arXiv:2606. 05999v1 Announce Type: cross Abstract: Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images.
By Yang Wu, Ye Deng, Pengna Li, Wenli Huang, Kangyi Wu, Xiaomeng Xin, Jinjun Wang
The paper introduces OrbitALIF, a federated learning framework that performs cloud removal on low‑earth‑orbit satellites. It uses a compact 2.30 M‑parameter spiking neural network with adaptive gated fusion and spectral‑spatial hybrid attention modules, enabling both training and inference onboard. The approach achieves competitive cloud‑removal quality while consuming only 0.287 mJ per inference on neuromorphic hardware, a 72.3‑fold energy reduction compared to an equivalent ANN.
By Bohan Zhang, Chenyu Xu, Yijie Mao, Yuanming Shi
arXiv:2609.37809v1 Announce Type: cross
Abstract: Predicting canopy height from medium-resolution satellite imagery is a common and scalable approach for assessing the condition of the world's forest...
By Sven Ligensa, Jan Pauls, Karsten Schr\"odter, Ibrahim Fayad, Fabian Gieseke
arXiv:2609.38165v1 Announce Type: cross
Abstract: The landscape of satellite imagery time series datasets and boundary-pushing architectures for cropland segmentation has never been richer. However,...
By Joseph Metcalfe, Sara Sharifzadeh, Fabio Caraffini
Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstruction, stereo mapping, and visual localization. While recent detector-free matching methods, like LoFTR, have advanced the field, the global features obtained by leveraging the global-range modeling capacity of the unconstrained attention mechanism compromise the model's attention to the salient structures in certain scenarios.
arXiv:2608.03423v2 Announce Type: replace
Abstract: Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstru...
By Zhihua Xu, Runyu Zhu, Rongjun Qin