RS-OVC: Open-Vocabulary Counting for Remote-Sensing Data
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915929585090560 |
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| author | Shor, Tamir Leifman, George Beryozkin, Genady |
| author_facet | Shor, Tamir Leifman, George Beryozkin, Genady |
| contents | Object-Counting for remote-sensing (RS) imagery is attracting increasing research interest due to its crucial role in a wide and diverse set of applications. While several promising methods for RS object-counting have been proposed, existing methods focus on a closed, pre-defined set of object classes. This limitation necessitates costly re-annotation and model re-training to adapt current approaches for counting of novel objects that have not been seen during training, and severely inhibits their application in dynamic, real-world monitoring scenarios. To address this gap, in this work we propose RS-OVC - the first Open Vocabulary Counting (OVC) model for Remote-Sensing and aerial imagery. We show that our model is capable of accurate counting of novel object classes, that were unseen during training, based solely on textual and/or visual conditioning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08704 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RS-OVC: Open-Vocabulary Counting for Remote-Sensing Data Shor, Tamir Leifman, George Beryozkin, Genady Computer Vision and Pattern Recognition Object-Counting for remote-sensing (RS) imagery is attracting increasing research interest due to its crucial role in a wide and diverse set of applications. While several promising methods for RS object-counting have been proposed, existing methods focus on a closed, pre-defined set of object classes. This limitation necessitates costly re-annotation and model re-training to adapt current approaches for counting of novel objects that have not been seen during training, and severely inhibits their application in dynamic, real-world monitoring scenarios. To address this gap, in this work we propose RS-OVC - the first Open Vocabulary Counting (OVC) model for Remote-Sensing and aerial imagery. We show that our model is capable of accurate counting of novel object classes, that were unseen during training, based solely on textual and/or visual conditioning. |
| title | RS-OVC: Open-Vocabulary Counting for Remote-Sensing Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.08704 |