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https://github.com/CosmiQ/CometTS
Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons
https://github.com/CosmiQ/CometTS
Last synced: about 1 month ago
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Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons
- Host: GitHub
- URL: https://github.com/CosmiQ/CometTS
- Owner: CosmiQ
- License: apache-2.0
- Created: 2018-01-12T15:38:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-23T17:09:05.000Z (over 4 years ago)
- Last Synced: 2024-05-08T15:52:26.288Z (about 2 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 15.8 MB
- Stars: 63
- Watchers: 8
- Forks: 16
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.MD
- License: LICENSE
Lists
- awesome-earthobservation-code - CometTS - Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons (`Python` processing of optical imagery (non deep learning) / Processing imagery - post processing)
- Awesome-Geospatial - CometTS - Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons. (Visualization)
README
Comet Time Series (CometTS) Visualizer
- [Installation Instructions](#installation-instructions)
- [Dependencies](#dependencies)
- [License](#license)
---
## Base Functionality
Comet Time Series (``CometTS``) is an open source tool coded in python including jupyter notebooks and command line utility that enables users to visualize or extract relevant statistics from [almost any](https://gdal.org/drivers/raster/index.html) format time series of overhead imagery within a specific region of interest (ROI). To use ``CometTS``, you must define your ROI, provide a CSV file documenting how your imagery is organized, and then run one of the ``CometTS`` analysis tools. This usually takes the following steps
1. Outline and download your ROI with a service like [geojson.io](www.geojson.io)
2. Organize your imagery and document it with the [CometTS.CSV_It tool](https://github.com/CosmiQ/CometTS/blob/master/CometTS/CSV_It.py)
3. Analyze your data using:
- [CometTS](https://github.com/CosmiQ/CometTS/blob/master/CometTS/CometTS.py) for trend analysis (optionally, mask unwanted clouds and other features with [`--maskit` option](https://github.com/CosmiQ/CometTS/search?l=Python&q=--maskit))
- or [CometTS.ARIMA](https://github.com/CosmiQ/CometTS/blob/master/CometTS/ARIMA.py) for averaging and anomaly detection
4. Plot the results using [the plotting notebook](Notebooks/Plot_Results.ipynb)
A full walkthrough of this functionality with example data is included in two notebooks: [CSV_Creator](Notebooks/CSV_Creator.ipynb) and [CometTS_Visualizer](Notebooks/CometTS_Visualizer.ipynb)
### File Formats:
[Supported Raster Formats](https://gdal.org/drivers/raster/index.html)
[Supported Vector Formats](https://gdal.org/drivers/vector/index.html)
## Installation
Python 2.7 or 3.6 are the base requirements plus several packages. ``CometTS`` can be installed in multiple ways including conda, pip, docker, and cloning this repository.
### Clone it
We recommend cloning to add all sample data and easier access to the jupyter notebooks that leverage our plotting functions.
```
git clone https://github.com/CosmiQ/CometTS.git
```
If you would like the full functionality of a python package we have several options.
### pip
```
pip install CometTS
```
pip installs may fail on macs with python3 as GDAL is finicky. Use some of the alternative approaches below.
### Docker
```
docker pull jss5102/cometts
docker run -it -v /nfs:/nfs --name cometts jss5102/cometts /bin/bash
```
### Conda
Create a conda environment!
```
git clone https://github.com/CosmiQ/CometTS.git
cd CometTS
conda env create -f environment.yml
source activate CometTS
pip install CometTS
```
### Dependencies
All dependencies can be found in the docker file [Dockerfile](./Dockerfile) or
[environment.yml](./environment.yml) or [requirements.txt](./requirements.txt).
## Examples
#### Agadez, Niger
Seasonal variation in brightness that likely indicates seasonal migrations and population fluctuations in central Niger, Africa.
#### Suruc Refugee Camp, Turkey
Increase in brightness coinciding with the establishment of a refugee camp in southern Turkey, north of Syria.
#### Allepo, Syria
Brightness declines (i.e., putative population decline) as a result of Syrian Civil War and military actions in Aleppo.
#### NDVI Visualization north of Houston, Texas
A visualization of the Normalized Difference Vegetation Index (NDVI) in a field north of Houston using a time-series of Landsat imagery.
#### Landsat Multispectral Visualization
A visualization of three Landsat bands in New Orleans, Louisiana. Note the effects of Katrina in 2005.
## Contribute or debug?
Interested in proposing a change, fixing a bug, or asking for help? Check out the [contributions](https://github.com/CosmiQ/CometTS/blob/master/CONTRIBUTING.MD) guidance.
## License
See [LICENSE](./LICENSE).
## Traffic
