Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: ieee.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.6%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: chenmu1204
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 17.9 MB
Statistics
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
Edge Feature Enhancement for Fine-Grained Segmentation of Remote Sensing Images. (Paper)
Fine-RSMI dataset
Fine-RSMI dataset contains 10,225 meticulously annotated remote sensing mineral images. Among these, there are 6,417 images with a resolution of 128 x 128 and 3,808 images with a resolution of 256 x 256. We partition the dataset into 8,175 images for training and 2,050 images for testing.
Download at Google Netdisk or Baidu Netdisk (Code：cb6c)
Annotation visualization examples
Statistics
Code
Our approach is based on an improved baseline twins_pcpvt model of MMSegmentation. See EDFEM for our module design.
Acknowledgement
Our implementation is mainly based on the following codebase MMSegmentation. We gratefully thank the authors for their wonderful works.
Citation
If you find this project useful in your research, please consider cite:
@article{Chen2024EdgeFE,
title={Edge Feature Enhancement for Fine-Grained Segmentation of Remote Sensing Images},
author={Zhenxiang Chen and Tingfa Xu and Yongzhuo Pan and Ning Shen and Huan Chen and Jianan Li},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2024},
volume={62},
pages={1-13},
url={https://api.semanticscholar.org/CorpusID:271940127}
}
Feature-Enhanced Convolutional Attention for Unstable Rock Detection in Aerial Images. (Paper)
RSUR-2D dataset
A collection of high-resolution remote sensing images, specifically features unstable rocks in Beijing's suburban karst terrains. Comprising 1,557 drone-captured images at 1300×800 resolution, it is split into 1,245 training and 312 validation samples, each meticulously annotated with multiple unstable rock objects. These annotations highlight key features like high protrusions and detachment from the parent mass, crucial for identifying potential geological hazards.
Download at Google Netdisk or Baidu Netdisk (Code：v15y)
Annotation visualization examples
Citation
If you find this project useful in your research, please consider cite:
@article{Peng2024FeatureEnhancedCA,
title={Feature-Enhanced Convolutional Attention for Unstable Rock Detection in Aerial Images},
author={Peiran Peng and Zhenxiang Chen and Jianan Li and Shuaihao Han and Tongtong Gao and Lang Hong and Tingfa Xu},
journal={IEEE Geoscience and Remote Sensing Letters},
year={2024},
volume={21},
pages={1-5},
url={https://api.semanticscholar.org/CorpusID:267160166}
}
Owner
- Name: chenmu
- Login: chenmu1204
- Kind: user
- Repositories: 1
- Profile: https://github.com/chenmu1204
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - name: "MMSegmentation Contributors" title: "OpenMMLab Semantic Segmentation Toolbox and Benchmark" date-released: 2020-07-10 url: "https://github.com/open-mmlab/mmsegmentation" license: Apache-2.0
GitHub Events
Total
- Issues event: 2
- Watch event: 2
- Issue comment event: 1
- Push event: 43
- Fork event: 3
Last Year
- Issues event: 2
- Watch event: 2
- Issue comment event: 1
- Push event: 43
- Fork event: 3