Scalable Multi-Temporal Remote Sensing Change Data Generation via
  Simulating Stochastic Change Process
Zhuo Zheng Shiqi Tian Ailong Ma Liangpei Zhang Yanfei Zhong
Understanding the temporal dynamics of Earth's surface is a mission of
multi-temporal remote sensing image analysis, significantly promoted by deep
vision models with its fuel -- labeled multi-temporal images. However,
collecting, preprocessing, and annotating multi-temporal remote sensing images
at scale is non-trivial since it is expensive and knowledge-intensive. In this
paper, we present a scalable multi-temporal remote sensing change data
generator via generative modeling, which is cheap and automatic, alleviating
these problems. Our main idea is to simulate a stochastic change process over
time. We consider the stochastic change process as a probabilistic semantic
state transition, namely generative probabilistic change model (GPCM), which
decouples the complex simulation problem into two more trackable sub-problems,
\ie, change event simulation and semantic change synthesis. To solve these two
problems, we present the change generator (Changen), a GAN-based GPCM, enabling
controllable object change data generation, including customizable object
property, and change event. The extensive experiments suggest that our Changen
has superior generation capability, and the change detectors with Changen
pre-training exhibit excellent transferability to real-world change datasets.