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solarPV_identification/PV_postprocessing/README.md
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Alina's code for linking PV-predicted pixels with buildings and roofs ID
1. I merged the TLM data into building polygons, each with a unique BLD_ID; this BLD_ID is attributed to each roof and each TLM polygon
- Merged polygons: *TLM_GVA_dissolved*
- Roofs with BLD_ID: *SOLKAT_DACH_GVA_BLD_ID*
- TLM with BLD_ID: *TLM_BLD_ID_GVA*
2. I have buffered the polygons, once by 3m and once by 5m:
- *TLM_GVA_buffer_3m, TLM_GVA_buffer_5m*
- Code for steps 1 and 2 is in **Create_building_polygons.ipynb**
3. I have spatially joined the PV pixels with the roofs, and the remaining ones with the buffered buildings:
- *PV_pixels_detected.csv* in /scratch/walch/pv_linking/files (fidis)
- Code is in **spatial_join_pv.py** - this is the computationally expensive bit, I have split it into batches
4. I have applied spatial clustering (DBSCAN) to the selected pixels, and excluded all clusters with less than 6m2 size
- *PV_pixels_clustered.csv* in /scratch/walch/pv_linking/files (fidis)
- Code is in **cluster_pv.py**
5. I have grouped the remaining pixels first per roof, then per building (which represents the individual installations as discussed)
- *PV_per_roof.csv* in /scratch/walch/pv_linking/files (fidis)
- *PV_installations.csv* in /scratch/walch/pv_linking/files (fidis)
- Code is in **group_PV_to_buildings.py**
To run steps 3-5 on the cluster, use **exec_intersect_pixels_w_roofs.sh**. The script is necessary since you have 9M pixels, so I am processing the spatial join in batches.
For the development of steps 3-5 I have used **Existing_PV_per_roof_GVA.ipynb**, but that might not be very understandable since it got a bit long.
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