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Hugging Face Trending Papers

HyperFlow: Class-Incremental Semantic Change Detection via Hyperbolic Flow

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.

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arXiv Computer Vision
Sep 24

From Change Captions to Change Detection: Semantic-Appearance Agreement Framework for Remote Sensing Change Detection

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
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arXiv AI
Jun 26

On-board Remote-Sensing Foundation Models for Unsupervised Change Detection of Disaster Events

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
diffusion
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arXiv AI
Aug 26

Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

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.

By Yaoyi Qi, Xingxing Weng, Chao Pang, Yongkang Cui, Xiangyu Hao, Xiaokang Zhang, Guibo Zhu, Gui-Song Xia
llmsmultimodal
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arXiv AI
Sep 2

Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations

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
diffusionbenchmarks
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