Summary of Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery, by Asim Khan et al.
Accurate and Efficient Urban Street Tree Inventory with Deep Learning on Mobile Phone Imagery
by Asim Khan, Umair Nawaz, Anwaar Ulhaq, Iqbal Gondal, Sajid Javed
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method uses deep learning techniques and mobile phone imaging to accurately segment tree trunks and compute diameter at breast height (DBH) for urban street tree inventory. The approach leverages a pair of images captured by smartphone cameras, offering superior accuracy, reduced equipment dependency, and applicability in hard-to-reach areas. Compared to traditional methods, the proposed method achieves a DBH estimation accuracy with an error rate of less than 2.5% on a comprehensive dataset of 400 trees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach helps improve forest management practices by enhancing the accuracy and efficiency of tree inventory. The model empowers urban managers to mitigate the adverse effects of deforestation and climate change, which can cause agricultural sector disruption, global warming, flash floods, and landslides. |
Keywords
» Artificial intelligence » Deep learning
