giswqs/geemap
Geemap
geemap is a Python library and toolkit for interactive geospatial analysis, visualization, and satellite imagery analysis using Google Earth Engine data and cloud computing. It provides a mapping tool for displaying geospatial datasets within Jupyter notebooks and a suite of tools for classifying imagery and calculating zonal statistics.
The project includes a utility to convert geospatial analysis scripts from JavaScript into Python code to facilitate data manipulation. It also enables the generation of timelapse animations and time-series visualizations from satellite imagery catalogs.
The library covers broad geospatial capabilities including large-scale spatial calculations, imagery classification, and geospatial data visualization. It supports data management tasks such as exporting geospatial layers to common formats, extracting image pixels into numpy arrays, and integrating local shapefiles with cloud-based analysis.
Features
- Geospatial Visualizations - Provides an interactive mapping tool for displaying satellite imagery and geospatial datasets within Jupyter notebooks.
- Earth Engine Client Libraries - Provides a programmatic Python interface for analyzing planetary-scale geospatial datasets on the Google Earth Engine platform.
- Image Classification - Identifies objects within satellite imagery and evaluates the classification results using standard accuracy metrics.
- Satellite Imagery Land Use Detection - Identifies land cover and objects within satellite images and calculates the accuracy of classification results.
- Remote Sensing and Imagery - Offers a toolkit for classifying satellite imagery, calculating zonal statistics, and generating timelapse animations.
- Cloud-Based Geospatial Computation - Provides the ability to execute heavy geospatial computations on remote server clusters to avoid local memory limits.
- Geospatial Analysis - Provides tools for performing complex, large-scale geospatial analysis and spatial calculations using cloud-based Earth observation data.
- Animated Visualizations - Generates animated GIFs and time-series visualizations from satellite imagery catalogs to illustrate geographic changes over time.
- Geospatial Data Exports - Exports geospatial layers and collections into formats such as GeoTIFF, shapefiles, CSV, and KML.
- Geospatial Search - Locates specific geographic places and datasets within a cloud-based geospatial data catalog using search queries.
- Geospatial - Retrieves pixel values from imagery into numpy arrays or tabular formats based on interactive map selections.
- Shapefile Importers - Loads local shapefiles for use in cloud-based geospatial analysis without requiring manual uploads.
- Geospatial Workflows - Integrates JavaScript-to-Python script conversion and local shapefile handling into cloud-based analysis workflows.
- Geospatial Script Translators - Converts geospatial analysis scripts from JavaScript into Python code and Jupyter notebooks for better data manipulation.
- Notebook Map Embeds - Renders interactive maps directly inside Jupyter notebook cells for geospatial data exploration.
- Python-JavaScript Bridges - Maps Python function calls to JavaScript strings for execution within the remote cloud geospatial engine.
- NumPy Array Integration - Converts remote pixel arrays into local NumPy arrays for immediate statistical analysis in Python.
- Zonal Statistics - Implements tools for calculating statistical summaries of raster data aggregated by defined geographic zones.
- Data Visualization - Interactive mapping with Google Earth Engine.
- Geospatial Data Services - Enables interactive mapping and analysis in Python.
- Python Geospatial Libraries - Enables interactive mapping with Google Earth Engine.
- Visualization and Mapping - Enables interactive mapping with cloud-based platforms.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
