1.0 DCPLUS 20250927 02570600 20250927 02570600 https://www.arcgis.com/home/item.html?id=cb95df49a32c4fa3a8dede8bbe445592 cb95df49a32c4fa3a8dede8bbe445592 GeoJson Building Footprints (from DataSF, pulled weekly) 2025-09-27T02:57:06 2025-09-27T02:57:08 san franciscobuildingsbuilding footprints These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years.The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible.These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall. These footprint extents are collapsed from an earlier 3D building model provided by Pictometry of 2010, and have been refined from a version of building masses publicly available on the open data portal for over two years.The building masses were manually split with reference to parcel lines, but using vertices from the building mass wherever possible.These split footprints correspond closely to individual structures even where there are common walls; the goal of the splitting process was to divide the building mass wherever there was likely to be a firewall.<br /><br /> An arbitrary identifier was assigned based on a descending sort of building area for 177,023 footprints. The centroid of each footprint was used to join a property identifier from a draft of the San Francisco Enterprise GIS Program's cartographic base, which provides continuous coverage with distinct right-of-way areas as well as selected nearby parcels from adjacent counties. See accompanying document SF_BldgFoot_2017-05_description.pdf for more on methodology and motivation<br /><br />Data pushed to ArcGIS Online on May 31, 2026 at 4:29 AM by SFGIS.<br />Data from: <a href='https://data.sfgov.org/d/ynuv-fyni' rel='nofollow ugc'>https://data.sfgov.org/d/ynuv-fyni</a><br /><br />Description of dataset columns: <table border='1'> <tbody> <tr> <td>sf16_bldgid</td> <td>San Francisco Building ID using criteria of 2016-09, 6-char epoch, '.' , 7-char zero-padded AreaID or new ID in editing epochs after initial '201006.'</td> </tr> <tr> <td>area_id</td> <td>Epoch 2010.06 Shape_Area sort of 177,023 building polygons with area &gt; ~1 sq m</td> </tr> <tr> <td>mblr</td> <td>San Francisco property key: Assessor's Map-Block-Lot of land parcel, plus Right-of-way area identifier derived from street Centerline Node Network (CNN)</td> </tr> <tr> <td>p2010_name</td> <td>Pictometry 2010 building name, if any</td> </tr> <tr> <td>p2010_zminn88ft</td> <td>Input building mass (of 2010,) minimum Z vertex elevation, NAVD 1988 ft</td> </tr> <tr> <td>p2010_zmaxn88ft</td> <td>Input building mass (of 2010,) maximum Z vertex elevation, NAVD 1988 ft</td> </tr> <tr> <td>gnd_cells50cm</td> <td>zonal statistic: LiDAR-derived ground surface grid, population of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_mincm</td> <td>zonal statistic: LiDAR-derived ground surface grid, minimum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_maxcm</td> <td>zonal statistic: LiDAR-derived ground surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_rangecm</td> <td>zonal statistic: LiDAR-derived ground surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_meancm</td> <td>zonal statistic: LiDAR-derived ground surface grid, mean value of 50cm square cells sampled in this building's zone, from integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_stdcm</td> <td>zonal statistic: LiDAR-derived ground surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters</td> </tr> <tr> <td>gnd_varietycm</td> <td>zonal statistic: LiDAR-derived ground surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_majoritycm</td> <td>zonal statistic: LiDAR-derived ground surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_minoritycm</td> <td>zonal statistic: LiDAR-derived ground surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>gnd_mediancm</td> <td>zonal statistic: LiDAR-derived ground surface grid, median value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>cells50cm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, population of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>mincm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, minimum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>maxcm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>rangecm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, maximum value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>meancm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, mean value of 50cm square cells sampled in this building's zone, from integer NAVD 1988 centimeters</td> </tr> <tr> <td>stdcm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters</td> </tr> <tr> <td>varietycm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>majoritycm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>minoritycm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>mediancm_1st</td> <td>zonal statistic: LiDAR-derived first return surface grid, median value of 50cm square cells sampled in this building's zone, integer NAVD 1988 centimeters</td> </tr> <tr> <td>hgt_cells50cm</td> <td>zonal statistic: LiDAR-derived height surface grid, population of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_mincm</td> <td>zonal statistic: LiDAR-derived height surface grid, minimum value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_maxcm</td> <td>zonal statistic: LiDAR-derived height surface grid, maximum value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_rangecm</td> <td>zonal statistic: LiDAR-derived height surface grid, maximum value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_meancm</td> <td>zonal statistic: LiDAR-derived height surface grid, mean value of 50cm square cells sampled in this building's zone, from integer centimeters</td> </tr> <tr> <td>hgt_stdcm</td> <td>zonal statistic: LiDAR-derived height surface grid, 1 standard deviation of 50cm square cells sampled in this building's zone, centimeters</td> </tr> <tr> <td>hgt_varietycm</td> <td>zonal statistic: LiDAR-derived height surface grid, count of unique values of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_majoritycm</td> <td>zonal statistic: LiDAR-derived height surface grid, most frequently occuring value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_minoritycm</td> <td>zonal statistic: LiDAR-derived height surface grid, least frequently occuring value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>hgt_mediancm</td> <td>zonal statistic: LiDAR-derived height surface grid, median value of 50cm square cells sampled in this building's zone, integer centimeters</td> </tr> <tr> <td>gnd_min_m</td> <td>summary statistic: zonal minimum ground surface height, NAVD 1988 meters</td> </tr> <tr> <td>median_1st_m</td> <td>summary statistic: zonal median first return surface height, NAVD 1988 meters</td> </tr> <tr> <td>hgt_median_m</td> <td>summary statistic: zonal median height surface value, meters</td> </tr> <tr> <td>gnd1st_delta</td> <td>summary statistic: discrete difference of (median first return surface -- minimum bare earth surface) for the building's zone, meters</td> </tr> <tr> <td>peak_1st_m</td> <td>summary statistic: highest cell value of first return surface in the building's zone, NAVD 1988 meters</td> </tr> <tr> <td>globalid</td> <td>Global Identifier</td> </tr> <tr> <td>shape</td> <td>Multi-Polygon geography</td> </tr> <tr> <td>data_as_of</td> <td>Timestamp the data was updated in the source system</td> </tr> <tr> <td>data_loaded_at</td> <td>Timestamp the data was loaded to the open data portal</td> </tr> </tbody> </table> Note: If no description was provided by DataSF, the cell is left blank. See <a href='https://data.sfgov.org/d/ynuv-fyni' rel='nofollow ugc'>the source data</a> for more information. Open Data Commons Public Domain Dedication and License: <a href='http://opendatacommons.org/licenses/pddl/1.0' rel='nofollow ugc'>http://opendatacommons.org/licenses/pddl/1.0/</a> cb95df49a32c4fa3a8dede8bbe445592 GeoJson 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