arXiv AI July 1, 2026 By Xinze Zhang

Cross-Modal Hierarchical Fusion for from Multi-Sensor Ground Observation

arXiv:2606. 30647v1 Announce Type: cross Abstract: Dense volumetric reconstruction of cloud microphysical fields from sparse ground-based instruments remains an open problem, largely because the available measurements are heterogeneous in both modality and spatial coverage.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 11

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

The paper introduces SolCloudLLM, a large language model–based framework that fuses sky‑image patches with time‑series data through bidirectional multimodal fusion for short‑term solar forecasting. Experiments on the SIRTA and SKIPP'D datasets show that SolCloudLLM outperforms existing baselines, achieving up to a 25.4% reduction in mean squared error, especially under cloudy conditions and in few‑shot scenarios.

By Ken Chen, Maneesha Perera, Wei Wang, Sachith Seneviratne, Hansani Weeratunge, Saman Halgamuge

llms rag multimodal

arXiv Computer Vision

Sep 15

ESAFusion: LiDAR--4-D Radar Fusion via Local Geometric Complementation and Multiscale Adaptive Interaction for 3-D Object Detection

arXiv:2609.14619v1 Announce Type: new Abstract: LiDAR--4-D radar fusion combines accurate spatial geometry with motion and reflectivity cues from radar, offering a promising solution for 3-D object d...

By Gang Ma, Senjie Hu, Junjie Liu, Chao Wang, Hui Wei

computer-vision multimodal

arXiv Machine Learning

Sep 4

Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

The paper presents a method to replace traditional window-based tracking in geostationary atmospheric motion vector (AMV) stereo matching with deep optical flow, enabling efficient and accurate retrieval of dense three‑dimensional wind fields. A stereo teacher model is distilled into a single‑satellite student model that emulates the teacher’s uncertainty estimates, allowing global wind generation from full‑disk GEO imagery. Validation against radiosondes, operational AMVs, ERA5 reanalysis, and EarthCARE cloud profiles shows that the stereo winds outperform operational AMVs in water‑vapor bands while performing slightly worse in the long‑wave infrared band.

By Thomas J. Vandal, Dong L. Wu, James L. Carr, Derek J. Posselt, Elise Penn, Tristan Ballard, August Posch, Kate Duffy

computer-vision fine-tuning

arXiv Computer Vision

Sep 24

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

arXiv:2409.13568v3 Announce Type: replace Abstract: Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing method...

By Foivos I. Diakogiannis, Zheng-Shu Zhou, Jeff Wang, Gonzalo Mata, Dave Henry, Roger Lawes, Amy Parker, Peter Caccetta, Suzanne Furby, Rodrigo Ibata, Ondrej Hlinka, Jonathan Richetti, Kathryn Batchelor, Chris Herrmann, Andrew Toovey, John Taylor

llms computer-vision benchmarks

arXiv Machine Learning

Jun 8

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery

arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

rag multimodal benchmarks

arXiv AI

Jun 9

ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity

arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.

By Debojyoti Biswas, Xianbiao Hu

computer-vision efficiency multimodal benchmarks

One email a morning, machine-written

Related stories

Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

ESAFusion: LiDAR--4-D Radar Fusion via Local Geometric Complementation and Multiscale Adaptive Interaction for 3-D Object Detection

Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

Tackling fluffy clouds: robust agricultural field boundary delineation from Sentinel-1 and Sentinel-2 satellite image time series

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery

ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity