Summary of Automated Road Extraction From Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with Deeplabv3+, by Arpan Mahara et al.
Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+
by Arpan Mahara, Md Rezaul Karim Khan, Naphtali D. Rishe, Wenjia Wang, Seyed Masoud Sadjadi
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This research paper proposes an innovative approach for automatically extracting roads from satellite imagery using the DeepLabV3+ architecture. By introducing a new module called Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP), the authors aim to improve the extraction of complex road structures. The proposed method combines the DenseDDSSPP module with an appropriately selected backbone network and Squeeze-and-Excitation block, which enables the generation of efficient dense feature maps that focus on relevant features. The results demonstrate better performance compared to state-of-the-art models, highlighting the effectiveness and success of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find roads in satellite pictures using a special kind of artificial intelligence called DeepLabV3+. Right now, finding roads from space is a big challenge because roads come in many shapes and sizes. The team behind this project used an updated version of DeepLabV3+, called DenseDDSSPP, which helps the computer look at the right details to find roads accurately. They think that by combining this new module with other important parts of the system, they can make the road-finding process even better. The results show that their method works really well and is a step forward in finding roads from space. |
