When we introduced our first building footprint dataset in June 2016, we knew that it had the potential to revolutionize location data. Two years later we speak daily with business and technical leaders across industries and they understand the value of building footprint data.
We know that building footprint data will displace the current state-of-the-art datasets – parcel data and address point data – for key business problems across industries. Whether a business problem requires locating, analyzing or visualizing information, building footprint data provides a higher accuracy solution than parcel and address point data.
As a geospatial data consumer you now have choices. Recent developments in building footprint data built exclusively from aerial imagery are amazing technical and open data achievements; however, these datasets may not deliver the full value of building footprint data for locating, analyzing and visualizing information as a part of your key business operations.
When you evaluate your provider of these next generation datasets, there are four questions that you can ask to insure you are getting the best data solution for your organization.
How richly Detailed is the data?
A building footprint has its highest analytical value when a single object describes the extent of a single building – no more, no less. Does each building footprint have the right level of detail? A building footprint that encompasses many buildings has limited value. In both commercial and open data you can see single objects that surround many buildings, for example a set of connected high rise buildings in a downtown area, a series of adjacent residential buildings, two manufacturing buildings that share a wall but are distinct buildings.
Buildings in commercial sections of urban areas might have a single object that encompasses them; these objects clearly cannot distinguish the individual buildings.
Two building polygons encompass 18 commercial buildings in one dataset
Residential neighborhoods with adjacent buildings are captured as single polygons that surround clusters of buildings.
In building footprint data lacking detail an individual building polygon can encompass as many as 30 separate, adjacent residential buildings.
It is exceptionally difficult to distinguish what is and is not the extent of a building while being limited to what you see from above. If 5, 10 or 20% of your buildings (or over 75% in some of urban areas) lack the detail to distinguish adjoining buildings the data has low value for your critical business processes that involve locating, analyzing and visualizing. For example, a Telecom company would not be able to model the height of each building for 5G network planning; an insurance company would not be able to distinguish the specific spatial-related risk for each residential building.
What Attribution comes with the data?
For two years we have spoken with companies across all industries – insurance, telecommunications, mobile ad tech, real estate, and more. With few exceptions, they believe a building footprint polygon with no attribute information is of little value.
Does your building footprint data have an address or addresses and all of the secondary address information associated with the building? Does your building footprint data come with height and ground elevation? Can your building footprint data easily connect with other commercial datasets? Can you join building footprint data with your own proprietary data?
If a building footprint dataset comes with limited or no attribution then it is up to you to bring your own address information and other datasets to the analysis.
Does using the data come with Limitations?
New mass-manufactured commercial datasets and open datasets come with their own challenges.
The most common open datasets that you might download are distributed with the Open Data Commons Open Database License (ODbL) license. Every organization should evaluate the limitations posed by an ODbL license. It may be important to you that the ODbL license makes it exceptionally challenging to enhance the data and blend the data with other datasets (proprietary and commercial) without risking the proprietary nature of the combined data.
For new commercial datasets, we would expect trends that are similar to the appearance of new data in the past. Commercial data companies will be very hesitant to allow unfettered use of the data, and will be quick to put up walls or barriers around your use of the data, or will charge prohibitive fees for your desired use case.
Is the data Affordable?
Innovative data shouldn’t have to carry a premium price. Open data building footprints obviously have a price of $0, and your only cost is to integrate and use the data. That has enabled many people to experiment risk-free with these open datasets, which we think is good for advancing awareness of building footprints..
New mass-manufactured commercial datasets are a different story. Creating building footprints from imagery comes with a cost; with many companies involved in production and years of R&D investment, the prices for these new datasets are set at a premium. We believe the most exciting innovation comes when you can get a much better mousetrap AND it costs less. Geospatial data innovation can and should have that same philosophy.
BuildingFootprintUSA™’s Answer to the 4 Questions
Everything that we do to create, deliver and support our products is done to have the best possible answers to the 4 questions above. Want to understand how we answer these questions? Start a conversation with us at firstname.lastname@example.org. We are creating a richly attributed building footprint dataset with the highest fidelity and affordability — and a dataset that can be used the way you need to use it.