Semantic Segmentation On Vaihingen

Metrics

mIoU

Results

Performance results of various models on this benchmark

		Paper Title
CMX	82.87	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
LMFNet-2 (	82.49	LMFNet: An Efficient Multimodal Fusion Approach for Semantic Segmentation in High-Resolution Remote Sensing
SA-Gate	81.03	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
V-FuseNet	79.56	Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks
UnetFormer	77.24	UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery
SegFormer-B1	76.92	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
PSPNet	76.79	Pyramid Scene Parsing Network
HRNet-48	76.75	Deep High-Resolution Representation Learning for Visual Recognition
SegFormer-B2	76.69	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
HRNet-18	75.90	Deep High-Resolution Representation Learning for Visual Recognition
SegFormer-B0	75.57	SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
FPN	74.86	Feature Pyramid Networks for Object Detection
DeepLabV3+	72.90	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

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Semantic Segmentation On Vaihingen | SOTA | HyperAI