Semantic Segmentation On Bjroad

Metriken

IoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
CMNeXt	63.22	Delivering Arbitrary-Modal Semantic Segmentation
CMMPNe	62.85	Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction
CMX	62.28	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
SA-Gate	62.14	Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
DeepDualMapper	60.19	DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
Sun et al.	59.18	Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery
D-LinkNet	57.96	D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
LinkNet	57.89	LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
UNet	54.88	U-Net: Convolutional Networks for Biomedical Image Segmentation
Res-UNet	54.24	Road Extraction by Deep Residual U-Net
DeepLabv3+	50.81	Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

0 of 11 row(s) selected.

Semantic Segmentation On Bjroad | SOTA | HyperAI