We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gabrielkasmi/deeppvmapper | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| try_download_version | Not yet run | gabrielkasmi/deeppvmapper/download_images.py code served (permissive licence) · get_code("a604c0c9609f6dda") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since public authorities often lack quality data about them. Overhead imagery is increasingly used to improve the knowledge of residential PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2209.03726")
get_code_for_paper("2209.03726")
have("2209.03726")
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