Introduction ================= A PYthon library for basic and eXtended COntinuous Change Detection algorithms ------------------------------------------------------------------------------ The Continuous Change Detection and Claasification (CCDC) algorithm has been popular for processing satellite-based time series datasets, particularly for Landsat-based datasets. As a CCDC user, you may already be familiar with the existing CCDC tools such as `pyccd `_ and `gee ccdc `_. **Wait.. so why does the pyxccd package still exist?** We developed pyxccd mainly for the below purposes: 1. **Near real-time monitoring**: Implements the unique S-CCD algorithm, which recursively updates model coefficients and enables timely change detection. 2. **Latest CCDC (COLD)**: Integrates the advanced COLD algorithm, offering the highest retrospective breakpoint detection accuracy to date, validated against `Zhe's MATLAB version `_. 3. **Efficient Large-scale time-series processing**: The core of pyxccd is written in C language, ensuring high computational efficiency and low memory usage in the desktop as well as HPC environments. 4. **Flexible multi-sensor support**: Supports arbitrary band combinations from diverse sensors (e.g., Sentinel-2, MODIS, GOSIF, and SMAP) in addition to Landsat. 5. **State-space model incoporation**: S-CCD allows modeling trend and seasonal signals as time-varying variables (namely “states”) guided by break detection, enabling (a) characterization of subtle inter-segment variations (e.g., phenological shifts) and (b) gap filling that accounts for land cover conversions (temporal breaks).