Projects per year
Personal profile
Short bio
Research scientist focusing on remote sensing and geoinformatics. Working with projects (e.g. forest-related and New Space) and my doctoral research that concentrates on retrieving biophysical variables using artificial intelligence and earth observation data.
Expertise area
Remote sensing, Hyperspectral imaging, Earth observation, Geoinformatics, Spatial information.
Research interests
- Hyperspectral remote sensing
- Forest reflectance models
- Earth observation applications
- Machine learning
- Remote sensing in forestry
- Spatial analytics
- Boreal forest
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Education, Master, Master of Science (Technology): Geoinformatics, Aalto University
Award Date: 11 Dec 2018
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Projects
- 1 Finished
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AVARTAVA: Avaruustoiminnan yhteiskunnallinen vaikuttavuus
Häme, T., Höyhtyä, M. & Halme, E.
1/02/21 → 31/12/21
Project: Finnish government project
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Assessing spatial variability and estimating mean crown diameter in boreal forests using variograms and amplitude spectra of very-high-resolution remote sensing data
Halme, E., Ihalainen, O., Korpela, I. & Mõttus, M., 23 Jan 2022, In: International Journal of Remote Sensing. 43, 1, p. 349-369 21 p.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile13 Downloads (Pure) -
TAIGA: A Novel Dataset for Multitask Learning of Continuous and Categorical Forest Variables From Hyperspectral Imagery
Mõttus, M., Pham, P., Halme, E., Molinier, M., Cu, H. & Laaksonen, J., 7 Jan 2022, In: IEEE Transactions on Geoscience and Remote Sensing. 60, 5521711.Research output: Contribution to journal › Article › Scientific › peer-review
Open Access -
A methodology for implementing a digital twin of the earth’s forests to match the requirements of different user groups
Mõttus, M., Dees, M., Astola, H., Dałek, S., Halme, E., Häme, T., Krzyżanowska, M., Mäkelä, A., Marin, G., Minunno, F., Pawlowski, G., Penttilä, J. & Rasinmäki, J., 2021, In: GI_Forum. 9, 1, p. 130-136Research output: Contribution to journal › Article › Scientific › peer-review
Open Access2 Citations (Scopus) -
Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest
Astola, H., Seitsonen, L., Halme, E., Molinier, M. & Lönnqvist, A., 18 Jun 2021, In: Remote Sensing. 13, 12, 2392.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile12 Citations (Scopus)92 Downloads (Pure) -
Patch size selection for analysis of sub-meter resolution hyperspectral imagery of forests
Mõttus, M., Molinier, M., Halme, E., Cu, H. & Laaksonen, J., 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE Institute of Electrical and Electronic Engineers, p. 2035-2038 4 p.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
1 Citation (Scopus)