Urban & settlement map
Land use and building structure, Prey Veng, Cambodia 2011 (GeoVille/ESA/SOS Children Village)
The product provides mapping of urban areas in terms of land cover and land use, as well as the associated temporal changes based on high to very high resolution optical or radar satellite data.
Based on this, the product can provide various urban indicators, as well as infrastructure and building inventories based on construction classes. Typical artificial surface land cover/use classes used in the classification follow the established nomenclatures (CORINE, MOLAND, FAO LCCS, etc.).
Mapping can include:
In the case of building inventories, only part of the building information can be captured, depending on the sensors used for data acquisition. Very high resolution optical sensor imagery makes it possible to estimate building footprints, building location, distance from building to building, building height classes (using stereo image pairs). Other features, such as building height as number of storeys, building material, structure type, load bearing structure system, construction technique, floor area, are more difficult to capture or must be inferred from other contextual information.
Using multi-temporal image information, the product is particularly relevant for monitoring urban expansion. Furthermore, the products serve as a starting point for a range of urban indicators for soil protection and management as well as the monitoring of crucial water supply systems, urban structures, and flood risk control.
While the EO products rarely achieve the accuracy of cadastral data, their accuracies are sufficiently high to form an objective basis for decision-making and enable continuous monitoring over time.
Known restrictions / limitations
A potential limitation of urban and settlement mapping is a high presence of cloud coverage within the analysis region, as optical satellite data is not capable of penetrating clouds. Potential approaches mitigating this issue could be:
1) combining optical and VHR SAR data as they are able to penetrate cloud coverage;
2) using VHR SAR stereo data to produce a nDSM (normalized Digital Surface Model) to support optical classification.
Urban and settlement mapping is also limited by the resolution of the input data used. Structures smaller than double the resolution of the input data cannot be mapped.
Lifecycle stage and demand
Pre-licensing & Exploration:
Development, Production & Decommissioning:
Geographic coverage and demand
In general, the products are independent and up-to-date, available practically around the globe. The demand is global, focusing on urban or densely populated areas.
OTM:024 Urban encroachment on O&G assets
OTM:036 Geohazard exposure analysis
OTM:039 Selection of development sites
OTM:063 Resettlement assessment
OTM:065 Floodplain mapping
OTM:072 Monitoring flash floods
Input data sources
Optical: VHR1, VHR2, HR1
Spatial resolution and coverage
The spatial resolution of most products can reach a few meters depending on the input imagery resolution.
Spatial resolution: < 10 m, but urban mapping can also be performed over extensive areas with 20 m, 30 m or even 100 m resolution.
Minimum Mapping Unit (MMU)
The MMU is depending on the input data resolution, the mapped objects and the accuracy required.
For optical satellite data with 0.5m spatial resolution this can be, for example, a MMU of 9 m².
Accuracy / constraints
The geometric accuracy is usually comparable to the spatial resolution of the input satellite data, i.e. typically a few metres. The thematic (classification) accuracy is in the range of 80–90% depending on the quality of the EO data.
Limits for mapping are always given by great off-nadir look angles and sun shadows.
Thematic accuracy: 80-90% in areas of low vegetation cover and density. Higher accuracies can be reached with manual extraction of features.
Spatial accuracy: The goal would be 1 pixel, but depends on reference 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, producer’s accuracy and kappa statistics for urban areas.
Frequency / timeliness
Observation frequency: The frequency is constrained by satellite revisit and acquisition, but also processing requirements. Depending on the requirements of the customer the most suitable satellite sensor has to be chosen considering spatial / spectral resolution as well as revisit frequency.
Timeliness of delivery: Depends on size of the mapped area, resolution, MMU and number of mapped classes. Automatic procedures may extract urban areas fast but more advanced analysis will require more time. Some analysis using stereo images are time consuming and may require dedicated operators to perform manual work.
Freely available or commercially acquired is depending on the sensor selected.
Delivery / output format
For detailed land use 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 are capable of keeping more complex information as raster data.
|Peer Reviewer:||Hatfield Consultants|
Maria Lemper, Jan Militzer
Urban & settlement map
# of Pages:
Internal – Project consortium and science partners
External – ESA
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