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.. .. TODO: uncomment these after docs / pypi / coverage are online .. .. |ReadTheDocs| |Codecov|
The base algorithms were mostly written using C wrapped in Python, and
have been verified with MATLAB version <https://github.com/GERSL/COLD>
. The C codes of the package were partially modified from C-CCDC <https://github.com/repository-preservation/lcmap-change-detection-c>
developed by USGS.
This library provides:
Original COntinuous monitoring of Land Disturbance (COLD): a upgraded CCDC algorithm proposed by Dr.Zhe Zhu for offline satellite-based time-series analysis
Stochastic Continuous Change Detection (S-CCD, a near real-time and short-memory implementation of COLD)
Object-based COLD (OB-COLD), integrating spatial information into COLD by using a ‘change object’ view
The recent applications of S-CCD and OB-COLD could be found in CONUS Land Watcher <https://gers.users.earthengine.app/view/nrt-conus>
_
Clone github repo to your local code directory for the first use:
.. code:: bash
git clone https://github.com/GERSL/pycold.git
Or you call pull the recent repo if you want to update the existing pycold repo:
.. code:: bash
git pull origin devel:devel
The steps to install this library in development mode are consolidated
into a single script: run_developer_setup.sh
. On debian-based systems,
this will install all of the developer requirements and ensure you are setup
with a working opencv-python-headless and gdal Python modules, as well as other
requirements and then it will compile and install pycold in editable
development mode.
The following is an overview of these details and alternative choices that could be made.
2.1 Install Required Libraries
The ZLIB, GSL libraries are required.
For Ubuntu/Debian systems, they can be installed via:
.. code:: bash
sudo apt-get update
sudo apt-get install build-essential -y
sudo apt-get install zlib1g-dev -y
sudo apt-get install gfortran -y
sudo apt-get install libgsl-dev -y
On CentOS systems run:
.. code:: bash
sudo apt-get install gcc gcc-c++ make -y
sudo apt-get install zlib-devel -y
sudo apt-get install gcc-gfortran -y
# Yum provides an gsl 1.5, but we need 2.7
# sudo apt-get install gsl-devel -y
curl https://ftp.gnu.org/gnu/gsl/gsl-2.7.1.tar.gz > gsl.tar.gz && tar xfv gsl.tar.gz && cd gsl-2.7.1 && ./configure --prefix=/usr --disable-static && make && make install
2.2 Compile and Install PYCOLD
The following instructure assume you are inside a Python virtual environment (e.g. via conda or pyenv).
.. code:: bash
# Install required packages
pip install -r requirements.txt
Note that in all cases gdal will need to be manually installed. The following
step will install GDAL from a custom pypi server <https://girder.github.io/large_image_wheels>
_ containing precompiled wheels.
.. code:: bash
# Install GDAL (note-this must be done manually)
pip install -r requirements/gdal.txt
Additionally, to access the cv2
module, pycold will require either
opencv-python
or opencv-python-headless
, which are mutually exclusive.
This is exposed as optional dependencies in the package via either "graphics"
or "headless" extras. Headless mode is recommended as it is more compatible
with other libraries. These can be obtained manually via:
.. code:: bash
pip install -r requirements/headless.txt
# XOR (choose only one!)
pip install -r requirements/graphics.txt
Option 1: Install in development mode
For details on installing in development mode see the
developer install instructions <docs/source/developer_install.rst>
_.
We note that all steps in the above document and other minor details are
consolidated in the run_developer_setup.sh
script.
Option 2: Build and install a wheel
Scikit-build will invoke CMake and build everything. (you may need to
remove any existing _skbuild
directory).
.. code:: bash
python -m build --wheel .
Then you can pip install the wheel (the exact path will depend on your system and version of python).
.. code:: bash
pip install dist/pycold-0.1.0-cp38-cp38-linux_x86_64.whl
You can also use the build_wheels.sh
script to invoke cibuildwheel to
produce portable wheels that can be installed on different than they were built
on. You must have docker and cibuildwheel installed to use this.
Option 3: build standalone binaries with CMake by itself (recommended for C development)
.. code:: bash
mkdir -p build cd build cmake .. make
Option 4: Use a docker image.
This repo provides dockerfiles that illustrate a reproduceable method for
compling and installing PYCOLD. See dockerfiles/README.rst <dockerfiles/README.rst>
__ for details.
jupyter examples <tool/notebook/pycold_example.ipynb>
_)COLD:
.. code:: python
from pycold import cold_detect cold_result = cold_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)
COLD algorithm for any combination of band inputs from any sensor:
.. code:: python
from pycold import cold_detect
cold_result = cold_detect_flex(dates, np.stack((band1, band2, band3), axis=1), qas, tmask_b1=1, tmask_b2=2)
S-CCD:
.. code:: python
from pycold import sccd_detect, sccd_update sccd_pack = sccd_detect(dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)
sccd_pack_new = sccd_update(sccd_pack, dates, blues, greens, reds, nirs, swir1s, swir2s, thermals, qas)
Q&A
Q1: Has pycold been verified with original Matlab codes?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Re: yes, multiple rounds of verification have been done. Comparison
based on two testing tiles shows that pycold and Matlab version have
smaller than <2% differences for breakpoint detection and <2%
differences for harmonic coefficients; the accuracy of pycold was also
tested against the same reference dataset used in the original COLD
paper (Zhu et al., 2020), and pycold reached the same accuracy (27%
omission and 28% commission) showing that the discrepancy doesn’t hurt
accuracy. The primary source for the discrepancy is mainly from the
rounding: MATLAB uses float64 precision, while pycold chose float32 to
save the run-time computing memory and boost efficiency.
Q2: how much time for production of a tile-based disturbance map (5000*5000 pixels) using pycold?
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Re: I tested it in UCONN HPC environment (200 EPYC7452 cores): for
processing a 40-year Landsat ARD tile (1982-2021), the stacking
typically takes 15 mins; per-pixel COLD processing costs averagely 1
hour; exporting maps needs 7 mins.
4. Citations
------------
If you make use of the algorithms in this repo (or to read more about them),
please cite (/see) the relevant publications from the following list:
`[COLD] <https://www.sciencedirect.com/science/article/am/pii/S0034425719301002>`_
Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., &
Zhou, C. (2020). Continuous monitoring of land disturbance based on
Landsat time series. *Remote Sensing of Environment*, *238*, 111116.
`[S-CCD] <https://www.sciencedirect.com/science/article/pii/S003442572030540X>`_
Ye, S., Rogan, J., Zhu, Z., & Eastman, J. R. (2021). A near-real-time
approach for monitoring forest disturbance using Landsat time series:
Stochastic continuous change detection. *Remote Sensing of Environment*,
*252*, 112167.
`[OB-COLD] <https://www.sciencedirect.com/science/article/pii/S0034425723000135>`_
Ye, S., Zhu, Z., & Cao, G., (2022). Object-based continuous monitoring
of land disturbance from dense Landsat time series. *Remote Sensing of Environment*, *287*, 113462.
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