Tree semantic segmentation from aerial image time series
Earth’s forests play an important role in the fight against climate change and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree spec...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Environmental Data Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225100137/type/journal_article |
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| author | Venkatesh Ramesh Arthur Ouaknine David Rolnick |
| author_facet | Venkatesh Ramesh Arthur Ouaknine David Rolnick |
| author_sort | Venkatesh Ramesh |
| collection | DOAJ |
| description | Earth’s forests play an important role in the fight against climate change and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing tree crown semantic segmentation using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performance. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our best model achieves a mean Intersection over Union (mIoU) of 55.97%, outperforming single-image approaches particularly for deciduous trees where phenological changes are most noticeable. Our findings highlight the benefit of exploiting the time series modality via our Processor module. Furthermore, leveraging taxonomic information through our hierarchical loss function often, and in key cases significantly, improves semantic segmentation performance. |
| format | Article |
| id | doaj-art-e6b4241474e54cdf9903e89055977950 |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-e6b4241474e54cdf9903e890559779502025-08-20T03:58:40ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.10013Tree semantic segmentation from aerial image time seriesVenkatesh Ramesh0https://orcid.org/0009-0005-5400-2516Arthur Ouaknine1https://orcid.org/0000-0003-1090-6204David Rolnick2Mila, https://ror.org/05c22rx21Quebec AI Institute, Montréal, QC, Canada Département d’informatique et de recherche opérationnelle, https://ror.org/0161xgx34Université de Montréal, Montréal, QC, CanadaMila, https://ror.org/05c22rx21Quebec AI Institute, Montréal, QC, Canada School of Computer Science, https://ror.org/01pxwe438 McGill University , Montréal, QC, CanadaMila, https://ror.org/05c22rx21Quebec AI Institute, Montréal, QC, Canada School of Computer Science, https://ror.org/01pxwe438 McGill University , Montréal, QC, CanadaEarth’s forests play an important role in the fight against climate change and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing tree crown semantic segmentation using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performance. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our best model achieves a mean Intersection over Union (mIoU) of 55.97%, outperforming single-image approaches particularly for deciduous trees where phenological changes are most noticeable. Our findings highlight the benefit of exploiting the time series modality via our Processor module. Furthermore, leveraging taxonomic information through our hierarchical loss function often, and in key cases significantly, improves semantic segmentation performance.https://www.cambridge.org/core/product/identifier/S2634460225100137/type/journal_articledeep learningforest monitoringphenologyremote sensingtime series |
| spellingShingle | Venkatesh Ramesh Arthur Ouaknine David Rolnick Tree semantic segmentation from aerial image time series Environmental Data Science deep learning forest monitoring phenology remote sensing time series |
| title | Tree semantic segmentation from aerial image time series |
| title_full | Tree semantic segmentation from aerial image time series |
| title_fullStr | Tree semantic segmentation from aerial image time series |
| title_full_unstemmed | Tree semantic segmentation from aerial image time series |
| title_short | Tree semantic segmentation from aerial image time series |
| title_sort | tree semantic segmentation from aerial image time series |
| topic | deep learning forest monitoring phenology remote sensing time series |
| url | https://www.cambridge.org/core/product/identifier/S2634460225100137/type/journal_article |
| work_keys_str_mv | AT venkateshramesh treesemanticsegmentationfromaerialimagetimeseries AT arthurouaknine treesemanticsegmentationfromaerialimagetimeseries AT davidrolnick treesemanticsegmentationfromaerialimagetimeseries |