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Automated Road Extraction from Satellite Imagery Integrating Dense Depthwise Dilated Separable Spatial Pyramid Pooling with DeepLabV3+

arXiv 2410.14836 ↗First posted 18 Oct 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

Road Extraction is a sub-domain of Remote Sensing applications; it is a subject of extensive and ongoing research. The procedure of automatically extracting roads from satellite imagery encounters significant challenges due to the multi-scale and diverse structures of roads; improvement in this field is needed. The DeepLab series, known for its proficiency in semantic segmentation due to its efficiency in interpreting multi-scale objects' features, addresses some of these challenges caused by the varying nature of roads. The present work proposes the utilization of DeepLabV3+, the latest version of the DeepLab series, by introducing an innovative Dense Depthwise Dilated Separable Spatial Pyramid Pooling (DenseDDSSPP) module and integrating it in place of the conventional Atrous Spatial Pyramid Pooling (ASPP) module. This modification enhances the extraction of complex road structures from satellite images. This study hypothesizes that the integration of DenseDDSSPP, combined with an appropriately selected backbone network and a Squeeze-and-Excitation block, will generate an efficient dense feature map by focusing on relevant features, leading to more precise and accurate road extraction from Remote Sensing images. The results section presents a comparison of our model's performance against state-of-the-art models, demonstrating better results that highlight the effectiveness and success of the proposed approach.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 18 Oct 2024.

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17 of 34 references.

References · 17

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  11. 2017Road Extraction by Deep Residual U-Netcited by 333
  12. 2018DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Imagescited by 276
5 more
  1. 2019Zero-Shot Semantic Segmentationcited by 245
  2. 2018ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Networkcited by 90
  3. 2016Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagerycited by 36
  4. 2023Privacy Risks Analysis and Mitigation in Federated Learning for Medical Imagescited by 7
  5. 2024The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translationcited by 7

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