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  • Product Sheet: Building Inventory

Building inventory


Land use and building density, Prey Veng, Cambodia, 2011 (Source: GeoVille/ESA/SOS Children Village)




Component products

Land Use


  • N/A



  • Environmental monitoring – Baseline historic mapping of environment and ecosystems
  • Environmental monitoring – Continuous monitoring of changes throughout the lifecycle
  • Environmental monitoring – Natural hazard risk analysis
  • Logistics planning and operations – Facility siting
  • Logistics planning and operations – Pipeline routing
  • Logistics planning and operations – Transportation network planning
  • Logistics planning and operations – Monitoring of assets

Geo-information requirements

  • Detailed land use information
  • Distribution and status of infrastructure
  • Distribution and status of assets


EO data can provide detailed information for small and large urban areas. With very high-resolution satellite images with a ground resolution of 1 m or less it is possible to map individual buildings and derive information on the construction properties.

The product provides a building inventory (footprints) with construction material (i.e. wood, concrete, brick, mud, etc.) based on high to very high resolution optical satellite images and in-situ information. For building material and construction assessments, in-situ data is needed. This prevails also for additional parameters such as building height which, to a certain point, can be derived from stereo and tri-stereo satellite images.

Building inventories are one of the core components of disaster vulnerability and loss estimations models, and as such, play a key role in providing decision support for risk assessment, disaster management and emergency response efforts. Therefore, to perform a comprehensive damage and vulnerability assessment and loss evaluation of urban area, a complete inventory of structures is a must.

Furthermore, building inventories can be of importance for valuation of land for compensation / purchase.

This product delivers maps or raster/vector digital files that delineate and identify:

  • Building footprints, building count, building area, building density
  • Building material, building height (number of stories - estimates), floor area
  • Database of building parameters & construction classes

Known restrictions / limitations

Persistent cloud cover can be an issue for acquiring optical satellite data in the tropics. This may be mitigated by combining radar and optical satellite images to map building inventories. The size of the mapped objects is dependent on the sensor used and its resolution.

Some parameters, for example construction material, can only be extracted with lower accuracies and in some cases cannot be distinguished or extracted by using EO data. In these cases or for high accuracies the availability of good in-situ data is needed. The same applies for building heights, which depend on the quality of stereo or tri-stereo satellite data based nDSM (normalised Digital Surface Model). For high accuracies LiDAR data can be needed.

Example: Mapping of buildings with footprints of 4m² or less using 2.5 m (VHR2) is not possible, thus VHR1 imagery with resolutions equal to or less than one metre are needed. Larger building footprints can be mapped using HR1 imagery.

Lifecycle stage and demand











Pre-licensing & Exploration:

  • Selection of an appropriate development site for an onshore facility, as site needs to be accessible, safe, connect to local O&G infrastructure (if any) and have limited impact on the environment.
  • Valuation of land for compensation / purchase and to document evidence relating to the status of the area at the given time and date.
  • Identify locations or traditional land use patterns of tribes in remote areas to more sensitively plan exploration activities.

Development & Production:

  • Information for monitoring the social implications of O&G development e.g. changes in land use, monitor impacts caused by construction activity, identify areas of success and improvement.

Geographic coverage and demand

Demand and coverage is global.


OTM:024 Encroachment on O&G assets
OTM:028 Land use mapping to detect the social impact of O&G developments
OTM:035 Assessing the social impact of construction work
OTM:036 Geohazard exposure analysis
OTM:039 Selection of development sites
OTM:063 Resettlement assessment
OTM:065 Floodplain mapping
OTM:072 Monitoring flash floods
OTM:075 Creating base maps in politically challenging regions

HC:1201 Identify up-to-date general land use patterns to plan access and apply safe setback distances

HC:2501 Characterization of surface/near-surface structural geological properties for infrastructure planning

HC:3201 Assessment of infrastructure placement and effects to the surrounding environment
HC:3203 Management of surface impacts due to ground deformation from operations
HC:4204 Monitoring local communities and land use in the project area
HC:4306 Assess and manage forest fire risk to facilities and infrastructure
HC:5103 Identify subsurface infrastructure for planning of pipeline crossings
HC:5201 Monitoring assets for risk management
HC:5306 Assessing terrain stability for infrastructure planning in permafrost environments
HC:5307 Assess coastal environment for infrastructure planning


Input data sources

Optical: VHR1, VHR2, HR1

Radar: VHR1, VHR2 (supporting optical data)

Spatial thematic data:

  • Digital elevation models (DEM) – LiDAR based Digital Surface Model (DSM) or optical or radar-based VHR2 DSM
  • Existing GIS data such as infrastructure and assets
  • Local knowledge

Spatial resolution and coverage

Spatial resolution: 0.5 - 10 m pixel size

Minimum Mapping Unit (MMU)

Minimum mapping unit (MMU) is dependent on the input data used. For 0.5 m input data it is between 25 m² and 50 m² for example.

Accuracy / constraints

Thematic accuracy: 80-90%

Spatial accuracy: Target is one pixel, but accuracy depends on the input data

Accuracy assessment approach & quality control measures

Stratified random points sampling approach utilizing VHR reference or other geospatial in-situ data. Statistical confusion matrix with user’s and producer’s accuracy as well as kappa statistics for building inventory.

Frequency / timeliness

Observation frequency: The frequency is constrained by satellite revisit and acquisition timeframes, but also processing requirements. While the minimum frequency is technically driven by the revisit cycle of the satellite, the maximum frequency is defined be the customer. Depending on the requirements of the customer the best suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency. Most of the time, long-term changes are detected over intervals of 2 years or longer. Short-term changes, e.g. monitoring of construction sites, are normally detected on a monthly or quarterly basis.

Timeliness of deliverable: Depending on size of the mapped area, resolution, MMU.


Freely available or commercially acquired depending on the sensor selected.

Delivery / output format

Data type:

  • Vector formats
  • Raster formats (depending on customer needs)

For detailed building information the use of vector data would be superior to raster data, as multiple raster files would be necessary to convey the same information. Furthermore, vector data is capable of keeping more complex information than raster data.

File format:

  • Geotiff or shapefile (standard - any other OGC standard file formats)

 Download Product Sheet


Lead Author:GeoVille
Peer Reviewer:Hatfield Consultants


Maria Lemper, Jan Militzer

Document Title:

Building inventory

# of Pages:



Internal – Project consortium and science partners


External – ESA

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