- Saint Lucia
- Saint Vincent and the Grenadines
- 1 Introduction
- 2 Analysing hazards
- 3 Flood hazards
- 4 Landslide hazards
- 5 Risk assessment
- 6 Risk reduction planning
- 7 land use planning
- 8 Critical infrastructure
- 9 Preparedness planning
- 10 Requirements for TORs
- 1. Introduction
- 2. Land use planning
- 3A. Critical infrastructure
- 3B. Critical Infrastructure
- 4. Planning alternatives
- 5. Preparedness planning
- 6. Risk assessment
- 7. Exposure and vulnerability
- 8. Hazard assessment
- 9. Data management
- 10. Super Use Case: St Lucia Flood modelling
- 1 Introduction
- 2 Data requirements
- 3 Base data collection
- 4 Hazard related data
- 5 Elements-at-risk data
- 6 Managing Geospatial data
- 7 Sharing geospatial data
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5.2 Building footprint maps
Keywords: Building footprint, element at risk, cadastral map, high resolution images
Authors: Dr. Manzul Kumar Hazarika, Syams Nashrrullah, Mujeeb Alam, Lixia Chen and Cees van Westen
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. A building footprint provides the outline of a building drawn along the exterior walls, with a description of the exact size, shape, and location of its foundation. Building footprint is the most basic information necessary for evaluating the vulnerabilities of a building for a specific hazard. It represents a 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, proximity, etc.). Once building footprints are available, attribute information such as its type, number of floors, use, occupancy etc. can be added for using them for vulnerability and risk analysis. The characterization of building attributes focuses on the impacts caused by flood and landslide can be found in Chapter 5.2 of Methodology Book. In the following sections, different sources and methods used for generating building footprint maps is explained. Examples of generating building footprint map and attribute characterization for the Caribbean countries is also described here.
- Explain different ways and sources to obtain building footprint maps
- Explain the methodology for generating building footprint maps using remote sensing data
There are several ways to obtain building footprint maps, either collecting from the available dataset such as cadastral map or creating a new dataset from ground survey or remote sensing data. Cadastral maps provide a detailed information about real property of a parcel in a specific area usually for purposes of taxation, thus it may contain also information about building footprint. A ground-based surveying method is a common technique to generate cadastral maps. In some countries, a Land Information System (LIS) is developed as a GIS tool for cadastral maps, which consists of an accurate, current and reliable land record with its associated attributes and spatial data (building footprint). This is a very good source to provide a good quality of building footprint data. However, in many countries the cadastral maps may not be available in proper GIS format. They might be in a data format like AutoCad DXF, which does not have a topology and a complete segment around each polygon. To edit this data for so many polygons is very difficult. The other caution is that the map might not be up to date and detailed information about building characteristics (e.g. building type, building use, building materials, roof type, etc.) is often not included. It is also important to note that sometimes there are restrictions to access cadastral data due to complicated process involving legal issues and security concerns against abuses especially on sensitive data such as taxes.
High resolution optical satellite images and aerial photographs are typical data sources to generate building footprint maps. Unlike cadastral map, building footprints from satellite data are generally extracted from the ‘footprint’ of the roof which may be larger than the building itself. Consequently, the building footprint may have some errors and the exact footprint of the building itself is not known. High-resolution satellite images such as QuickBird, IKONOS, WorldView, and GeoEye are relatively costly. However, now-a-days it is possible to have free high-resolution satellite images from the Bing Map of Microsoft under the Open Street Map (OSM) project. In recent decades, other data sources such as Light Detection and Ranging (LiDAR) and Synthectic Aperture Radar (SAR) has made it possible to obtain height information, which brings new opportunities and challenges for building extraction.
Methods for generation of building footprints from remote sensing data
Building footprints can be delineated using manual, semi-automatic or automatic methods. Manual on-screen digitizing can be a very time-consuming and labor intensive work. While there is no need for a high level of expertise is not required to delineate building footprints, however, the level of experience of the operator does affect the speed of the digitization. Although it can be applied over a wide city area, the time necessary for the digitization makes it mostly appropriate at only over neighborhood/town level. A possible way to improve the scalability is to distribute the work among many GIS operators and collate the results in the end. This method provides great control over the results and the digitized building footprints can be of the highest accuracy.
As previously mentioned, generating building footprint maps for a large area with the manual method is very costly and time consuming. Therefore, the development of semi-automatic or automatic building extraction methods has been explored in many studies. However, the automatic method for building extraction is not an easy tasks and with the current existing method is still impossible to get a perfect 100% accuracy. The complex shape of buildings and various compositional materials of roofs are the main reasons for these difficulties. In many cases, most of simple rectangular building roofs can be detected correctly, while the extraction of building footprints with complex shapes were not satisfactory enough. The extraction of individual building footprint is even more complicated in urban areas where space between buildings are 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.
There are various methods to generate building footprint maps automatically from high resolution satellite images based on the characteristics and geometric structure of buildings, including supervised and unsupervised classification, edge detection methods such as Canny algorithm, Hough transformation, and object-based classification with image segmentation techniques. Other methods such as active contour model (snakes) and energy function have been also introduced to improve the extraction of complex buildings in urban area. With the advance of LiDAR technology, capable to generate 3D terrain data, buildings can be identified based on height, size, and shape information from point clouds. Methods such as plane fitting and region growing algorithm segmenting LiDAR points to identify building roofs, tracing building boundary and regularizing the boundaries.
The current trend of building extraction methods are a mixture of different data sources and various algorithms. High-resolution optical imagery and LiDAR are integrated for more accurate building extraction. Optical imagery provide spectral information, while LiDAR data contains height and intensity information. In some studies, LiDAR data is used to extract non-ground features and then remove the vegetation based on the NDVI derived from optical images. In some other studies, object-based classification of high resolution images is used to extract build-up areas and then analyzed the LiDAR point clouds to shape roofs within the build-up areas.
Although the time required for automatic building footprint extraction methods to generate the results is relatively less compare to the manual digitizing method, however, this method should be implemented by an expert with necessary technical skills. Furthermore, there is still a lot of manual editing is needed to generate satisfactory results, and currently the technique is not a substitute yet for manual interpretation.
Example 1: Generation of a building footprint map for Dominica
We only had a building footprint map for the Roseau area. In order to be able to calculate building exposure, we applied a method for the generation of a building footprint map for the entire country. We used the satellite data as indicated in Table below, and we used a thresholding method for the three spectral bands, separating areas with high reflection.
We combined the three masked areas into one single map, which was still overestimating the number of buildings and contained also other high reflection areas, like bare surfaces, road, quarries etc. Next we converted this raster map into a polygon map, and the polygon map was again converted into a point map. This resulted in a large number of points, many of which didn’t represent buildings but other features. Therefore we analyzed the point visually on top of a color composite image of the satellite image. The resulting building map was developed as a point map, and was carefully checked. The Figure below shows an example for a part of the country.
Example 2: Building attribute characterization for Grenada
The physical planning unit of Grenada had provided building footprints for the whole country. However, no attribute information was available to determine, whether a certain building is a dwelling, market, hospital, a school, or some other structure. Without any attribute information the usefulness of such data becomes limited. To solve this problem a procedure was used to characterize buildings by classifying them based on their possible use. It was important to separate residential buildings from all other buildings so that population information can be attached only with residential buildings. Since, no attribute information was linked with building footprints to make this distinction, the only possible option was to make use of latest available satellite imagery and Google Earth, using visual image interpretation. High resolution imagery of Pléiades satellite was available for the whole island. The resolution of the multi-spectral image is 2 meters whereas panchromatic is 0.5 meters. Both images were fused to get the highest possible resolution with color. This provided a good quality data that could be used together with the vector building footprints to characterize the buildings. Building footprints were overlaid on the Pléiades satellite imagery in ArcGIS for visual interpretation. Two attributes ‘Use type’ and ‘occupancy’ were added in the buildings attribute table. Similarly, the building footprint map was exported to KML (Keyhole Markup Language) format to view with Google Earth.
Obviously, it was not easy to distinguish residential buildings from other buildings even on very high resolution imagery. So, the strategy was to identify and isolate large buildings, which could potentially be hotels, industries, schools, churches, offices, business centers, or supermarkets etc. Snapshots of some examples of buildings identified using Google Earth are presented in Figure 2.
After identifying these visible and known structures, the remaining buildings were considered to be residential. In Grenada, over 85 % residential houses are separate houses, therefore; one cannot expect large population living in the big buildings or apartments. Through, the visual inspection all large buildings and other obvious buildings like schools, forts, churches etc. were identified and characterized (Table 2) them manually. The remaining buildings were considered as residential houses and attributed them ‘residential’.
Figure 3 shows the resulting building footprint layer with added attribute information. When you double click on it you will go to the layer in the GeoNode and can consult it interactively. Of course the attribute information should be improved, through surveys on the ground. However, this is time consuming and generally requires a large team of people to do this. Such an attempt is currently underway in Saint Lucia. The building footprint maps with additions attribute information serve many purposes other than disaster mitigation and preparedness, and their collection and management should be an inter-departmental effort, also involving statistics office. Eventually also a link with census information might be obtained, as is illustrated in Use Case 7.5.
Feng, T., Zhao, J. (2009). Review and comparison: Building extraction methods using high-resolution images. Second International Symposium on Information Science and Engineering.
Zeng, C., Wang, J., Lehrbass, L. (2013). An evaluation system for building footprint extraction from remotely sensed data. Applied Earth Observations and Remote Sensing Vol 6 (3), pp 1640-1652.
Vicini, A., Bevington, J., Esquivias, G. Iannelli, G-C., Wieland, M. (2014). User guide: Building footprint extraction and definition of homogeneous zone extraction from imagery. Global Earthquake Model (GEM) Technical Report.
Bhadauria, A.S., Bhadauria, H.S., Kumar, A. (2013). Building extraction from satellite images. IOSR Journal of Computer Engineering Vol 12 (2), pp 76.81.
van Westen, C.J., Alkema, D., Damen, M.C.J., Kerle N., Kingma, N.C. (2011). Multi-hazard risk assessment: Distance education course Guide book. United Nations University – ITC School on Disaster Geo-information Management (UNU-ITC DGIM).