ICML2025 Remote Sensing AI paper notes paper summaries Multimodal/VLM Reasoning Self-Supervised Learning Time-Series Forecasting

🛰️ Remote Sensing¶

🧪 ICML2025 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (63) · 🔬 ICLR2026 (11) · 🧪 ICML2026 (3) · 🤖 AAAI2026 (7) · 🧠 NeurIPS2025 (12) · 📹 ICCV2025 (11)

Causal Foundation Models: Disentangling Physics from Instrument Properties: Introduces a causally-driven foundation model that disentangles physical signals and instrumental effects from astronomical time series using a dual-encoder architecture and structured contrastive learning. By leveraging naturally occurring observational triplets (the same target observed by different instruments, or different targets observed by the same instrument), the proposed model significantly outperforms single latent space approaches in low-data regimes.
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts: ExPLoRA is proposed to continue self-supervised pre-training on a target domain in a parameter-efficient manner by unfreezing 1-2 ViT blocks and applying LoRA to the remaining layers. Under domain shift scenarios like remote sensing, it outperforms SOTAs that undergo full pre-training from scratch, while utilizing <10% of the parameters.
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data: Fine-tuning the pretrained multimodal Earth observation model Galileo generates 10-meter resolution Live Fuel Moisture Content (LFMC) maps, reducing RMSE by 20%+ compared to randomly initialized models, with the pipeline's utility validated by a 2025 Los Angeles wildfire case study.
LIGHTHOUSE: Fast and Precise Distance to Shoreline Calculations from Anywhere on Earth: This work introduces Lighthouse, a global shoreline dataset with a 10-meter resolution and a millisecond-level query library. By fusing ESA WorldCover and OpenStreetMap data, and combining a hierarchical BallTree with spherical Voronoi indexing, it enables real-time shoreline distance queries requiring only 1 CPU and 2GB RAM, improving accuracy by over 100 times compared to existing datasets.
MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models: This paper proposes the MapEval benchmark, which systematically evaluates the geo-spatial reasoning capabilities of 30 foundation models in map scenarios using 700 multiple-choice questions across textual, API, and visual tasks. The results show that the strongest model achieves an accuracy of no more than 67%, and all models lag behind human performance by over 20%.
Neural Augmented Kalman Filters for Road Network Assisted GNSS Positioning: A Temporal Graph Neural Network (TGNN) is proposed to integrate open-source road network information into GNSS Kalman filtering. The TGNN predicts the most likely road segments on the graph structure and dynamically estimates their uncertainties, reducing the P95 localization error from 77.23m to 55.02m (a 29% reduction) in real-world urban data.
Resampling Augmentation for Time Series Contrastive Learning: Application to Remote Sensing: The paper proposes a resampling augmentation for time series contrastive learning, which constructs positive pairs through "upsampling + disjoint subsequence extraction + realigning back to the original timeline." This approach outperforms common augmentation strategies on multiple SITS agricultural classification tasks and yields leading results on S2-Agri100.