IEEE

SSTGCNs: Spectral–Spatial–Temporal Long-Range Dependencies Joint Feature Extraction With Graph Convolutional Networks for Adaptive Change Detection

Abstract: Hyperspectral image change detection plays an important role in ground observation tasks, making full use of the rich spectral and spatial information in the bitemporal hyperspectral to ...
GitHub

STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

There’s a Global Network of Fungi Under Your Feet. This Is the First Complete Map

A new study has succeeded in mapping, on a global scale, the fungal network that supports plant life and helps regulate our ...
IEEE

S4DR-Net: Self-Supervised Spatial–Spectral Distance Reconstruction Network for Multispectral Point Cloud Classification

Abstract: Multispectral LiDAR point clouds are valuable in remote sensing for their spatial–spectral consistency, yet their high acquisition and annotation costs pose significant challenges. To ...

Diffractive networks enable optical information transfer through random and unknown diffusers

The transmission of optical information through random scattering media is a major challenge in optics, biomedical imaging, ...