ts-raster
Raster analyzer
Extracts and analyzes time-series characteristics from raster data using Python.
Extract and analyze space-time-series characteristics from raster data.
4 stars
1 watching
0 forks
last commit: almost 4 years ago
Linked from 2 awesome lists
Related projects:
Repository | Description | Stars |
---|---|---|
hfattahi/pysar | Analyzes Interferometric Synthetic Aperture Radar time series data to estimate ground surface displacement | 46 |
shawshank-smile/pytsda-tdengine | An end-to-end time series data analysis system with integrated machine learning and deep learning capabilities | 23 |
martibosch/pylandstats | A Python library for analyzing landscape metrics and evolution | 83 |
tuw-geo/pytesmo | A Python toolbox for comparing and validating geospatial time series datasets with an initial focus on soil moisture observations. | 78 |
perrygeo/python-rasterstats | Computes summary statistics of geospatial raster datasets based on vector geometries | 531 |
microsoft/taganomaly | Anomaly detection tool for multiple time series data with interactive visualization and labeling capabilities | 323 |
rafa-rod/pytrendseries | A Python library for detecting trends in time series data and analyzing their characteristics. | 126 |
aazuspan/wxee | A Python interface between Earth Engine and xarray for processing time series data | 206 |
makepath/xarray-spatial | A Python library for efficient raster analysis and processing of spatial data | 844 |
stringertheory/traces | An unevenly-spaced time series analysis library designed to handle irregular measurement intervals and multiple series with different frequencies. | 530 |
johannfaouzi/pyts | A Python package for time series classification and analysis | 1,772 |
alkaline-ml/pmdarima | A statistical library for time series analysis and forecasting | 1,594 |
isciences/exactextract | An algorithm for efficiently calculating statistics from overlapping raster data and polygons. | 260 |
fzj-iek3-vsa/tsam | A Python package for aggregating and analyzing time series data to accelerate model runs | 148 |
jpeg729/pytorch_bits | An experimental framework for developing and testing deep learning models on time-series prediction tasks | 79 |