Building Footprints and AI
Defining Building Footprints
Building footprint is defined as follows by ‘The Free Dictionary’: The area on a project site that is used by the building structure and is defined by the perimeter of the building plan. Parking lots, landscapes, and other non-building facilities are not included in the building footprint.
A more common definition used by cartographers and planners for building footprints is: Building footprints are roof representations moved to the base of the structure.
A building footprint map provides the schema (outline) of a building drawn along the exterior walls, with a description of the exact size, shape, and location of its foundation. It represents the total area of a building and provides a better description of its spatial characteristics compared to a point representation in terms of spatial location, form, distribution, floor space ratio and relationship between buildings and other objects (topological, orientation-wise, proximity wise, etc.)
Building footprints are roof representations moved to the base of the structure
Why building footprints
In today’s world, buildings form the basic “building block” of civilization. Buildings have been normalized in our lives and evidently are equated with progress. Thus, understanding buildings is one of the key ways in which one can evaluate and manipulate the progress of society. The major method of analyzing buildings is through building footprints’
Buildings are one of the most important elements-at-risk for risk assessment. A building houses both assets and people and the behavior and response of a building to a specific hazard determines the damage to be incurred as well as the number of people to be injured or killed. Building footprints can ease the process of risk analysis. It can provide line of sight analysis for future development, planning, and visualization. Having such a solution can also help organizations to effectively monitor assets, change detection as well as taxation related issues.
In today’s world, buildings form the basic “building block” of civilization. Buildings have been normalized in our lives and evidently are equated with progress.
What objective information can be derived from a building footprint?
Building footprints can help one to identify the following information
- Number of buildings within a given perimeter or a property
- Risk identification from nearby trees, water, and other hazardous surrounding elements.
- Complete locational accuracy by verifying a building’s exact location through rooftop geofencing methods
- The building geometry data can be used to associate other relevant data that may relate to a specific location, business, product, market, etc.
Uses of Building footprints in Industries & Organisations
Building footprints are used by numerous industries including and not limited to the following:
Insurance: Building footprints can be used to assess risks, make better decisions at underwriting and claim settlements.
Landscape: Landscape design, maintenance, and construction employ numerous and varied use of building footprints at every stage.
Real Estate: Real Estate evaluations can be conducted remotely with the help of building footprints and other related situational datasets.
Navigation: Building footprints are used extensively to create navigation maps and produce highly accurate street annotations and road geometry.
Social & Economic Organisations: Building footprints are used in a variety of ways for different scopes ranging from poverty maps to ridding a war-torn country of IEDs.
Defense: Building footprints is a major component for GeoInt or Geospatial-Intelligence, a recent yet powerful technology used in defense.
Apart from the above examples, building footprints are being used for Geo-marketing, R&D, urban planning, rural development, policymaking, etc. Clearly, the scope of use for this data is innumerable and only limited by the user’s imagination.
Clearly, the scope of use for building footprints is innumerable and only limited by the user’s imagination.
What building footprint data looks like
Building footprints are technically a “feature” that can be extracted from a geospatial imagery. Each of these features is associated with two parameters:
- Geometry: This is manifested on the imagery in the form of a polygon that encapsulates the built-up area of the footprint of the building. It is exchanged between systems as a vector. Moreover, while doing the extraction, you can ask for joint buildings to be split into individual sections based on roof profile.
- Attributes: Attributes refer to the qualities or values associated with the building footprints. These could be surface or built-up area, perimeter length, height, roof type, superstructures on the roof to be captured or not(a Yes/No attribute), minimum size of superstructure if it needs to be captured, presence of swimming pool, distance from trees, etc.
How to create building footprints
Building footprints are not readily available. There are numerous organizations that perform custom extraction of such data. Open Sources are very few and might not contain complete or relevant data. Having said that, the three most common ways of obtaining building footprints for a given area of interest are as follows:
- Collection from the available data set, i.e. cadastral map,
- Creating a new dataset from ground surveys, or
- Creating a new dataset from remote sensing data
1. Cadastral Maps
Cadastral maps are a very good source to provide good quality building footprint data. However, this data might not be up to date and lack detailed information about building characteristics such as building type, building use, building materials, roof type, etc. Also, manual processes for extracting data from such maps can be very time consuming and labor-intensive work. The complex shapes of buildings and various compositional materials of roof are tough to detect compared to simple rectangular building roofs. The extraction of individual building footprint is even more complicated in urban areas where space between buildings is very close such as in slum areas, and many other objects in close proximity such as trees, power lines that may occlude the building’s rooftops. Hence there is a need for faster and more reliable method for obtaining a building footprint map.
2. Ground Surveys for building footprints
Ground surveys involve sending a team of personnel to the specific building location where they perform various surveying and measurement processes to record building attributes and dimensions. Even though ground surveys are one of the most easy methods to obtain data about buildings, they suffer from a variety of major disadvantages as follows:
- Cost is high as numerous ground personnel and equipment are hired to perform building survey
- The turnaround time for the data is high
- Manual errors are inherently frequent during this process
- Digitization is not available and hence either another process is required for digitization or else data cannot be fed into digital systems for further analytics and planning.
3. Extracting building footprints from remote sensing data
The most efficient, effective and accurate way of obtaining building footprints is through the use of remote sensing data.
Remote sensing data is the geospatial data captured by satellites, airplanes or drones. These vehicles contain sensors that capture high- resolution imagery of the earth’s surface. Use of geospatial data is not new but the level of detail and scale that could be achieved was limited. Lately, the industry has seen a proliferation of high-resolution source data -30 cm satellite imagery, 5 cm aerial imagery and high-density LiDAR point clouds.
The availability of this data provides the raw material for building ‘building footprints’. This extraction of building footprint is done as a GIS data layer through the use of extraction platform and software like QGIS and others.
What it entails is the use of manual skills and time for extraction of highly detailed building geometry polygons and attributes. This is one of the most time-consuming and tedious processes in this entire chain. As a result, the timelines and deliverables are often later than required. Project timelines are pushed and cost heads inflate just to align the schedule with the deadline of building footprint delivery. For example, deployment of assets like 5G mobile networks and autonomous vehicle technologies, proactive land-use planning, enforcement of regulations and emergency response should not get delayed due to long geodata production cycles.
This problem has been a persistent bottleneck in this entire geospatial value chain. However, this problem has been resolved now with the use of deep learning and AI.
Raw Materials for developing building footprints with AI
The basic raw materials needed to develop building footprints are geospatial imagery of the required location and an artificial intelligence algorithm capable enough to identify and extract building footprint data and deliver it in a usable format.
Geospatial Imagery: Geospatial Imagery contains information sensed by satellite, drone, and airplanes with highly specialized sensors and equipment. Given the rapid development of remote sensing in the last couple of decades, geospatial imagery is one of the most relevant, updated and accurate raw materials to extract building footprints from. To understand the various aspects of imagery please refer to the article: The Right Geospatial Imagery for your Project.
AI: An AI runs through input geospatial imagery and performs a trained extraction of information bits from it. These information bits are saved in a standard GIS formats like Shapefiles, KML, etc. The information basically consists of vector layers which can be used in various application, products or software. These layers can also be used to create visualizations alone or along with other data layers for analysis and calculations.
Now, the main concern, in this case, is the AI. It has to be developed and trained with data that helps it turnout building footprint layers very fast as well as accurate. Attentive AI has developed AI to extract relevant and required information from imagery in the shortest time with more than 97 percent accuracy. Attentive AI has also developed an online feature extraction platform called MapX, where users can request feature extraction at any time and get near-instant delivery in the required format.
Use of AI in building footprint
Attentive AI has developed a deep learning solution that extracts building footprints from high-resolution satellite imagery using machine learning algorithms (a type of artificial intelligence) and converts it into extensive HD vector maps. These maps, called digital maps, are a source of vector-based information with planetary coverage and relatively high accuracy.
Through the use of AI, this process becomes more than 5 times faster and the building footprint data layers are delivered almost instantly and on-demand. Moreover, the accuracy of this process is high, avoiding any manual pitfalls arising out of fatigue and other dispositions.
Thus, with the use of AI and geospatial imagery, one of the most complex problems has been solved. Organizations are strengthening their offerings and sales with building footprints data. As a result, the competitiveness is on the rise. As a business leader, you shouldn’t wait to use geospatial data to achieve more for less. Contact us to understand how you can use building footprints and we will tell you all about your buildings.