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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06972 |
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| _version_ | 1866913934074707968 |
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| author | Orsholm, Johanna Quinto, John Autto, Hannu Banelyte, Gaia Chazot, Nicolas deWaard, Jeremy deWaard, Stephanie Farrell, Arielle Furneaux, Brendan Hardwick, Bess Ito, Nao Kar, Amlan Kalttopää, Oula Kerdraon, Deirdre Kristensen, Erik McKeown, Jaclyn Mononen, Tommi Nein, Ellen Rogers, Hanna Roslin, Tomas Schmitz, Paula Sones, Jayme Sujala, Maija Thompson, Amy Zakharov, Evgeny V. Zarubiieva, Iuliia Gupta, Akshita Lowe, Scott C. Taylor, Graham W. |
| author_facet | Orsholm, Johanna Quinto, John Autto, Hannu Banelyte, Gaia Chazot, Nicolas deWaard, Jeremy deWaard, Stephanie Farrell, Arielle Furneaux, Brendan Hardwick, Bess Ito, Nao Kar, Amlan Kalttopää, Oula Kerdraon, Deirdre Kristensen, Erik McKeown, Jaclyn Mononen, Tommi Nein, Ellen Rogers, Hanna Roslin, Tomas Schmitz, Paula Sones, Jayme Sujala, Maija Thompson, Amy Zakharov, Evgeny V. Zarubiieva, Iuliia Gupta, Akshita Lowe, Scott C. Taylor, Graham W. |
| contents | Insects comprise millions of species, many experiencing severe population declines under environmental and habitat changes. High-throughput approaches are crucial for accelerating our understanding of insect diversity, with DNA barcoding and high-resolution imaging showing strong potential for automatic taxonomic classification. However, most image-based approaches rely on individual specimen data, unlike the unsorted bulk samples collected in large-scale ecological surveys. We present the Mixed Arthropod Sample Segmentation and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples. It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens. Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens. Combining the taxonomic resolution of DNA barcodes with precise abundance estimates of bulk images holds great potential for rapid, large-scale characterization of insect communities. This dataset pushes the boundaries of tiny object detection and instance segmentation, fostering innovation in both ecological and machine learning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level Orsholm, Johanna Quinto, John Autto, Hannu Banelyte, Gaia Chazot, Nicolas deWaard, Jeremy deWaard, Stephanie Farrell, Arielle Furneaux, Brendan Hardwick, Bess Ito, Nao Kar, Amlan Kalttopää, Oula Kerdraon, Deirdre Kristensen, Erik McKeown, Jaclyn Mononen, Tommi Nein, Ellen Rogers, Hanna Roslin, Tomas Schmitz, Paula Sones, Jayme Sujala, Maija Thompson, Amy Zakharov, Evgeny V. Zarubiieva, Iuliia Gupta, Akshita Lowe, Scott C. Taylor, Graham W. Computer Vision and Pattern Recognition Insects comprise millions of species, many experiencing severe population declines under environmental and habitat changes. High-throughput approaches are crucial for accelerating our understanding of insect diversity, with DNA barcoding and high-resolution imaging showing strong potential for automatic taxonomic classification. However, most image-based approaches rely on individual specimen data, unlike the unsorted bulk samples collected in large-scale ecological surveys. We present the Mixed Arthropod Sample Segmentation and Identification (MassID45) dataset for training automatic classifiers of bulk insect samples. It uniquely combines molecular and imaging data at both the unsorted sample level and the full set of individual specimens. Human annotators, supported by an AI-assisted tool, performed two tasks on bulk images: creating segmentation masks around each individual arthropod and assigning taxonomic labels to over 17 000 specimens. Combining the taxonomic resolution of DNA barcodes with precise abundance estimates of bulk images holds great potential for rapid, large-scale characterization of insect communities. This dataset pushes the boundaries of tiny object detection and instance segmentation, fostering innovation in both ecological and machine learning research. |
| title | A multi-modal dataset for insect biodiversity with imagery and DNA at the trap and individual level |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.06972 |