arXiv:2606. 27018v1 Announce Type: cross Abstract: Remote Sensing Foundation Models (RSFMs) have emerged as a powerful alternative to supervised models for Earth Observation, allowing satellites to autonomously trigger high-resolution captures or adjust tasking parameters upon detecting an anomaly, thereby maximizing the utility of the mission's limited power and computational resources.
By S. Ram\'irez-Gallego
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
The paper introduces MaSoN, an end-to-end unsupervised remote sensing change detection framework that synthesises diverse changes directly in latent feature space during training. By generating changes based on feature statistics of the target data, MaSoN produces data‑driven variations that align with the target domain and can be applied to new modalities such as SAR and multispectral imagery. The method achieves a 14.1 percentage point improvement in average F1 score across five benchmarks, demonstrating strong generalisation across diverse change types.
By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc
The paper proposes a weakly supervised remote sensing change detection method that uses change captions as the sole supervision signal, eliminating the need for pixel‑level change masks. It introduces a caption‑driven generation pipeline to create bi‑temporal image pairs with controlled changes and a Semantic‑Appearance Agreement Framework (SAAF) that fuses caption‑grounded semantic responses with RGB differences for accurate change localization. Experiments on the Flair‑RSGen and WHU‑CDC datasets demonstrate that SAAF outperforms existing limited‑supervision baselines in macro‑averaged IoU and F1 metrics.
By Yuan Qian, Jie Ma
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.
arXiv:2611.05570v1 Announce Type: new
Abstract: Remote sensing change detection is typically formulated as Siamese feature comparison, where two images are encoded independently and a decoder determi...
By Buddhi Wijenayake, Dhananjaya Jayasundara, Vishal M. Patel
arXiv:2610.10181v1 Announce Type: new
Abstract: Robots operating in dynamic environments require reliable detection of how their surroundings change over time. Existing learning-based methods largely...
By Ruihan Xu, Jiae Yoon, Kaichen Zhou, Ue-Hwan Kim, Luca Carlone
arXiv:2610.27793v1 Announce Type: cross
Abstract: Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly due to imperfect land surface representations i...
By Amirpasha Mozaffari, Marina Casta\~no, Stefano Materia, Joan Vedr\'i, Jordi Varela-Agrelo, Dario Garcia-Gasulla, Etienne Tourigny, Mario Acosta, Amanda Duarte
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
By Atiq Ur Rehman, Joseph Michael Donovan
arXiv:2609.37485v1 Announce Type: cross
Abstract: Bi-temporal change understanding, which localizes and characterizes what changed between two satellite images, is central to disaster response and en...
By Haruki Watase, Shunya Nagashima, Takayuki Nishimura
SIMPLER is a pre‑fine‑tuning method that reduces inference and deployment costs for Earth Observation foundation models by pruning redundant layers. It uses layer‑wise representation similarity on unlabeled task data to identify and remove up to 79% of parameters without requiring gradients, magnitude heuristics, or hyperparameter tuning. Experiments on Prithvi‑EO‑2, TerraMind, and ImageNet‑pretrained ViT‑MAE show that SIMPLER retains 94% of baseline performance while achieving 2.1× faster training and 2.6× faster inference.
By V\'ictor Barreiro, Johannes Jakubik, Francisco Arg\"uello, Dora B. Heras
HyperFlow is a method for class‑incremental semantic change detection in remote sensing, allowing a model to progressively learn new types of land‑cover changes. It leverages the hierarchical nature of land‑cover semantics by embedding categories in hyperbolic space and uses Busemann prototypes to model change as an anomaly in a learned semantic flow. The authors also introduce HiChange, a large‑scale dataset with fine‑grained hierarchical labels, and show that incremental refinement of categories improves both change detection and segmentation performance.