ASPECT: Adaptive spatio-temporal prediction of evolving crack traces

Automated crack analysis remains largely reactive: most methods delineate damage only after it has been observed, whereas maintenance planning would benefit from evidence about how visible crack patterns may evolve between inspections. Forecasting future crack traces is difficult because crack pixels are sparse and thin, small discontinuities can change structural interpretation, and uncertainty increases with prediction horizon. We introduce the Crack Predictor benchmark and ASPECT (Adaptive Spatio-temporal Prediction of Evolving Crack Traces), a non-autoregressive framework that predicts four future crack masks directly from 16 observed RGB frames. ASPECT employs a compact StarNet spatial backbone augmented with Haar Wavelet Downsampling for frequency-aware encoding, ConvLSTM for temporal modeling on a compressed two-dimensional feature grid, and a Geometry-adaptive Decoding Head for reconstructing thin, irregular crack masks. On the validation split, ASPECT achieves 90.33% mIoU, 94.66% mF1, and 80.91% crack-class IoU with 0.32 M trainable parameters. It also ranks first in topology sensitivity and endpoint precision, recall, and F1, reaching an endpoint F1 of 66.56%. Across the four prediction horizons, ASPECT retains the highest Crack F1 among the evaluated methods. These results establish ASPECT as a compact, topology-aware framework for short-horizon crack forecasting on the controlled multi-temporal benchmark.