates of the number of likely damaged or destroyed structures. Pixel values of 1 represent a region likely damaged or destroyed; pixel values of 0 represent all other regions. Satellite/Sensor: European Space Agency Copernicus Sentinel-1/10 meter spatial resolution processed to 40 meters for interferometric coherence change analysis Resolution: 40 meters Credits: Damage analysis of Copernicus Sentinel-1 satellite data by Corey Scher of CUNY Graduate Center and Jamon Van Den Hoek of Oregon State University. Contact: Jamon Van Den Hoek (jamon.vandenhoek@oregonstate.edu). Data sources: Sentinel-1 https://asf.alaska.edu/datasets/daac/sentinel-1/ Alaska Satellite Facility https://hyp3-docs.asf.alaska.edu/usage_guidelines/ CA Perimeters NIFC FIRIS data (gis.data.cnra.ca.gov/datasets/CALFIRE-Forestry::ca-perimeters-nifc-firis-public-view/) Access and Use Constraints: Likely Damaged Areas: The damage map has not been field-validated and omission/commission errors are likely present. Regions that changed over the course of the reference period or were otherwise dynamic (such as places of construction or destruction or vegetated areas) are likely excluded from the analysis. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=f8faeec910c74ef8a32f95838ddf0bdc WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel1_dmg/ImageServer/WMSServer

Following the June 2026 earthquakes that struck the central coast of Venezuela, the Microsoft AI for Good Lab ran their building damage assessment AI models to map and classify the buildings affected by the earthquakes. The models were applied to satellite imagery captured on 25 and 26 June 2026, producing building footprints with predicted damage across the affected areas of Catia La Mar, La Guaira, and Caraballeda. For each assessed area, the collection provides a reference area map, a valid-area mask describing the extent of the analysis, and a GeoPackage of predicted building damage footprints. This collection mirrors the dataset published on the Humanitarian Data Exchange (HDX) by the Microsoft AI for Good Lab and made available under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license. ## Methodology The Microsoft AI for Good Lab ran their damage assessment AI models on satellite images taken 25 and 26 June 2026 and have mapped out the affected buildings. ## Disclaimer This analysis represents a portion of the damage from the earthquake; other damage in the region is not included in this assessment. While these insights offer a valuable initial overview, they remain preliminary and require on-the-ground verification for a comprehensive understanding of the damage. ## Caveats Please note that the source imagery has orthorectification quality issues, which may cause building footprints to be misaligned with existing basemaps.

Creamos evaluaciones de daños a nivel de edificio tras el terremoto en Colombia mediante el entrenamiento y la posterior ejecución de un modelo de inteligencia artificial sobre imágenes satelitales adquiridas después del desastre. El modelo de IA clasifica cada edificio identificado en las imágenes como "sin daños", "afectado" o "desconocido". La categoría "desconocido" se utiliza cuando el edificio no puede ser evaluado adecuadamente, por ejemplo, debido a la presencia de nubes. Utilizamos los polígonos de edificios de Overture Maps, que representan el estado de las edificaciones sobre el terreno antes del evento. Los resultados se distribuyen como un archivo vectorial en formato GeoPackage, con los siguientes atributos para cada edificio: - `id` – identificador único de Overture Maps y Google para cada edificio. - `damaged` – valor 1 si el edificio está dañado; de lo contrario, 0. - `unknown` – valor 1 si el edificio está cubierto por nubes, neblina, humo o si, por alguna otra razón, no fue posible clasificarlo; de lo contrario, 0. - `area` – área del edificio en metros cuadrados. We create building level damage assessments by training and then running an AI model on the post-disaster imagery. The AI model predicts whether each footprint in the imagery is "building", "damaged", or "unknown" (i.e. cloudy). We use Overture Maps and Google building footprints which represent the state on the ground pre-event and distribute the resulting data as a vector file GeoPackage with the following per-footprint attributes: - `id` – the Overture Maps unique ID for each footprint. - `damaged` – 1 if the building is damaged, else 0. - `unknown` – 1 if the building was covered by clouds/haze/smoke or otherwise unable to be classified, else 0. - `area` – area of the building in sq meters.

Unified School Districts, Secondary School Districts, Elementary School Districts

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Any schools that receives public funding are considered public, including continuation schools and some charter & online schools.  This layer is symbolized to show the percentage and count of students in kindergarten through 12th grade who are enrolled in a private school. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B14002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).    The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2022 vintage; Generalized

The purpose of this feature class is to illustrate the location of proposed water areas in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts. The locations of proposed water areas, along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning, project development, state and federal funding applications, Kentucky State Clearinghouse, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

The purpose of this feature class is to illustrate the location of water pumps in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts and provided by the Kentucky Division of Water. The locations of water pumps, along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning and management, project development, asset inventories, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.

Line features representing a new bridge, each with a description field and a recorded length. Published by Galway City Council and covering Ireland.

Summary: The product maps urban areas and building footprints in Caracas and La Guaira, Venezuela, that show substantial post-event spectral change. The damage likelihood combines increased visible brightness and increased SWIR1 reflectance, which may be associated with exposed concrete, rubble, dust, debris, roof disturbance, or collapsed structures. Approximately 46,000 buildings were identified as having a high damage likelihood based on Sentinel-2 observations and building-level screening. These estimates are intended for rapid situational awareness and should be validated using higher-resolution imagery or ground observations. For each building footprint, damage-score statistics were calculated using a 20 m scale:Mean damage score  Maximum damage score  75th percentile  90th percentile  Valid Sentinel-2 pixel count Dates of Images and Input Data:Sentinel-2 pre-event composite: 1 January 2026 – 23 June 2026 Sentinel-2 post-event composite: 24 June 2026 – 30 June 2026 Sentinel-2 collection: COPERNICUS/S2_SR_HARMONIZED ESA WorldCover: v200, built-up class 50 Building footprints: Google Open Buildings v3, confidence ≥ 0.75 Summary: This experimental product provides a rapid assessment of approximately 46,000 potentially damaged buildings following the June 2026 Venezuela earthquake using Sentinel-2 imagery and Google Open Buildings footprints. It is designed to support emergency response, situational awareness, and prioritization of areas for further investigation. Suggested Use:Rapid earthquake response and situational awareness Screening of possible building damage hotspots Prioritization of high-resolution optical or SAR image tasking Regional-scale validation against commercial imagery Emergency management planning support Product Description: Approximately 46,000 buildings were identified as having a high damage likelihood based on the experimental Sentinel-2 assessment. The product was generated from pre- and post-event Sentinel-2 median composites using cloud- and shadow-masked surface reflectance data. A damage score combining visible brightness and SWIR1 changes was computed and restricted to urban areas using ESA WorldCover. Google Open Buildings footprints were used to identify structures with elevated damage likelihood following the June 2026 Venezuela earthquake. The product is intended for rapid screening and emergency response applications.Symbology: building_damage_likelihood.tif  Damage Likelihood Color Recommended Label -0.05 Blue/white Low Probability 0.05 Yellow Moderate Probability 0.15 Red High Probability Satellite/Sensor/Resolution:• Satellite: Sentinel-2A / Sentinel-2B• Sensor: MultiSpectral Instrument (MSI)• Processing level: Level-2A Surface Reflectance• Bands used: B2, B3, B4, B11• Visible bands resolution: 10 m• SWIR1 resolution: 20 m• Building-summary scale: 20 m• Temporal Baseline: 5 days Caveats and Limitations:Sentinel-2 cannot reliably confirm individual building damage at 10–20 m resolution. Building polygons are used only to summarize damage-score signals, not to directly observe structural collapse. Spectral changes may be caused by rubble, dust, exposed soil, roof material change, shadow, moisture, cloud contamination, or other non-damage factors. ESA WorldCover urban masking reduces but does not eliminate false positives. Google Open Buildings may contain missing, outdated, or inaccurate footprints. This product should be treated as an experimental damage-likelihood layer, not a definitive building damage inventory. Validation using high-resolution commercial optical imagery, SAR coherence/backscatter products, or ground reports is strongly recommended. File names: high_damage_buildings.kml — building polygons with high possible damage  building_footprints.kml — Buildings footprints intersecting the study area  building_damage_likelihood.tif — Sentinel-2 optical damage likelihood raster S2_Pre_RGB.tif — pre-event Sentinel-2 RGB composite S2_Post_RGB.tif — post-event Sentinel-2 RGB composite Esri REST Endpoint: See Layers below. Credits Khuong Tran (Khuong.tran@nasa.gov)

This dataset contains all permit inspections for Building, Construction, Engineering, and Halifax Water permits. Metadata

The information is extracted from the asset inventory database on a daily basis. Items identified have been geolocated over a long period of time and through various methods, including information provided by 3rd parties. In general, asset locations are obtained from ‘As Built Diagrams’ and as such may not be validated in all circumstances. The asset inventory is frequently updated and modification can be made to the asset data structure (asset hierarchy) without prior notification. Due to a wide range of source information all asset locations should be verified through the Asset Information Officers and or site visits. This is an incomplete dataset, other information is held and maintained independently. The primary purpose of this inventory is for asset valuations. The inventory is utilized in forward works and capital work planning. Information on Waste water assets for service requests is displayed on 3 Waters map. The wastewater network is an integral part of the land use and consents process, however site visits should be done to validate the status, position and condition of assets. Waikato OneView does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Locations and dimensions of assets depicted in the data may not be accurate due to circumstances not notified to Council. While you are free to crop, export and re-purpose the data, we ask that you attribute the Waikato OneView and clearly state that your work is a derivative and not the authoritative data source.

Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows public vs. private school enrollment by sex by grade group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Any schools that receives public funding are considered public, including continuation schools and some charter & online schools.  This layer is symbolized to show the percentage of students in kindergarten through 12th grade who are enrolled in a private school. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left.  Vintage: 2019-2023ACS Table(s): B14002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov  The United States Census Bureau's American Community Survey (ACS):About the Survey   Geography & ACS   Technical Documentation   News & Updates   This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census:  Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data).  The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.   Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).    The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.   The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate.   The data for this geographic area cannot be displayed because the number of sample cases is too small.

This feature layer provides counts of forecasted chargers needed and current gap in charging for census tracts in Tempe. Each record represents a census tract in Tempe and includes details on forecasted charger count, existing charger, and gap in chargers at level 2 and DC Fast charging power levels. This modeled dataset was created by Catalyst Mobility (formerly Calstart). This information is a component of ongoing efforts for EV charging infrastructure development for Tempe. Data Scope  Charger forecasts are to 2050 for level 2 and DC fast charging power levels.   Data Use  Intended to be used in the Tempe EVCI Development Strategy Map.   Data Limitations  Charger count data is forecasted and may not reflect actual future conditions.   Data Dictionary   Additional Information Contact Name: Sustainability Division Contact Email: sustainability@tempe.gov Data Source:  Catalyst Mobility (formerly Calstart) Data Source Type:  GIS Feature Layer  Preparation Method:  See Task 2 of Electric Vehicle Charging Infrastructure (EVCI) Siting Plan Assessment Publish Method:  Manual Publish Frequency:  One-time static

Bing Maps is releasing open building footprints around the world. We have detected over 999 million buildings from Bing Maps imagery between 2014 and 2021 including Maxar and Airbus imagery. The data is freely available for download and use under ODbL. This dataset complements our other releases. For more information, see the GlobalMLBuildingFootprints repository on GitHub. ## Building footprint creation The building extraction is done in two stages: 1. Semantic Segmentation – Recognizing building pixels on an aerial image using deep neural networks (DNNs) 2. Polygonization – Converting building pixel detections into polygons **Stage 1: Semantic Segmentation** !Semantic segmentation **Stage 2: Polygonization** !Polygonization ## Data assets The building footprints are provided as a set of geoparquet datasets in [Delta][delta] table format. The data are partitioned by 1. Region 2. quadkey at [Bing Map Tiles][tiles] level 9 Each `(Region, quadkey)` pair will have one or more geoparquet files, depending on the density of the of the buildings in that area. Note that older items in this dataset are *not* spatially partitioned. We recommend using data with a processing date of 2023-04-25 or newer. This processing date is part of the URL for each parquet file and is captured in the STAC metadata for each item (see below). ## Delta Format The collection-level asset under the `delta` key gives you the fsspec-style URL to the Delta table. This can be used to efficiently query for matching partitions by `Region` and `quadkey`. See the notebook for an example using Python. ## STAC metadata This STAC collection has one STAC item per region. The `msbuildings:region` property can be used to filter items to a specific region, and the `msbuildings:quadkey` property can be used to filter items to a specific quadkey (though you can also search by the `geometry`). Note that older STAC items are not spatially partitioned. We recommend filtering on items with an `msbuildings:processing-date` of `2023-04-25` or newer. See the collection summary for `msbuildings:processing-date` for a list of valid values. [delta]: https://delta.io/ [tiles]: https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2022 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2023 vintage; Generalized

City facilities, public parks, public schools, libraries, parking lots and structures, fire stations, hospitals, and Woodlawn Cemetery.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2020 vintage

The Mowing Operations dataset provides detailed information on scheduled mowing operations across District-managed properties. It includes data fields such as mowing dates, mowing frequency, and specific location identifiers like property names and addresses. The dataset also categorizes mowing areas by agency or property type—such as schools, parks, and government facilities—and includes timestamps showing the last time the information was updated. Developed to promote transparency and community engagement, the dataset allows residents to see when and where mowing services are planned throughout the season.

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized

Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized