EasyEarth enables seamless application of cutting-edge computer vision and vision-language models directly on Earth observation data — without writing code. The platform integrates with QGIS via a plugin GUI and provides server-side infrastructure for scalable model inference and management.
- Server-Side Infrastructure – Scalable backend to run AI models on geospatial data
- QGIS Plugin GUI – User-friendly interface to apply models inside QGIS
- Model Manager (in development) – Upload, version, and deploy models with ease
📽️ Watch Demo
.
├── easyearth # Server-side code for EasyEarth
│ ├── app.py # Main application entry point
│ ├── config
│ ├── controllers # Controllers for handling requests
│ ├── models # Model management and inference logic
│ ├── openapi # OpenAPI specification for the API
│ ├── static # Static files for the server
│ ├── tests # Unit tests for the server
├── easyearth_plugin # QGIS plugin for EasyEarth
│ ├── core # Core logic for the plugin
│ ├── data # Sample data for testing
│ ├── environment.yml
│ ├── launch_server_local.sh # Script to launch server locally
│ ├── plugin.py # Main plugin entry point
│ ├── requirements.txt # Python dependencies for the plugin
│ ├── resources # Resources for the plugin (icons, images, etc.)
│ └── ui # User interface files for the plugin
├── docs # Documentation for EasyEarth
│ ├── APIReference.md # API reference documentation
│ └── DeveloperGuide.md # Developer guide for contributing to EasyEarth
├── docker-compose.yml # Docker Compose configuration for EasyEarth
├── Dockerfile # Dockerfile for building the EasyEarth server image
├── environment.yml # Conda environment file for EasyEarth
├── launch_server_local.sh # Script to launch the EasyEarth server locally
├── README.md
├── requirements_mac.txt # Python dependencies for macOS
├── requirements.txt # Python dependencies for EasyEarth
├── setup.sh # Script to set up the dockerized EasyEarth server (only needed for building the image from the start)
- Docker Compose ≥ 1.21.2 (install guide)
- Python ≥ 3.6
- CUDA ≥ 12.4 (download)
⚠️ CUDA is only required for GPU inference. CPU-only mode is also available (though slower) - (optional) QGIS > = 3.22 (tested with 3.38 and 3.40)
⚠️ to use the plugin on QGIS, otherwise, one can use the server side only
Currently tested on:
✅ Ubuntu
✅ macOS
We have not yet tested EasyEarth on Windows. If you encounter any issues or would like to help us add Windows support, contributions are welcome!
# go to your download directory
cd ~/Downloads # Specify your own path where you want to download the code
git clone https://github.com/YanCheng-go/easyearth.gitYou can also download the latest release (.zip) directly from the Releases Page.
- Open QGIS >
Settings>User Profiles>Open Active Profile Folder - Navigate to
python/plugins - Copy
easyearth_pluginfolder into this directory - Restart QGIS >
Plugins>Manage and Install Plugins> enable EasyEarth
cd ~/Downloads/easyearth/easyearth_plugin # go to the directory where easyearth_plugin is located
cp -r ./easyearth_plugin ~/.local/share/QGIS/QGIS3/profiles/default/python/plugins # copy the easyearth_plugin folder to the plugins directoryAfter this, Restart QGIS > Plugins > Manage and Install Plugins > enable EasyEarth
- Click on the EasyEarth icon in the toolbar
- Select a project directory, some folders will be created in the project directory, the structure is as follows:
easyearth_base/images⚠️ images to be processed need to be placed hereeasyearth_base/embeddings- for storing embeddingseasyearth_base/logs- for storing logseasyearth_base/tmp- for storing temporary fileseasyearth_base/predictions- for storing predictions
- Click Docker to launch the EasyEarth server dockerized container, or Local to run the non-dockerized server
- Then you will see the Server Status as Online - Device: in the Server section
- Click Browse Image to select an image from the
easyearth_base/imagesfolder - Select a model from the dropdown menu
- Click Start Drawing to draw points or boxes on the image
⚠️ when the real time mode is checked, the prediction of each drawing prompt will be shown in real time, so no need to go step 8 - Click Predict to run the model inference
- Prediction results will be saved in the easyearth_base/tmp folder and can be moved to the easyearth_base/predictions folder as desired.
| Model Name | Description | Prompt Type | Prompt Data |
|---|---|---|---|
| SAM | Segment Anything Model | Point | [[x, y], [x, y], ...] |
| SAM | Segment Anything Model | Box | [[x1, y1, x2, y2]] |
| SAM2 | Segment Anything Model | Point | [[x, y], [x, y], ...] |
| SAM2 | Segment Anything Model | Box | [[x1, y1, x2, y2]] |
| LangSAM | Language Model | Text | ["text1", "text2"] |
| restor/tcd-segformer-mit-b2 | Semantic Segmentation | None | [] |
Check out our User Guide and Developer Guide for more.
- Developer Guide # for developers to contribute and extend EasyEarth
- API Reference # for developers to use the EasyEarth APIs
- EasyEarth server for model inference
- QGIS plugin for model application
- Dockerized server for scalable model inference
- Advanced prompt-guided segmentation
- Editing tools for segmentation
- Model Manager for uploading/updating/tracking models
- Chatbot integration for model management and reporting
- Cloud deployment templates
We welcome community contributions! If you'd like to contribute, check out:
Developed by:
Yan Cheng ([email protected]) –
🌐 Website
Ankit Kariryaa ([email protected]) –
Lucia Gordon ([email protected]) –