  With the widespread application of remote sensing technology in environmental
monitoring, the demand for efficient and accurate remote sensing image change
detection (CD) for natural environments is growing. We propose a novel deep
learning framework named EfficientCD, specifically designed for remote sensing
image change detection. The framework employs EfficientNet as its backbone
network for feature extraction. To enhance the information exchange between
bi-temporal image feature maps, we have designed a new Feature Pyramid Network
module targeted at remote sensing change detection, named ChangeFPN.
Additionally, to make full use of the multi-level feature maps in the decoding
stage, we have developed a layer-by-layer feature upsampling module combined
with Euclidean distance to improve feature fusion and reconstruction during the
decoding stage. The EfficientCD has been experimentally validated on four
remote sensing datasets: LEVIR-CD, SYSU-CD, CLCD, and WHUCD. The experimental
results demonstrate that EfficientCD exhibits outstanding performance in change
detection accuracy. The code and pretrained models will be released at
https://github.com/dyzy41/mmrscd.
