We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| microsoft/farmvibes-ai | pwc_unofficial | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| meshgrid_1d_array | Ran | microsoft/farmvibes-ai/ops/chunk_raster/chunk_raster.py code served (permissive licence) · get_code("4319fd10bf309879") |
| post_process | Not yet run | microsoft/farmvibes-ai/ops/compute_cloud_prob/compute_cloud_prob.py code served (permissive licence) · get_code("3bd59bc7a1a36fa6") |
| softmax | Not yet run | microsoft/farmvibes-ai/ops/compute_cloud_prob/compute_cloud_prob.py code served (permissive licence) · get_code("1b111adfaff1bbd9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images. We leverage radio frequency (RF) signals in the ultra/super-high frequency band that penetrate clouds to help reconstruct the occluded regions in multispectral images. We introduce the first multi-modal multi-temporal cloud removal model. Our model uses publicly available satellite observations and produces daily cloud-free images. Experimental results show that our system significantly outperforms baselines by 8dB in PSNR. We also demonstrate use cases of our system in digital agriculture, flood monitoring, and wildfire detection. We will release the processed dataset to facilitate future research.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2106.08408")
get_code_for_paper("2106.08408")
have("2106.08408")
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