Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

Cheng, Jian; Deng, Changjian; Su, Yanzhou; An, Zeyu; Wang, Qi

doi:10.1016/j.isprsjprs.2024.03.012

ISPRS Journal of Photogrammetry and Remote Sensing

2024

DOI: 10.1016/j.isprsjprs.2024.03.012

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Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

Jian Cheng,

Changjian Deng,

Yanzhou Su

et al.

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Cited by 9 publications

References 295 publications

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LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

Şener,

Ergen

2024

Earth Sci Inform

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In recent years, remote sensing technologies have played a crucial role in the detection and management of natural disasters. In this context, deep learning models are of great importance for the early detection of natural disasters such as landslides. Landslide segmentation is a fundamental tool for the development of geographic information systems, natural disaster management and risk mitigation strategies. In this study, we propose a new semantic segmentation model called LandslideSegNet to improve early intervention capabilities for potential landslide scenarios. LandslideSegNet incorporates an encoder-decoder architecture that integrates local and contextual information, advanced encoder-decoder residual blocks and Efficient Hybrid Attentional Atrous Convolution. Thanks to this structure, the model is able to extract high-resolution feature maps from remote sensing imagery, accurately delineate the landslide areas and minimize the loss of contextual information. The developed LandslideSegNet model has shown significantly higher accuracy rates with fewer parameters compared to existing image segmentation models. The model was trained and tested using the Landslide4Sense dataset specially prepared for landslide detection. LandslideSegNet achieved an accuracy of 97.60% and 73.65% mean Intersection over Union of 73.65 on this dataset, demonstrating its efficiency. These results indicate the potential usability of the model in landslide detection and related disaster management applications.

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LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

Şener,

Ergen

2024

Earth Sci Inform

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A terrain segmentation network for navigable areas with global strip reliability evaluation and dynamic fusion

Li,

Liao,

Zou

2025

Expert Systems with Applications

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Research Advances in Deep Learning for Image Semantic Segmentation Techniques

Xiao,

Chai,

et al. 2024

IEEE Access

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Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review

Cited by 9 publications

References 295 publications

LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

LandslideSegNet: an effective deep learning network for landslide segmentation using remote sensing imagery

A terrain segmentation network for navigable areas with global strip reliability evaluation and dynamic fusion

Research Advances in Deep Learning for Image Semantic Segmentation Techniques

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