Land use and building density, Prey Veng, Cambodia, 2011 (Source: GeoVille/ESA/SOS Children Village)
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:
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:
Development & Production:
Geographic coverage and demand
Demand and coverage is global.
OTM:024 Encroachment on O&G assets
HC:3201 Assessment of infrastructure placement and effects to the surrounding environment
Input data sources
Optical: VHR1, VHR2, HR1
Radar: VHR1, VHR2 (supporting optical data)
Spatial thematic data:
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
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.
|Peer Reviewer:||Hatfield Consultants|
Maria Lemper, Jan Militzer
# of Pages:
Internal – Project consortium and science partners
External – ESA
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