Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection

• URL: http://arxiv.org/abs/2604.11231v1
• Date: Mon, 13 Apr 2026 09:35:14 GMT
• Title: Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection
• Abstract summary: Existing change detection methods are limited to predefined classes in training datasets.<n>We propose Seg2Change, an adapter designed to adapt open-vocabulary semantic segmentation models to change detection task.<n>Our framework achieves state-of-the-art OVCD performance.
• Score: 5.3514684920696824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection is a fundamental task in remote sensing, aiming to quantify the impacts of human activities and ecological dynamics on land-cover changes. Existing change detection methods are limited to predefined classes in training datasets, which constrains their scalability in real-world scenarios. In recent years, numerous advanced open-vocabulary semantic segmentation models have emerged for remote sensing imagery. However, there is still a lack of an effective framework for directly applying these models to open-vocabulary change detection (OVCD), a novel task that integrates vision and language to detect changes across arbitrary categories. To address these challenges, we first construct a category-agnostic change detection dataset, termed CA-CDD. Further, we design a category-agnostic change head to detect the transitions of arbitrary categories and index them to specific classes. Based on them, we propose Seg2Change, an adapter designed to adapt open-vocabulary semantic segmentation models to change detection task. Without bells and whistles, this simple yet effective framework achieves state-of-the-art OVCD performance (+9.52 IoU on WHU-CD and +5.50 mIoU on SECOND). Our code is released at https://github.com/yogurts-sy/Seg2Change.

Related papers

• Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations [0.0]
  Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training.<n>We propose MaSoN, an end-to-end UCD framework that synthesises diverse changes directly in the latent feature space during training.<n>It generates changes that are dynamically estimated using feature statistics of target data, enabling diverse yet data-driven variation aligned with the target domain.
  arXiv  Detail & Related papers  (2026-02-23T14:27:36Z)
• LAB-Det: Language as a Domain-Invariant Bridge for Training-Free One-Shot Domain Generalization in Object Detection [53.988759250627425]
  We introduce training-free one-shot domain generalization for object detection.<n>We project each exemplar into a descriptive text that conditions and guides a frozen detector.<n>We evaluate on UODD (underwater) and NEU-DET (industrial defects)
  arXiv  Detail & Related papers  (2026-02-06T08:03:04Z)
• Referring Change Detection in Remote Sensing Imagery [49.841833753558575]
  We introduce Referring Change Detection (RCD), which leverages natural language prompts to detect specific classes of changes in remote sensing images.<n>We propose a two-stage framework consisting of (I) textbfRCDNet, a cross-modal fusion network designed for referring change detection, and (II) textbfRCDGen, a diffusion-based synthetic data generation pipeline.
  arXiv  Detail & Related papers  (2025-12-12T16:57:12Z)
• Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting [49.15470825004932]
  We propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem.<n>Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost.
  arXiv  Detail & Related papers  (2025-08-24T02:02:16Z)
• SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective [0.6749750044497732]
  We develop a fine-tuning strategy called the Semantic Change Network (SCN) to address the data scarcity issue.<n>We observe that the locations of changes between the two images are spatially identical, a concept we refer to as spatial consistency.<n>This enhances the modeling of multi-scale changes and helps capture underlying relationships in change detection semantics.
  arXiv  Detail & Related papers  (2025-03-26T17:15:43Z)
• Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection [52.62459671461816]
  This paper explores incorporating semantic priors from visual foundation models to improve the ability to detect changes.<n>Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features.
  arXiv  Detail & Related papers  (2024-12-22T08:27:15Z)
• Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection [82.65760006883248]
  We introduce a new task named Change Detection Question Answering and Grounding (CDQAG) CDQAG extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. We construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks.
  arXiv  Detail & Related papers  (2024-10-31T11:20:13Z)
• Segment Any Change [64.23961453159454]
  We propose a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions.<n>AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching.<n>We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability.
  arXiv  Detail & Related papers  (2024-02-02T07:17:39Z)
• Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization [10.059696915598392]
  We propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets.
  arXiv  Detail & Related papers  (2023-11-19T11:30:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.