S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

Shen, Li; Lu, Yao; Chen, Hao; Wei, Hao; Xie, Da; Yue, Jiabao; Chen, Rui; Lv, Shouye; Jiang, Bitao

doi:10.3390/rs13245094

Cited by 80 publications

(37 citation statements)

References 66 publications

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“…Most of the existing datasets are small in scale and are constructed with near-nadir images. Only S2Looking [22] dataset contains off-nadir images, and mainly focuses on rural construction. However, urban high-rise buildings are most seriously affected by the imaging angle.…”

Section: Building Change Detection Datasetsmentioning

confidence: 99%

Detecting Building Changes with Off-Nadir Aerial Images

Pang¹,

Wu²,

Ding³

et al. 2023

Preprint

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The tilted viewing nature of the off-nadir aerial images brings severe challenges to the building change detection (BCD) problem: the mismatch of the nearby buildings and the semantic ambiguity of the building facades. To tackle these challenges, we present a multi-task guided change detection network model, named as MTGCD-Net. The proposed model approaches the specific BCD problem by designing three auxiliary tasks, including: (1) a pixel-wise classification task to predict the roofs and facades of buildings; (2) an auxiliary task for learning the roof-to-footprint offsets of each building to account for the misalignment between building roof instances; and (3) an auxiliary task for learning the identical roof matching flow between bi-temporal aerial images to tackle the building roof mismatch problem. These auxiliary tasks provide indispensable and complementary building parsing and matching information. The predictions of the auxiliary tasks are finally fused to the main building change detection branch with a multi-modal distillation module. To train and test models for the BCD problem with off-nadir aerial images, we create a new benchmark dataset, named BANDON * . Extensive experiments demonstrate that our model achieves superior performance over the previous state-of-the-art competitors.

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Section: Building Change Detection Datasetsmentioning

confidence: 99%

Detecting Building Changes with Off-Nadir Aerial Images

Pang¹,

Wu²,

Ding³

et al. 2023

Preprint

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show abstract

“…S2Looking. S2Looking dataset [31] contains 5000 image pairs (1024 × 1024, 7:1:2 for train, eval and test) and more than 65,920 annotated instances of changes extracted from side-looking rural area satellite images, which were collected by optical satellites around the world. The images span 1-3 years with a resolution of 0.5-0.8 m/pixel.…”

Section: Datasetmentioning

confidence: 99%

“…Specifically, change detection is a pixel-to-pixel task, which takes bi-temporal images as input and predicts "where" the change has occurred. Driven by large amounts of RS data [31,35,36], change detection models based on ConvNet or Vision Transformer achieve more competitive MetaChanger is presented as a general architecture with alternative interaction layer (e.g. AD and feature "exchange" in this paper) and fusion layer (e.g.…”

Section: Introductionmentioning

confidence: 99%

Changer: Feature Interaction is What You Need for Change Detection

Fang¹,

Li²,

Li³

2022

Preprint

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Change detection is an important tool for long-term earth observation missions. It takes bi-temporal images as input and predicts "where" the change has occurred. Different from other dense prediction tasks, a meaningful consideration for change detection is the interaction between bi-temporal features. With this motivation, in this paper we propose a novel general change detection architecture, MetaChanger, which includes a series of alternative interaction layers in the feature extractor. To verify the effectiveness of MetaChanger, we propose two derived models, ChangerAD and ChangerEx with simple interaction strategies: Aggregation-Distribution (AD) and "exchange". AD is abstracted from some complex interaction methods, and "exchange" is a completely parameter&computation-free operation by exchanging bi-temporal features. In addition, for better alignment of bi-temporal features, we propose a flow dual-alignment fusion (FDAF) module which allows interactive alignment and feature fusion. Crucially, we observe Changer series models achieve competitive performance on different scale change detection datasets. Further, our proposed ChangerAD and ChangerEx could serve as a starting baseline for future MetaChanger design.

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“…The S2Looking [18] dataset is a building change detection dataset consisting of 5,000 1024x1024 0.5-0.8 m resolution image pairs taken by the Gaofen, Su-perView, and BeiJing-2 satellites. The dataset contains imagery with significantly off-nadir angles of rural locations along with labels for both newly built and demolished buildings.…”

Section: S2lookingmentioning

confidence: 99%

Supervising Remote Sensing Change Detection Models with 3D Surface Semantics

Corley¹,

Najafirad²

2022

Preprint

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Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications. Recent advances in multimodal self-supervised pretraining have resulted in state-of-the-art methods which surpass vision models trained solely on optical imagery. In the remote sensing field, there is a wealth of overlapping 2D and 3D modalities which can be exploited to supervise representation learning in vision models. In this paper we propose Contrastive Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above ground level (AGL) map pairs. We then evaluate these pretrained models on several building segmentation and change detection datasets to show that our method does, in fact, extract features relevant to downstream applications where natural and artificial surface information is relevant. 1

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S2Looking: A Satellite Side-Looking Dataset for Building Change Detection

Cited by 80 publications

References 66 publications

Detecting Building Changes with Off-Nadir Aerial Images

Detecting Building Changes with Off-Nadir Aerial Images

Changer: Feature Interaction is What You Need for Change Detection

Supervising Remote Sensing Change Detection Models with 3D Surface Semantics

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