CoeusAI QGIS plugin - Efficient exploratory search for features

Our QGIS plugin, CoeusAI, is designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain classification models in seconds. Traditional machine learning approaches can lack intelligent predictions, while deep neural nets typically require vast amounts of training data and compute. Our tool combines a pretrained UNet for feature extraction with a RandomForest for classification. We therefore leverage strong points of both. The use of a UNet makes it possible to take into account complex patterns and encode these in a feature space. The RandomForest is then trained on top of those features with little labeled data, and only seconds (or minutes) of compute time required. This allows for an interactive and explorative user experience. CoeusAI uses the functionalities of the command-line-tool and Python package, Pycoeus, it's main dependency, and makes them available with a user-friendly graphical user-interface to use in QGIS.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-11
DOI
https://doi.org/10.5281/zenodo.15681272
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

CoeusAI QGIS plugin - Efficient exploratory search for features

Zenodo (CERN European Organization for Nuclear Research)
Remote-Sensing Image Classification
article

CoeusAI QGIS plugin - Efficient exploratory search for features

article en

Abstract

Our QGIS plugin, CoeusAI, is designed for exploration of multiband geospatial datasets. It lets the user iteratively train and retrain classification models in seconds. Traditional machine learning approaches can lack intelligent predictions, while deep neural nets typically require vast amounts of training data and compute. Our tool combines a pretrained UNet for feature extraction with a RandomForest for classification. We therefore leverage strong points of both. The use of a UNet makes it possible to take into account complex patterns and encode these in a feature space. The RandomForest is then trained on top of those features with little labeled data, and only seconds (or minutes) of compute time required. This allows for an interactive and explorative user experience. CoeusAI uses the functionalities of the command-line-tool and Python package, Pycoeus, it's main dependency, and makes them available with a user-friendly graphical user-interface to use in QGIS.

Zenodo (CERN European Organization for Nuclear Research)
Netherlands eScience Center (NL), Vrije Universiteit Amsterdam (NL), University of Amsterdam (NL)
Openalex Percentile: Top 46%
Remote-Sensing Image Classification
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