This is the python code for detecting rooftops from Aerial RGBD (and IR if available) data using simple image processing techniques. It uses moderate computational resources and has low interface time for segmenting rooftops. Works best on elevation data generated from photogrammetry with around 5cm ground resolution. The two inputs are the RGB (ortho-photo) and the Digital Elevation Model (DEM) geotifs. IR band data may be additionally provided for using NDVI in removing tree canopies more accurately. The shape files for the roofs and clutter in roof are generated as output which can be opened in any GIS software like QGIS.
Kritik Soman. 2019. Rooftop Detection using Aerial Drone Imagery. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD '19). ACM, New York, NY, USA, 281-284. https://dlnext.acm.org/doi/abs/10.1145/3297001.3297041
cv2
gdal
scipy
skimage
matplotlib
numpy
multiprocessing
osgeo
[1] Download the repository zip and upload on Colab.
[2] Unzip using the command! unzip Rooftop-Segmentation.zip
[3] Open demo.ipynb, change directory using % cd Rooftop-Segmentation and run cells.
[1] Rooftop segmentation
| Our Dataset | ISPRS Potsdam Dataset |
|---|---|
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[2] Clutter and Rooftop segmentation
| DEM consisting of rooftops | Ortho-photo of rooftops | The detected rooftops along with clutter |
|---|---|---|
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[3] DEM Segmentation
[4] Rooftop segmentation when roof is covered with grass
| Ortho-photo | Segmented rooftops |
|---|---|
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This code was tested with following version of packages:
cv2 3.3.0
gdal 2.1.3
scipy 0.19.1
skimage 0.13.0
matplotlib 2.1.1
numpy 1.13.3