Tree semantic segmentation from aerial image time series

• URL: http://arxiv.org/abs/2407.13102v1
• Date: Thu, 18 Jul 2024 02:19:57 GMT
• Title: Tree semantic segmentation from aerial image time series
• Authors: Venkatesh Ramesh, Arthur Ouaknine, David Rolnick,
• Abstract summary: We perform semantic segmentation of trees using an aerial dataset image 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 performances. 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.
• Score: 24.14827064108217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 semantic segmentation of trees 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 performances. 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 findings demonstrate the superiority of our methodology in exploiting the time series modality and confirm that enriching labels using taxonomic information improves the semantic segmentation performance.

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