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.
arXiv:2605. 15375v2 Announce Type: replace-cross Abstract: Remote sensing change detection (RSCD) localises changes between two images of the same geographic region.
The paper introduces KnowChange, a framework that uses pretrained vision‑language models to guide the synthesis of change data for remote sensing. By reasoning about plausible change locations and class transitions, KnowChange flexibly generates diverse change types within a unified pipeline. Experiments show that data produced by KnowChange outperforms existing synthetic datasets in both synthetic‑to‑real transfer and data augmentation scenarios, even at a compact scale.
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...
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.
arXiv:2602.13780v4 Announce Type: replace Abstract: Remote sensing (RS) change detection is essential for interpreting surface dynamics. Semantic change detection (SCD) further enables pixel-level un...
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: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.
arXiv:2608.28247v1 Announce Type: new Abstract: Change detection in Earth observation (EO) is critical for monitoring land surface transformations, yet recent research in the field is constrained by...
arXiv:2608.21754v1 Announce Type: new Abstract: Despite the success of data pruning (DP) in reducing training data sizes and improving downstream model performance in classification and segmentation...
arXiv:2606. 27410v1 Announce Type: cross Abstract: The primary goal of Remote Sensing Image Change Captioning (RSICC) is to automatically generate descriptions of changes between remote sensing images captured at different time points.
arXiv:2607.25815v2 Announce Type: replace Abstract: Remote sensing change detection identifies land-cover changes from bi-temporal images. Most existing methods follow a one-shot dense prediction par...
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.