Semantic Segmentation On Isprs Potsdam

Metriken

Mean F1

Mean IoU

Overall Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

				Paper Title
AerialFormer-B	94.1	89.1	93.9	AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
ViT-G12X4	92.12	-	92.58	A Billion-scale Foundation Model for Remote Sensing Images
LSKNet-S	93.1	87.2	92.0	LSKNet: A Foundation Lightweight Backbone for Remote Sensing
FT-UNetFormer	93.3	87.5	92.0	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
DC-Swin	93.25	87.56	92.0	A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
EfficientUNets and Transformers	93.7	-	91.8	Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers
IMP-ViTAEv2-S-UperNet	-	-	91.6	An Empirical Study of Remote Sensing Pretraining
MANet	-	-	91.318	Multiattention network for semantic segmentation of fine-resolution remote sensing images
ABCNet	-	-	91.3	ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
UNetFormer	92.8	86.8	91.3	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
ViTAE-B + RVSA -UperNet	-	-	91.22	Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
RSP-ViTAEv2-S-UperNet	-	-	91.21	An Empirical Study of Remote Sensing Pretraining
BANet	-	-	91.06	Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
RSP-Swin-T-UperNet	-	-	90.78	An Empirical Study of Remote Sensing Pretraining
ViT-B + RVSA-UperNet	-	-	90.77	Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
RSP-ResNet-50-UperNet	-	-	90.61	An Empirical Study of Remote Sensing Pretraining
PSPNet (SAP)	-	74.3	88.56	Stochastic Subsampling With Average Pooling
U-Net (ConvFormer-M36)	-	89.45	-	U-Net Ensemble for Enhanced Semantic Segmentation in Remote Sensing Imagery
SFA-Net	93.5	-	-	SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

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Semantic Segmentation On Isprs Potsdam | SOTA | HyperAI