The Change You Want To Detect: Semantic Change Detection In Earth Observation With Hybrid Data Generation
This work addresses the challenge of limited annotated data for scalable bi-temporal change detection in Earth monitoring, offering a domain-specific solution for remote sensing applications.
The paper tackles the problem of semantic change detection in Earth observation by introducing HySCDG, a generative pipeline that creates a large hybrid dataset (FSC-180k) combining real and inpainted VHR images, which when used for pretraining significantly boosts performance, outperforming fully synthetic datasets like SyntheWorld across various configurations.
Bi-temporal change detection at scale based on Very High Resolution (VHR) images is crucial for Earth monitoring. This remains poorly addressed so far: methods either require large volumes of annotated data (semantic case), or are limited to restricted datasets (binary set-ups). Most approaches do not exhibit the versatility required for temporal and spatial adaptation: simplicity in architecture design and pretraining on realistic and comprehensive datasets. Synthetic datasets are the key solution but still fail to handle complex and diverse scenes. In this paper, we present HySCDG a generative pipeline for creating a large hybrid semantic change detection dataset that contains both real VHR images and inpainted ones, along with land cover semantic map at both dates and the change map. Being semantically and spatially guided, HySCDG generates realistic images, leading to a comprehensive and hybrid transfer-proof dataset FSC-180k. We evaluate FSC-180k on five change detection cases (binary and semantic), from zero-shot to mixed and sequential training, and also under low data regime training. Experiments demonstrate that pretraining on our hybrid dataset leads to a significant performance boost, outperforming SyntheWorld, a fully synthetic dataset, in every configuration. All codes, models, and data are available here: https://yb23.github.io/projects/cywd/