Summary of Forest Inspection Dataset For Aerial Semantic Segmentation and Depth Estimation, by Bianca-cerasela-zelia Blaga and Sergiu Nedevschi
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation
by Bianca-Cerasela-Zelia Blaga, Sergiu Nedevschi
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract presents a novel approach to addressing the lack of ground truth recordings for forest inspection using UAVs. To overcome this challenge, researchers introduce a large aerial dataset containing real-world and virtual recordings of natural environments with annotated semantic segmentation labels and depth maps. The dataset is designed to simulate various acquisition conditions, including different illumination, altitudes, and recording angles. Two multi-scale neural networks (HRNet and PointFlow network) are tested for solving the semantic segmentation task, demonstrating the benefits of transfer learning from virtual to real data. The study highlights the importance of training models on diverse scenarios rather than categorizing data. Additionally, a framework is proposed for assessing deforestation degree in an area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Forest inspection using UAVs can provide valuable information about environmental changes, but current datasets lack detailed annotations. Researchers have created a new dataset containing real-world and virtual recordings of forests with semantic segmentation labels and depth maps. The dataset simulates different conditions like light, altitude, and angle. Two neural networks were tested to see how well they work on this task. The study shows that training models on many scenarios is better than just one type of scenario. It also proposes a way to measure deforestation in an area. |
Keywords
» Artificial intelligence » Semantic segmentation » Transfer learning
