Create your own training datasets from satellite imagery, effortlessly and faster than ever.
SCANEO is an AI-powered web tool for smart labeling of satellite data training datasets. It can generate labels on its own, and its model can also be retrained with user-validated labels, creating a feedback loop that increases accuracy and speeds up labeling. In this way, SCANEO benefits both models and data sets simultaneously, and promotes the proliferation of artificial intelligence models applied to Earth observation data.
Why SCANEO?
The shortage of suitable and accessible training datasets used to train AI models applied to Earth observation data is just one of the many problems faced by users who want to apply artificial intelligence to satellite data. Furthermore, the acquisition and labeling of EO data is complicated and expensive, slowing the advancement of AI in EO and limiting its potential compared to other fields.
This is how SCANEO emerges, a smart labeling web application for training sets with satellite data, powered through artificial intelligence and active learning.
Installation
SCANEO is simply a Python package that can be installed using pip.
pip install scaneo
Is is recommended to upgrade the package regularly, in order to get the latest changes.
pip install scaneo --upgrade
The library requires Python >= 3.8
For a fast installation, we commend using
uv pip install scaneo.
Usage
SCANEO allows to launch the labelling web application through CLI commands. The first thing you can do is run the help command, which will give you a list of all the available commands in the CLI.
scaneo --help
You can launch scaneo with a single command:
scaneo
This will launch the UI, which will be accessible on your localhost:8000.
Documentation and tutorials
To view the documentation, launch the UI and go to localhost:8000/docs. There you will find all the detailed SCANEO documentation, with advanced examples of usage, videos and tutorials.
Build
This repository contains the source code for SCANEO.
- scaneo: includes the CLI, the library and the API.
- ui: includes the web application UI.
The CLI runs the API, which in turns serves the static files for the UI.
Development
Run the API with the CLI:
python scaneo/main.py run -r
Then, run the UI:
cd ui
yarn dev
Remember to create the .env file from .env.example
cp .env.example .env
Production
In the UI, create .env.production with an empty API_URL.
Build the UI, copy the build inside scaneo and build the python package:
Your annoatations will be stored as GeoJSON files with the same name of your images, followed by _labels.geojson. For example, if you are labeling image1.tif, your annotations will be stored in image1_labels.geojson. Additionally, a labels.json file will be created containing some metadata about your annotations.
It is needed to install mkdocs with
pip install mkdocs-material
Notes
Do not add scaneo/ui to gitignore since the build process will fail (missing entry folder).
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file scaneo-2024.12.11.post8.tar.gz.
File metadata
- Download URL: scaneo-2024.12.11.post8.tar.gz
- Upload date:
- Size: 61.0 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.5.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
006e87df3d88d68e86d23b2249af4f0b9844106c94d89f00e86cfb21638a11e5
|
|
| MD5 |
dfb49dc06e0aac4bf1c2d847b5997941
|
|
| BLAKE2b-256 |
2e71961768b57cc6983709ed574f4536c9bb9f9e865aab0c9997f5108bad89a6
|
File details
Details for the file scaneo-2024.12.11.post8-py3-none-any.whl.
File metadata
- Download URL: scaneo-2024.12.11.post8-py3-none-any.whl
- Upload date:
- Size: 20.8 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.5.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4d0b663f49037bf1bc35d2743f32cf6e4541b2efaab335d1288daa5811f3bd8f
|
|
| MD5 |
c79b6757c022be615c431a4658b49dc6
|
|
| BLAKE2b-256 |
423ae18e8ea3d0c7824838c75ab38e5c06ffbca181c27161bcd604c3e38beb0a
|
