Building footprints: Bonaire (Microsoft)

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

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.

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.

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

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten', 'school', 'college', 'university','language_school') OR office='educational_institution' Features may have these attributes: name, amenity, isced:level, capacity Geometry types: point, polygon This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten', 'school', 'college', 'university','language_school') OR office='educational_institution' Features may have these attributes: name, amenity, isced:level, capacity Geometry types: point, polygon This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.