One pipeline. One decoder. One loss. One schedule. Five backbone families. The only variable is the encoder — so the results finally mean something.

What Is This?

Remote-sensing segmentation papers routinely change the backbone and the decoder and the loss and the training schedule all at once. The numbers tell you who tuned harder, not which backbone is better.

This repo fixes that. One shared pipeline — swap the backbone — read the truth.

Component	Status
Encoder backbone	🔀 Swapped per experiment — the ONLY variable
Decoder	🔒 Fixed (lightweight U-Net, 256ch, MambaBlock2d)
Loss	🔒 Fixed (Lovász-Softmax + Focal + Boundary)
Training schedule	🔒 Fixed (50k iters, AdamW, poly LR decay)
Augmentations	🔒 Fixed (random crop, flip, colour jitter)
Input resolution	🔒 Fixed (512×512)
Feature interface	🔒 Fixed ({F1–F4} at strides {4, 8, 16, 32})

Checkpoints in This Repository

All checkpoints are best.pth files (highest validation mIoU during training) stored with their original directory structure.

LoveDA Experiments — `Comparison_Experiments/`

MambaVision (NVIDIA hybrid Mamba-Transformer)

Checkpoint path	Training split
`Comparison_Experiments/mambavision_tiny_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/mambavision_tiny_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/mambavision_tiny_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/mambavision_tiny2_512/checkpoints/best.pth`	All→All (v2)
`Comparison_Experiments/mambavision_tiny2_ruraltrain_512/checkpoints/best.pth`	Rural→Urban (v2)
`Comparison_Experiments/mambavision_tiny2_urbantrain_512/checkpoints/best.pth`	Urban→Rural (v2)
`Comparison_Experiments/mambavision_small_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/mambavision_small_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/mambavision_small_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/mambavision_base_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/mambavision_base_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/mambavision_base_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/mambavision_large_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/mambavision_large_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/mambavision_large_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/mambavision_large2_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/mambavision_large2_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/mambavision_large2_urbantrain_512/checkpoints/best.pth`	Urban→Rural

VMamba (cross-scan 2D selective SSM)

Checkpoint path	Training split
`Comparison_Experiments/Vmamb_tiny_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/vmamba_tiny_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/vmamba_tiny_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/Vmamb_small_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/Vmamb_small_512_2/checkpoints/best.pth`	All→All (run 2)
`Comparison_Experiments/Vmamb_small_512_3/checkpoints/best.pth`	All→All (run 3)
`Comparison_Experiments/vmamba_small_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/vmamba_small_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/Vmamb_base_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/vmamba_base_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/vmamba_base_urbantrain_512/checkpoints/best.pth`	Urban→Rural

VisionMamba / Vim (bidirectional Mamba)

Checkpoint path	Training split
`Comparison_Experiments/VisionMamba_tiny_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/visionmamba_tiny_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/visionmamba_tiny_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/VisionMamba_small_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/visionmamba_small_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/visionmamba_small_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/VisionMamba_base_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/visionmamba_base_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/visionmamba_base_urbantrain_512/checkpoints/best.pth`	Urban→Rural

Spatial-Mamba (spatially-aware SSM)

Checkpoint path	Training split
`Comparison_Experiments/spatialmamba_tiny_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/spatialmamba_tiny_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/spatialmamba_tiny_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/spatialmamba_small_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/spatialmamba_small_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/spatialmamba_small_urbantrain_512/checkpoints/best.pth`	Urban→Rural
`Comparison_Experiments/spatialmamba_base_512/checkpoints/best.pth`	All→All
`Comparison_Experiments/spatialmamba_base_ruraltrain_512/checkpoints/best.pth`	Rural→Urban
`Comparison_Experiments/spatialmamba_base_urbantrain_512/checkpoints/best.pth`	Urban→Rural

CNN & Transformer Baselines

Checkpoint path	Model
`Comparison_Experiments/cnn_deeplabv3p_r50_512/checkpoints/best.pth`	DeepLabv3+ ResNet-50, All→All
`Comparison_Experiments/cnn_deeplabv3p_resnet50_ruraltrain_512/checkpoints/best.pth`	DeepLabv3+ ResNet-50, Rural→Urban
`Comparison_Experiments/cnn_deeplabv3p_resnet50_urbantrain_512/checkpoints/best.pth`	DeepLabv3+ ResNet-50, Urban→Rural
`Comparison_Experiments/cnn_unet_r50_512/checkpoints/best.pth`	U-Net ResNet-50, All→All
`Comparison_Experiments/transformer_unetformer_r18_512/checkpoints/best.pth`	UNetFormer ResNet-18, All→All
`Comparison_Experiments/transformerunetformer_resnet18_ruraltrain_512/checkpoints/best.pth`	UNetFormer ResNet-18, Rural→Urban
`Comparison_Experiments/transformerunetformer_resnet18_urbantrain_512/checkpoints/best.pth`	UNetFormer ResNet-18, Urban→Rural

ISPRS Potsdam Experiments — `Comparison_Experiments_ICPRS_potsdam/`

Checkpoint path	Model
`Comparison_Experiments_ICPRS_potsdam/mambavision_tiny_512/checkpoints/best.pth`	MambaVision-Tiny
`Comparison_Experiments_ICPRS_potsdam/mambavision_tiny2_512/checkpoints/best.pth`	MambaVision-Tiny2
`Comparison_Experiments_ICPRS_potsdam/mambavision_small_512/checkpoints/best.pth`	MambaVision-Small
`Comparison_Experiments_ICPRS_potsdam/mambavision_base_512/checkpoints/best.pth`	MambaVision-Base
`Comparison_Experiments_ICPRS_potsdam/mambavision_large_512/checkpoints/best.pth`	MambaVision-Large
`Comparison_Experiments_ICPRS_potsdam/mambavision_large2_512/checkpoints/best.pth`	MambaVision-Large2
`Comparison_Experiments_ICPRS_potsdam/vmamba_tiny_512/checkpoints/best.pth`	VMamba-Tiny
`Comparison_Experiments_ICPRS_potsdam/vmamba_small_512/checkpoints/best.pth`	VMamba-Small
`Comparison_Experiments_ICPRS_potsdam/vmamba_base_512/checkpoints/best.pth`	VMamba-Base
`Comparison_Experiments_ICPRS_potsdam/spatialmamba_tiny_512/checkpoints/best.pth`	Spatial-Mamba-Tiny
`Comparison_Experiments_ICPRS_potsdam/spatialmamba_small_512/checkpoints/best.pth`	Spatial-Mamba-Small
`Comparison_Experiments_ICPRS_potsdam/spatialmamba_base_512/checkpoints/best.pth`	Spatial-Mamba-Base
`Comparison_Experiments_ICPRS_potsdam/cnn_deeplabv3p_r50_512/checkpoints/best.pth`	DeepLabv3+ ResNet-50
`Comparison_Experiments_ICPRS_potsdam/transformer_unetformer_r18_512/checkpoints/best.pth`	UNetFormer ResNet-18

ImageNet Backbone Weights — `weights/imagenet/`

File	Description
`weights/imagenet/resnet50-11ad3fa6.pth`	ResNet-50 ImageNet-1K pretrained
`weights/imagenet/resnet18-f37072fd.pth`	ResNet-18 ImageNet-1K pretrained

Results Summary

Every row shares the same decoder, loss, optimizer, schedule, and data splits. The only variable is the encoder.

LoveDA

Backbone	mIoU (All→All)	mIoU (U→R)	mIoU (R→U)
DeepLabv3+ ResNet-50 (CNN)	43.01	30.36	39.98
UNetFormer ResNet-18 (Transformer)	48.61	34.56	44.84
VMamba-Small 🥇	55.66	40.62	53.52
MambaVision-Large	55.25	38.53	54.01
Spatial-Mamba-Base	48.03	35.23	46.55

ISPRS Potsdam

Backbone	mIoU
DeepLabv3+ ResNet-50	75.09
UNetFormer ResNet-18	74.99
VMamba-Small 🥇	77.59
MambaVision-Large	77.07
Spatial-Mamba-Base	70.00

Key findings:

SSMs outperform CNNs and Transformers by a significant margin under identical conditions (+7–12 mIoU on LoveDA).
Scaling the encoder past VMamba-Small yields diminishing returns under a fixed decoder.
Domain transfer is asymmetric across all backbone families (Rural→Urban consistently outperforms Urban→Rural by 10–15 points) — a data distribution property, not a model property.
Boundary accuracy collapses under domain shift while interior accuracy holds — every backbone, every family.

How to Load a Checkpoint

python

import torch

# Example: load MambaVision-Base best checkpoint for LoveDA All→All
ckpt = torch.load(
    "Comparison_Experiments/mambavision_base_512/checkpoints/best.pth",
    map_location="cpu"
)
# keys: 'model', 'optimizer', 'scheduler', 'iter', 'best_score'
model_state = ckpt["model"]

To build the full model and run inference, clone the code repository and follow the setup instructions:

bash

git clone https://github.com/dineth18/Mamba-Segmentation
cd Mamba-Segmentation/MambaVision   # or VMamba/, spatial-mamba/, etc.
pip install -r requirements.txt

# Set your dataset path (no need to edit config files)
export LOVEDA_ROOT=/path/to/LoveDA
export POTSDAM_ROOT=/path/to/ISPRS_Potsdam

python eval.py --checkpoint path/to/best.pth

Citation

If this benchmark is useful for your research, please cite:

bibtex

@article{wasalathilaka2026controlledbenchmark,
  title={A Controlled Benchmark of Visual State-Space Backbones with
         Domain-Shift and Boundary Analysis for Remote-Sensing Segmentation},
  author={Wasalathilaka, Nichula and Perea, Dineth and Samarakoon, Oshadha
          and Wijenayake, Buddhi and Godaliyadda, Roshan and Herath, Vijitha
          and Ekanayake, Parakrama},
  journal={IGARSS 2026},
  year={2026}
}

Acknowledgements

VMamba — Visual State Space Model
MambaVision — NVIDIA hybrid Mamba-Transformer
Spatial-Mamba — Spatially-aware Mamba
LoveDA — Land-cover domain adaptation dataset
ISPRS Potsdam — Urban semantic labeling benchmark

Built at the University of Peradeniya.

dineth18/Mamba-Segmentation

Mamba-Segmentation