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Found 3 papers, 3 papers with code
HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images
To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data.
GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data
Towards a fair and comprehensive analysis of existing methods, the proposed benchmark consists of 1) a large-scale dataset including co-registered RGB and nDSM pairs and pixel-wise semantic labels; 2) a comprehensive evaluation and analysis of existing multi-modal fusion strategies for both convolutional and Transformer-based networks on remote sensing data.
Ranked #1 on
Semantic Segmentation
on GAMUS
Disentangled Latent Transformer for Interpretable Monocular Height Estimation
Furthermore, a novel unsupervised semantic segmentation task based on height estimation is first introduced in this work.
