Semantic Segmentation on ISPRS Potsdam

Metric: Overall Accuracy (higher is better)

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#	Model↕	Overall Accuracy▼	Extra Data	Paper	Date↕	Code
1	AerialFormer-B	93.9	No	AerialFormer: Multi-resolution Transformer for A...	2023-06-12	Code
2	ViT-G12X4	92.58	No	A Billion-scale Foundation Model for Remote Sens...	2023-04-11	-
3	FT-UNetFormer	92	Yes	UNetFormer: A UNet-like Transformer for Efficien...	2021-09-18	Code
4	DC-Swin	92	Yes	A Novel Transformer Based Semantic Segmentation ...	2021-04-25	Code
5	LSKNet-S	92	No	LSKNet: A Foundation Lightweight Backbone for Re...	2024-03-18	Code
6	EfficientUNets and Transformers	91.8	Yes	Semantic Labeling of High Resolution Images Usin...	2022-06-20	-
7	IMP-ViTAEv2-S-UperNet	91.6	Yes	An Empirical Study of Remote Sensing Pretraining	2022-04-06	Code
8	MANet	91.318	Yes	-	-	Code
9	UNetFormer	91.3	Yes	UNetFormer: A UNet-like Transformer for Efficien...	2021-09-18	Code
10	ABCNet	91.3	No	ABCNet: Attentive Bilateral Contextual Network f...	2021-02-04	Code
11	ViTAE-B + RVSA -UperNet	91.22	Yes	Advancing Plain Vision Transformer Towards Remot...	2022-08-08	Code
12	RSP-ViTAEv2-S-UperNet	91.21	Yes	An Empirical Study of Remote Sensing Pretraining	2022-04-06	Code
13	BANet	91.06	Yes	Transformer Meets Convolution: A Bilateral Aware...	2021-06-23	Code
14	RSP-Swin-T-UperNet	90.78	Yes	An Empirical Study of Remote Sensing Pretraining	2022-04-06	Code
15	ViT-B + RVSA-UperNet	90.77	Yes	Advancing Plain Vision Transformer Towards Remot...	2022-08-08	Code
16	RSP-ResNet-50-UperNet	90.61	Yes	An Empirical Study of Remote Sensing Pretraining	2022-04-06	Code
17	PSPNet (SAP)	88.56	No	Stochastic Subsampling With Average Pooling	2024-09-25	-

#1AerialFormer-BSOTA
93.9
Overall Accuracy· 2023-06-12
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation Code
#2ViT-G12X4SOTA
92.58
Overall Accuracy· 2023-04-11
A Billion-scale Foundation Model for Remote Sensing Images
#3FT-UNetFormer
92
Overall Accuracy· Extra Data· 2021-09-18
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery Code
#4DC-SwinSOTA
92
Overall Accuracy· Extra Data· 2021-04-25
A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images Code
#5LSKNet-S
92
Overall Accuracy· 2024-03-18
LSKNet: A Foundation Lightweight Backbone for Remote Sensing Code
#6EfficientUNets and Transformers
91.8
Overall Accuracy· Extra Data· 2022-06-20
Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers
#7IMP-ViTAEv2-S-UperNet
91.6
Overall Accuracy· Extra Data· 2022-04-06
An Empirical Study of Remote Sensing Pretraining Code
#8MANet
91.318
Overall Accuracy· Extra Data
No paperCode
#9UNetFormer
91.3
Overall Accuracy· Extra Data· 2021-09-18
UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery Code
#10ABCNetSOTA
91.3
Overall Accuracy· 2021-02-04
ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images Code
#11ViTAE-B + RVSA -UperNet
91.22
Overall Accuracy· Extra Data· 2022-08-08
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model Code
#12RSP-ViTAEv2-S-UperNet
91.21
Overall Accuracy· Extra Data· 2022-04-06
An Empirical Study of Remote Sensing Pretraining Code
#13BANet
91.06
Overall Accuracy· Extra Data· 2021-06-23
Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images Code
#14RSP-Swin-T-UperNet
90.78
Overall Accuracy· Extra Data· 2022-04-06
An Empirical Study of Remote Sensing Pretraining Code
#15ViT-B + RVSA-UperNet
90.77
Overall Accuracy· Extra Data· 2022-08-08
Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model Code
#16RSP-ResNet-50-UperNet
90.61
Overall Accuracy· Extra Data· 2022-04-06
An Empirical Study of Remote Sensing Pretraining Code
#17PSPNet (SAP)
88.56
Overall Accuracy· 2024-09-25
Stochastic Subsampling With Average Pooling