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Building footprint data is a catalog of polygons that are representative of the range or boundary of each building in a given geographical location. It's used for address information and construction planning. Datarade helps you find building footprint data APIs and datasets. Learn more →
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Raw Location Data
by start.io
2 years of history data
Mobile location GPS data with timestamp and ADID, sourced from our proprietary SDK, cleaned, filtered and structured.
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SafeGraph
Based in USA
SafeGraph is a data company. That's it - that's all we do. We predict the past. We want to understand how humans interact with the physical world. We are a m...
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start.io
Based in USA
Start.io is a mobile data platform. Start.io (formerly StartApp) enables organizations to uncover insights and make data-driven decisions that enhance strate...
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Reomnify
Based in USA
Actionable insights & Profitable decisions using extensive Location data and Alternate Data Sources across verticals like real estate , insurance & retail. G...
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The Ultimate Guide to Building Footprint Data 2021

Learn everything about Building Footprint Data. Understand data sources, popular use cases, and data quality.

What is Building Footprint Data?

Building footprints are crucial environmental polygonal representations of buildings that range from homes, offices, retail shops, government housing structures, industrial complexes, entertainment spots and many more. The overall analysis of building footprint data is crucial as far as accurate geocoding of addresses for given places of interest are concerned. Furthermore, localities, shapes and distribution overlays of built structures both in urban and rural areas are critical pre-requisite for several studies in urban morphology.

How is Building Footprint Data collected?

The collection of building footprint data is twofold. First, it involves the use of semantic breakdown to collect building pixels by aerial imagery through the application of advanced algorithms such as Microsoft’s Deep Neural Networks. The second part is the conversion of these collected building pixels data into geometric polygons that are presented in vector formats. The collection of building footprint data is an advanced form of semi-automatic footprint acquisition that involves blending artificial intelligence and human analysis to source consistent data. This data is a geometrically precise overview of every ground-level structure covering a wide-ranging built environment.

What are the typical attributes of Building Footprint Data?

The most important attribute of building footprint data is that it provides close to accurate outlines of buildings that are sketched along the exterior walls. Also, this data describes the precise size, shape, and location of a building, giving an account of the evaluations of possible vulnerabilities and hazards that may pertain to a specific structure. Furthermore, building footprint data provides spatial features of buildings in terms of spatial locality, form, dispersal, surface space ratio and the association concerning the buildings and other surrounding objects. Some building footprint data also goes beyond this to show the type of buildings that are represented, the available number of floors and data on occupancy. This additional data can help when assessing the vulnerability and risk analysis of buildings for urban development and planning.

What is an AOI?

AOI means “Area of Interest”. An area of interest in building footprint data can refer to a range of different locations, including businesses, agencies, landmarks, malls, and landmarks. An AOI can also cover a larger expanse of land, like a ZIP code, city, or region. Commercial building footprint data for your AOI and field of interest will allow you to see the structures in a given geographical proximity and build building footprint maps specific to your use case.

Is Building Footprint Data the same as GIS data?

Building footprint data is not the same as GIS data in the commercial building footprint data set. Building footprints are main environmental polygonal depictions of buildings ranging from residences, workplaces, department outlets, government housing structures, commercial parks, entertainment venues and many others. The overall processing of building footprint data is critical in terms of precise geocoding of addresses for particular areas of interest, while the Geographic Information System (GIS) is a computer system for recording, saving, testing and showing real-time building footprint data relevant to Earth’s surface locations. By connecting geospatial data points, GIS enables users to better understand spatial patterns and relationships.

What is Building Footprint Data used for?

An accurate overview of building overlays gives an accurate account of the density of built-up areas which is an important indicator of urban morphology or spatial structures of cities and metropolitan areas. Applying building footprint data enhances the precision of geocoding, spatial analysis and data visualization. For instance, the telecommunications industry makes use of this building footprint data to plan where to build cell towers. Additionally, marketers use this data to enhance geo-fencing capabilities through mobile trace data to buildings. This helps them have a better comprehension of population visitation and movement within a given period.

What is Building Footprint imagery?

Building footprint imagery refers to real-time building footprint data images of the Earth’s surface that we capture from satellites, drones and fixed-wing aircraft and other aviation vehicles. It is a type of visual representation of different geographical features. In short, it’s a snapshot of a single region of our world in time. And when imagery providers and GIS software carefully digitize the snapshot and turn it into layers of information, the imagery becomes a map that can be used to grasp, link, visualize, explain and interpret different spatial, sociological and historical building footprint data parameters. Infrastructure preparation, resource discovery, emergency management, and navigation have been the most common uses cases where we’ve seen the advantages of constructing building footprint photos.

What is Building Footprint segmentation?

Building Footprint is a term used to define the borders of the outer walls of a building or structure placed on a piece of land. It can also define the borders of the roof or the enclosed area of the roof framework where there are no walls covering the building or the structure. Building satellite and aerial footprint segmentation is a crucial and challenging phase for high-resolution building map generation which requires real-time building footprint data. For urban management applications, such as building tracking, infrastructure growth, smart three-dimensional cities and building change detection, building footprint data, that can be accessed from data marketplaces via building footprint data subscription, enables cartographers and developers to create accurate, multi-scale building maps.

What is a Building Polygon?

A building polygon is used to represent a structure that is wide enough to be captured on a scale of 1:50,000 and has an area of 625m2 or more. The construction point is used to represent a building with an area too small to be captured as a polygon at a scale of 1:50,000. A complex database of building polygons includes homes, sheds, foundations, offices, undefined buildings. This commercial building footprint dataset is best used to view undefined buildings throughout the chosen region. It can also be used to display the polygon of larger buildings.

What is a Microsoft Building Footprint?

Microsoft building footprint is a machine learning algorithm that uses Bing Maps (Microsoft AI-assisted mapping capabilities) to provide real-time footprint building data. Microsoft has made major investments in machine learning, computer vision and AI related to navigation. Over the last few years, Bing Maps has developed high-quality building footprints that exploit AI and harness the ability of computer vision to recognize map features on a scale. Microsoft depends on the Open Source CNTK Unified Toolkit, built by themselves, to do this. Using CNTK Deep Neural Networks and ResNet34, up-sampling layers of RefineNet were used to detect building footprints from Bing imagery. Polygonization algorithm is used to sense building edges and angles in order to establish a correct building footprint. The price of Microsoft building footprint data is typically greater than the purchase of building footprint data that can be bought from alternative data vendors.

How can I download Building Footprint Data?

Downloading building footprint data comes with a cost. But first, you need to know what you want to do with the data you’re getting. Once you have a clear use case, you can then source building footprint data from Datarade’s data marketplace online and shop for building footprint data. You have the chance to input the building foot print data you want or input the providers names for easy tracking and comparison. Purchasing building footprint data to download can be arranged via a building footprint data subscription, where you download commercial data in real-time data. Data available to download also includes historical building footprint data.

How can a user assess the quality of Building Footprint Data?

A user can assess the quality of building footprint data by assessing several factors that include:

  • Accuracy
  • Coverage
  • Available delivery formats
  • Recency

A building footprint dataset can only be considered accurate if provides information that is at least 95% accurate. That is, the polygons have only a very small margin of error when compared to the original, full scale buildings. Global availability alludes to the fact that quality building footprint data is wide-ranging in such a manner that it does not exhibit any element of geographical restrictions. Companies that provide this data ought to produce as much data as possible for enhanced coverage, via a range of delivery formats to suit the user’s needs and pre-exisiting systems. The recency aspect of building footprint data shows the quality of metadata that is comprised of images and the precise dates of extraction to ensure that the data provided is not out of date.

Who are the best Building Footprint Data providers?

Finding the right Building Footprint Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Building Footprint Data providers that you might want to buy Building Footprint Data from are SafeGraph, start.io, Reomnify, Ecopia AI, and Here.

Where can I buy Building Footprint Data?

Data providers and vendors listed on Datarade sell Building Footprint Data products and samples. Popular Building Footprint Data products and datasets available on our platform are Raw Location Data by start.io.

How can I get Building Footprint Data?

You can get Building Footprint Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Building Footprint Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Building Footprint Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Building Footprint Data?

Building Footprint Data is similar to HERE Map Data. These data categories are commonly used for Retail Site Selection and Building Footprint Data analytics.

What are the most common use cases for Building Footprint Data?

The top use cases for Building Footprint Data are Retail Site Selection, Store Visit Tracking, and Store Visit Attribution.