Monitoring construction changes using dense satellite time series and deep learning

Monitoring construction changes is essential for understanding the anthropogenic impacts on the environment. However, mapping construction changes at a medium scale (i.e., 30 m) using satellite time series and deep learning models presents challenges due to their large spectral variability during different phases of construction and the presence of small and isolated change targets. These challenges reduce the effectiveness of feature extraction from deep convolutional layers. To address these issues, we propose a novel Classify Areas with Potential and then Exclude the Stable pixels (hereafter called CAPES) method using a U-net model along with perpixel-based time series model information derived from the COntinuous monitoring of Land Disturbance (COLD) algorithm (Zhu et al., 2020). Our major findings are as follows: (1) the U-net with time series model information performed best when combining time series model coefficients and RMSE values extracted before and after the change (average F1 score of 70.8%); (2) the CAPES approach substantially improves the accuracy by addressing the loss of spatial information for small and isolated construction change targets in deep convolutional layers; (3) the U-net with time series model information showed better performance than other pixel-based machine learning algorithms for monitoring construction change; (4) our model can be transferred to different time periods and geographic locations with similar performance as the baseline model after fine-tuning.