Semantic Segmentation On Isprs Vaihingen

Métriques

Average F1

Overall Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
LSKNet-T	91.7	93.6	LSKNet: A Foundation Lightweight Backbone for Remote Sensing
LSKNet-S	91.8	93.6	LSKNet: A Foundation Lightweight Backbone for Remote Sensing
EfficientUNets and Transformers	93.7	91.8	Semantic Labeling of High Resolution Images Using EfficientUNets and Transformers
DC-Swin	90.7	91.6	A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images
FT-UNetFormer	91.3	91.6	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
UNetFormer	90.4	91.0	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
MANet	-	90.963	Multiattention network for semantic segmentation of fine-resolution remote sensing images
ABCNet	-	90.7	ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images
BANet	-	90.5	Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
UPerNet (SAP)	-	90.14	Stochastic Subsampling With Average Pooling
SFA-Net	91.2	-	SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation

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Semantic Segmentation On Isprs Vaihingen | SOTA | HyperAI