TY - JOUR

T1 - Zero-shot detection for InSAR-based land displacement by the deformation-prompt-based SAM method

AU - He, Yufang

AU - Chen, Bo

AU - Motagh, Mahdi

AU - Zhu, Yuyan

AU - Shao, Songdong

AU - Li, Jiaye

AU - Zhang, Bing

AU - Kaufmann, Hermann

N1 - Publisher Copyright: © 2025 The Authors

PY - 2025/2

Y1 - 2025/2

N2 - Recently, geological disasters such as land subsidence, landslides, mining-related collapse and others have been occurring more and more, posing serious threats to social and economic stability as well as human safety. InSAR (Interferometric Synthetic Aperture Radar) and advanved InSAR time-series technologies can monitor surface displacements at sub-centimeter level of accuracy, which has great and significant potential in detecting these disasters. Although several automated deformation detection methods based on CNN (Convolutional Neural Networks) have been proposed, these methods are not only time-consuming- requiring specific datasets and models- but also exhibit limited generalizability. In this study, we propose a zero-shot deformation-prompt-based SAM (Segment Anything Model) method, which does not require any pre-training operations or establishing special datasets. It initially maps the surface displacement velocity of the study area by the InSAR time-series analysis. Then the deformation-prompt-based SAM method is proposed to directly identify the exact locations and morphologies of surface deformations on the velocity maps. The method primarily extracts the rectangular boxes and points of deformation areas according to the color display characteristics of the InSAR-based land deformation map. Then these boxes and points of deformation zones serve as the prompt to the SAM, which detects deformation zones in a zero-shot manner. To validate its effectiveness, experiments on deformation extraction for various types of geohazards are conducted by utilizing Sentinel-1 and TerraSAR-X images. The results show that 137 and 93 local urban subsidence areas in Shenzhen City are identified based on different SAR images. Most of them can be automatically detected by the proposed method. Additionally, we have identified 18 landslides near Jichang Town, 36 landslides in Heifangtai and 27 major deformation zones in the Huainan mining area of Anhui Province. Statistical analysis reveals that our method can obtain high pixel accuracy in detecting land subsidence, landslides, and mining-related deformation areas with zero-shot based on different SAR images. Thus, the automatic surface deformation detection method holds considerable promise for compiling and regularly updating inventories of surface deformation data related to different types of geohazards. It has the potential to significantly enhance monitoring efficiency and provide robust technical support for disaster prevention and loss reduction.

AB - Recently, geological disasters such as land subsidence, landslides, mining-related collapse and others have been occurring more and more, posing serious threats to social and economic stability as well as human safety. InSAR (Interferometric Synthetic Aperture Radar) and advanved InSAR time-series technologies can monitor surface displacements at sub-centimeter level of accuracy, which has great and significant potential in detecting these disasters. Although several automated deformation detection methods based on CNN (Convolutional Neural Networks) have been proposed, these methods are not only time-consuming- requiring specific datasets and models- but also exhibit limited generalizability. In this study, we propose a zero-shot deformation-prompt-based SAM (Segment Anything Model) method, which does not require any pre-training operations or establishing special datasets. It initially maps the surface displacement velocity of the study area by the InSAR time-series analysis. Then the deformation-prompt-based SAM method is proposed to directly identify the exact locations and morphologies of surface deformations on the velocity maps. The method primarily extracts the rectangular boxes and points of deformation areas according to the color display characteristics of the InSAR-based land deformation map. Then these boxes and points of deformation zones serve as the prompt to the SAM, which detects deformation zones in a zero-shot manner. To validate its effectiveness, experiments on deformation extraction for various types of geohazards are conducted by utilizing Sentinel-1 and TerraSAR-X images. The results show that 137 and 93 local urban subsidence areas in Shenzhen City are identified based on different SAR images. Most of them can be automatically detected by the proposed method. Additionally, we have identified 18 landslides near Jichang Town, 36 landslides in Heifangtai and 27 major deformation zones in the Huainan mining area of Anhui Province. Statistical analysis reveals that our method can obtain high pixel accuracy in detecting land subsidence, landslides, and mining-related deformation areas with zero-shot based on different SAR images. Thus, the automatic surface deformation detection method holds considerable promise for compiling and regularly updating inventories of surface deformation data related to different types of geohazards. It has the potential to significantly enhance monitoring efficiency and provide robust technical support for disaster prevention and loss reduction.

KW - Deformation prompts

KW - InSAR

KW - Land displacement identification

KW - SAM

KW - Zero-shot

UR - http://www.scopus.com/inward/record.url?scp=85217437858&partnerID=8YFLogxK

U2 - 10.1016/j.jag.2025.104407

DO - 10.1016/j.jag.2025.104407

M3 - Article

AN - SCOPUS:85217437858

VL - 136

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 1569-8432

M1 - 104407

ER -