The document outlines the extraction of building footprints from satellite data using QGIS to aid in urban planning, monitoring city growth, and managing facilities like hospitals and schools. It describes the necessary multispectral satellite data, primarily from Landsat and Sentinel 2, and explains the mathematical formulation for the Index Based Built-up Index (IBI) used to identify built-up areas. Additionally, it highlights the methods for land cover classification utilizing various indices for different land types.

1. Built-Up Index for extraction of
building footprints -
from satellite data using - QGIS

2. Why should we extract building footprints?
●
To understand urban sprawl.
●
To model city growth .
●
To plan facilities like hospitals, schools based on city growth.
●
To monitor master plan execution.
●
To plan/replan transportation networks .

3. What data do we need?
To extract building footprint,
●
We need multispectral satellite data.
●
Landsat satellite data from NASA.
●
Sentinel 2 satellite data from ESA can also be used.
But using these datasets due to their resolutions we will only
be able to extract building footprint but not individual
buildings.
To extract individual buildings we need satellite datasets of
higher resolutions.

4. Bands Wavelength
(micrometers)
Resolution
(meters)
B1 - Ultra Blue 0.435 - 0.451 30
B2 - Blue 0.452 - 0.512 30
B3 - Green 0.533 - 0.590 30
B4 - Red 0.636 - 0.673 30
B5 - NIR 0.851 - 0.879 30
B6 - SWIR 1 1.566 - 1.651 30
B7 - SWIR 2 2.107 - 2.294 30
B8 - Pan 0.503 - 0.676 15
B9 - Cirrus 1.363 - 1.384 30
B10 - TIRS 1 10.60 - 11.19 100 * (30)
B11 - TIRS 2 11.50 - 12.51 100 * (30)
Landsat data specification
Builtup areas can be
mapped using
Green, Red, NIR,
and SWIR bands
of Landsat.

5. Bands useful to extract building footprint
Green
Red Short wave infra red (SWIR)
Near infra red (NIR)
INFRARED SPECTRUMVISIBLE SPECTRUM

6. This is how they look after color coding them
INFRARED SPECTRUMVISIBLE SPECTRUM
Near infra red (NIR)Green
Red Short wave infra red (SWIR)

7. Math behind Index based Builtup Index
IBI result
Green
Red
NIR
SWIR
2 x SWIR / ( SWIR+NIR ) – [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
2 x SWIR / ( SWIR+NIR ) + [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
IBI =

8. Why these bands are used?
●
Normalized difference building index(NDBI) is derived from
NIR and SWIR.
●
Minerals have peculiar signature in this spectrum when
compared to vegetation or water.
●
But sand and soil also have minerals in it. So NDBI sometimes
has similar signatures for buildings and sand/soil.
●
So to enhance building footprint IBI is derived as a
combination of
– NDBI,
– (SAVI or NDVI – depending on plant cover)
– NDWI

9. Computing IBI using QGIS
Load raster layers Green, Red and NIR, SWIR bands in QGIS

10. Computing IBI using QGIS
Go to main menu
>> Choose Raster
>> Raster Calculator
Output
Formula
Input
2 x SWIR / ( SWIR+NIR ) – [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
2 x SWIR / ( SWIR+NIR ) + [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
IBI =

11. Results
2 x SWIR / ( SWIR+NIR ) – [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
2 x SWIR / ( SWIR+NIR ) + [ NIR / (NIR+RED) + GREEN / (GREEN+RED) ]
Ref: Raster styling in QGIS
IBI =
Range of IBI : -1 to +1
Higher value (towards 1) : - Density of builtup areas
Lower value (towards -1) : - Low or No builtup areas
(water, vegetation, etc.,)

12. We convert this to vector using Raster to Vector convertor
Extracted building footprint

13. How can we do Land cover classification?
Various land cover classes can be derived using indices.
Water using NDWI,
Vegetation using NDVI,
Builtup areas with IBI.
These will be helpful to prepare Land use maps.