title: JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
    Multi-Teacher Knowledge Distillation Framework
summary: Deep learning has achieved significant success in the field of remote sensing
  image change detection (CD), yet two major challenges remain: the scarcity of
  sub-meter, all-inclusive open-source CD datasets, and the difficulty of
  achieving consistent and satisfactory detection results across images with
  varying change areas. To address these issues, we introduce the JL1-CD dataset,
  which contains 5,000 pairs of 512 x 512 pixel images with a resolution of 0.5
  to 0.75 meters. Additionally, we propose a multi-teacher knowledge distillation
  (MTKD) framework for CD. Experimental results on the JL1-CD and SYSU-CD
  datasets demonstrate that the MTKD framework significantly improves the
  performance of CD models with various network architectures and parameter
  sizes, achieving new state-of-the-art results. The code is available at
  https://github.com/circleLZY/MTKD-CD.
paper_authors._id: 67bb33f3829dedfc99ae1288
paper_authors.name: Ziyuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-24T09:07:29.223Z
paper_authors._id: 67bb32b6a0cb6e48cfd27d80
paper_authors.avatarUrl: /avatars/3cafe3a3fb60405252962d00105667c5.svg
paper_authors.fullname: Ziyuan Liu
paper_authors.type: user
paper_authors.user: circleLZY
paper_authors._id: 67bb33f3829dedfc99ae1289
paper_authors.name: Ruifei Zhu
paper_authors._id: 67bb33f3829dedfc99ae128a
paper_authors.name: Long Gao
paper_authors._id: 67bb33f3829dedfc99ae128b
paper_authors.name: Yuanxiu Zhou
paper_authors._id: 67bb33f3829dedfc99ae128c
paper_authors.name: Jingyu Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-24T16:16:21.974Z
paper_authors._id: 673a9a638127cd120c9c272d
paper_authors.avatarUrl: /avatars/d6e225d7a869487cb48c4ac89d048cb4.svg
paper_authors.fullname: Jingyu Ma
paper_authors.type: user
paper_authors.user: jingyum
paper_authors._id: 67bb33f3829dedfc99ae128d
paper_authors.name: Yuantao Gu
paper_title: JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
    Multi-Teacher Knowledge Distillation Framework
paper_summary: Deep learning has achieved significant success in the field of remote sensing
  image change detection (CD), yet two major challenges remain: the scarcity of
  sub-meter, all-inclusive open-source CD datasets, and the difficulty of
  achieving consistent and satisfactory detection results across images with
  varying change areas. To address these issues, we introduce the JL1-CD dataset,
  which contains 5,000 pairs of 512 x 512 pixel images with a resolution of 0.5
  to 0.75 meters. Additionally, we propose a multi-teacher knowledge distillation
  (MTKD) framework for CD. Experimental results on the JL1-CD and SYSU-CD
  datasets demonstrate that the MTKD framework significantly improves the
  performance of CD models with various network architectures and parameter
  sizes, achieving new state-of-the-art results. The code is available at
  https://github.com/circleLZY/MTKD-CD.
paper_ai_keywords: multi-teacher knowledge distillation (MTKD) change detection (CD) JL1-CD dataset SYSU-CD datasets network architectures