Google Open Buildings V3 footprint of the country of Chad. This dataset is released to support humanitarian efforts in Chad. For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq The file contains centroids, building footprints (as WKT), and Plus codes.

Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses.

For each building in this dataset we include the polygon describing its footprint on the ground, a confidence score indicating how sure we are that this is a building, and a Plus Code corresponding to the centre of the building. There is no information about the type of building, its street address, or any details other than its geometry.

More information at Google Open Buildings

A dataset of building footprints in Morocco, in the area of the 8 September earthquake. Footprint as of May 2023.

Building footprints are useful for a range of important applications, from population estimation, urban planning and humanitarian response, to environmental and climate science. This large-scale open dataset contains the outlines of buildings derived from high-resolution satellite imagery in order to support these types of uses.

For more info visit open buildings FAQ https://sites.research.google/open-buildings/#faq

מערך הנתונים הפתוח הזה בקנה מידה גדול מורכב מתווים של מבנים שמבוססים על צילומי לוויין ברזולוציה גבוהה של 50 ס"מ. הוא מכיל 1.8 מיליארד זיהויים של בניינים באפריקה, באמריקה הלטינית, באיים הקריביים, בדרום אסיה ובדרום-מזרח אסיה. ההסקה נערכה על פני שטח של 58 מיליון קמ"ר. לכל מבנה במערך הנתונים הזה אנחנו כוללים את הפוליגון שמתאר את שטח המבנה על הקרקע, ציון ביטחון שמציין את מידת הוודאות שלנו שמדובר במבנה וPlus Code שתואם למרכז המבנה. אין מידע על סוג הבניין, הכתובת שלו או פרטים אחרים מלבד הגיאומטריה שלו. שטחי הבניינים שיוצרים צללית הם שימושיים למגוון אפליקציות חשובות: החל ממדדי אוכלוסייה, תכנון עירוני ותגובה הומניטרית ועד למדעי הסביבה והאקלים. הפרויקט מבוסס בגאנה, והוא מתמקד בהתחלה ביבשת אפריקה, עם עדכונים חדשים על דרום אסיה, דרום מזרח אסיה, אמריקה הלטינית והקריביים. ההסקה בוצעה במהלך מאי 2023. פרטים נוספים זמינים באתר הרשמי של מערך הנתונים Open Buildings.

“Automatically Extracted Buildings” is a raw digital product in vector format created by NRCan. It consists of a single topographical feature class that delineates polygonal building footprints automatically extracted from airborne Lidar data, high-resolution optical imagery or other sources.

The Open Database of Buildings (ODB) is a collection of open data on buildings, primarily building footprints, and is made available under the Open Government License - Canada. The ODB brings together 65 datasets originating from various government sources of open data. The database aims to enhance access to a harmonized collection of building footprints across Canada.

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.

Bu büyük ölçekli açık veri kümesi, 50 cm yüksek çözünürlüklü uydu görüntülerinden elde edilen binaların ana hatlarından oluşur. Afrika, Latin Amerika, Karayipler, Güney Asya ve Güneydoğu Asya'da 1,8 milyar bina tespiti içerir. Çıkarılan sonuç 58 milyon km²'lik bir alanı kapsıyordu. Bu veri kümesindeki her bina için, binanın yer kaplamasını açıklayan poligonu, binanın bina olduğundan ne kadar emin olduğumuzu gösteren bir güven puanını ve binanın merkezine karşılık gelen bir Plus Kodu ekleriz. Binanın türü, açık adresi veya geometrisi dışında herhangi bir bilgi yok. Bina ayak izleri, nüfus tahmini, şehir planlama ve insani yardımdan çevre ve iklim bilimine kadar çeşitli önemli uygulamalar için kullanışlıdır. Gana merkezli olan projenin ilk odak noktası Afrika kıtası olmakla birlikte Güney Asya, Güneydoğu Asya, Latin Amerika ve Karayipler ile ilgili yeni güncellemeler de sağlanmaktadır. Çıkarsama, Mayıs 2023'te yapılmıştır. Daha fazla bilgi için OpenBuildings veri kümesinin resmi web sitesine göz atın.

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

Previously posted versions of the data are retained to comply with Local Law 106 of 2015 and can be provided upon request made to Open Data.

تتألف مجموعة البيانات المفتوحة هذه على نطاق واسع من مخططات المباني المستمدة من صور الأقمار الصناعية العالية الدقة التي يبلغ قياسها 50 سم. ويتضمّن 1.8 مليار عملية رصد للمباني في أفريقيا وأمريكا اللاتينية وجزر الكاريبي وجنوب آسيا وجنوب شرق آسيا. وقد غطّى الاستنتاج مساحة تبلغ 58 مليون كيلومتر مربع. ولكل مبنى في مجموعة البيانات هذه، ندرج المضلع الذي يصف …

High resolution buildings dataset for Lake County, IN. The primary sources used to derive this buildings layer were 2013 LiDAR data and 2013 Ortho imagery. Ancillary data sources included GIS data provided by Lake County or created by the UVM Spatial Analysis Laboratory. This buildingsdataset is considered current as of Summer, 2013. Object-based image analysis techniques (OBIA) were employed to extract building information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2,500 and all observable errors were corrected.This dataset contains footprints for buildings and some large out buildings. Many garages and sheds are not included in this dataset.

Benefits and key featuresUnderstand your area in detail, including the location of key sites such as schools and hospitals.Share high-quality maps of development proposals to help interested parties to understand their extent and impact.Analyse data in relation to important public buildings, roads, railways, lines and more.Present accurate information consistently with other available open data products.

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.

This data shows the digitized building footprints of buildings located within the City of Winchester, Virginia. This data was collected off Eagleview 2017 aerial imagery and was provided to the City after the flight.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) buildings data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

这个大型开放式数据集包含从高分辨率50 厘米卫星图像派生出来的建筑物轮廓。其中包含非洲、拉丁美洲、加勒比地区、南亚和东南亚的18 亿个建筑检测结果。推理涵盖了5800 万平方公里的区域。对于此数据集中的每座建筑物，我们都包含描述其…

(Link to Metadata) This dataset models building footprints in multiple contexts; contexts include emergency management, planning, and analysis. It's based on the VT Building Footprints Geospatial Data Standard.Generally, this dataset is updated weekly.NOTE--This dataset is NOT intended for uses such as property assessment and site engineering.For a dataset that models footprints of other VT E911 features of interest (e.g., solar fields, alpine trails, sporting fields, and quarries/mines), go to VT E911 Other Mapped Features of Interest.

這個大型開放式資料集包含建築物輪廓，這些輪廓是從高解析度50 公分衛星圖像擷取而來。這份資料包含非洲、拉丁美洲、加勒比海、南亞和東南亞的18 億棟建築物偵測結果。推論範圍涵蓋5800 萬平方公里的面積。 針對這個資料集中的每棟建築物，我們會納入多邊形，說明其在地面上的足跡、信賴分數，表示我們有多確定這是一棟建築物，以及對應建築物中心的Plus Code。除了幾何圖形之外，沒有任何關於建築物類型、街道地址或其他詳細資料的資訊。 建築物足跡可用於一系列重要應用，從人口估計、都市規劃和人道救援，到環境和氣候科學。這個專案位於迦納，最初著重於非洲大陸，並提供南亞、東南亞、拉丁美洲和加勒比海的新更新內容。 推論是在2023 年 5 月進行。 詳情請參閱Open Buildings 資料集的官方網站。

Building footprints in the city of Naperville.

The process of automatic generalization is one of the elements of spatial data preparation for the purpose of creating digital cartographic studies. The presented data include a part of the process of generalization of building groups obtained from the Open Street Map databases (OSM) [1].