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Munich Sentinel2 Crop Segmentation Benchmark (UNET Segmentation)
U-Net is an architecture for semantic segmentation. It consists of a contracting path (Up to down) and an expanding path (Down to up). During the contraction, the spatial information is reduced while feature information is increased. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 for downsampling. At each downsampling step, we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 convolution (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer, a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.
The archive carries no text for this table; the description above is the archive's text for the task UNET Segmentation. archive 2025-07-28
Over time archive 2025-07-28
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Direction inferred from the metric name, not from the archive: Overall Accuracy (higher is better). Points are placed at the row's paper date; 5 of 5 rows carry one.
Results archive 2025-07-28
Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.
| Paper | Code | Ran Syntology | Report | |||||
|---|---|---|---|---|---|---|---|---|
| 1 | Swin UNETR | 95.26 | – | Paper | Code | 2024 | linked, not harvested | report |
| 2 | UNet3D | 94.73 | – | Paper | Code | 2024 | linked, not harvested | report |
| 3 | 3D FPN with NDVI Loss | 93.55 | – | Paper | Code | 2021 | linked, not harvested | report |
| 4 | Sequential Recurrent Encoders | 89.60 | – | Paper | – | 2018 | no code linked | report |
| 5 | DeepLabv3 3D | 85.98 | – | Paper | Code | 2024 | linked, not harvested | report |
All 5 rows shown. 5 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28
Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 0 rows have a graph line, from 0 distinct papers; 0 rows (0 papers) have at least one sample that ran. Counting each paper once: Syntology ran 0 of 0 samples; 0 unverified. Separately, 0 of those 0 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.
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