I am a researcher at the Finnish Environment Institute SYKE, focusing on applying machine learning and computer vision to environmental problems, especially biodiversity monitoring.
Blog
Publications
Impiö, M., Härmä, P., Tammilehto, A., Anttila, S., & Raitoharju, J. (2022). Habitat classification from satellite observations with sparse annotations. arXiv preprint arXiv:2209.12995. Paper
Impiö, M., Yamaç, M., & Raitoharju, J. (2021, June). Multi-level reversible encryption for ECG signals using compressive sensing. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1005-1009). IEEE. Paper | Code
de Schaetzen, F., Impiö, M., Wagner, B., Nienaltowski, P., Arnold, M., Huber, M., … & Stocker, R. (2023). The Riverine Organism Drift Imager: A new technology to study organism drift in rivers and streams. Methods in Ecology and Evolution. Paper
Code
Taxonomist - a species classification pipeline
A modular, extensible framework for training deep learning models for species classification.
Point-EO - Python library for training machine learning models on point-based geospatial data and large rasters
A python libary that makes it simple to sample points from large rasters, fit ML models and perform inference on larger-than-memory rasters.