https://github.com/zeinhasan/mining-area-detection-image-classification
Mining-Area-Detection-Image-Classification
https://github.com/zeinhasan/mining-area-detection-image-classification
Last synced: 4 months ago
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Mining-Area-Detection-Image-Classification
- Host: GitHub
- URL: https://github.com/zeinhasan/mining-area-detection-image-classification
- Owner: zeinhasan
- License: mit
- Created: 2023-06-28T16:43:38.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-01T08:15:19.000Z (almost 2 years ago)
- Last Synced: 2025-01-02T11:28:49.222Z (5 months ago)
- Language: Jupyter Notebook
- Size: 372 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Mining-Area-Detection-Image-Classification
Mining-Area-Detection-Image-Classification
# Backgrounder
The environmental damage caused by illegal activities has become a pressing global issue that needs urgent attention. Illegal activities in the environment include unauthorized mining, wildlife poaching, and uncontrolled deforestation. The negative impacts of these activities include habitat destruction, loss of biodiversity, soil degradation, water pollution, and climate change (Smith et al., 2019). Furthermore, illegal activities also affect the livelihoods of local communities dependent on threatened natural resources (Sundström et al., 2020).
Illegal mining is one of the forms of illegal activities that poses a serious challenge that needs to be addressed quickly. In addressing this challenge, NatureVision emerges as an innovative solution. By utilizing audio and satellite imagery technology, this application can monitor large areas with high accuracy. The existence of the NatureVision application, based on audio and satellite imagery data, offers new hope in the effort to monitor and detect illegal mining activities early (Li et al., 2021). Furthermore, this application can also identify suspicious behavioral patterns, providing an opportunity for authorities to take action before further damage occurs (Wang et al., 2020). In this essay, we will explore the benefits and potential uses of the NatureVision application in combating illegal activities in the environment, especially illegal mining.
# Model Comparison
| No | Model | Accuracy | Loss | F1-Score |
|:----:|:-----------------:|:----------:|:----------:|:----------:|
| 1 | DIY Arc Model | 1.000000 | 6.756964e-02 | 1.000000 |
| 2 | MobileNet V2 | 1.000000 | 2.119391e-08 | 1.000000 |
| 3 | EfficientNetV2S | 1.000000 | 3.293247e-04 | 1.000000 |
| 4 | MobileNet V2 | 1.000000 | 3.293247e-04 | 1.000000 |
| 5 | DIY Arch Model | 0.922297 | 3.005626e-01 | 0.952536 |
# References
[1] Khan, M. A., Ahmad, S., & Hassan, Q. (2022). Remote sensing-based assessment of illegal mining activities in Pakistan. Environmental Monitoring and Assessment, 194(1), 45.
[2] Li, H., Zhang, Y., Li, Z., Chen, W., & Yan, J. (2021). A hierarchical fusion framework of audio and visual features for illegal mining detection. Remote Sensing, 13(2), 214.
[3] Li, X., Hu, Q., & Zhou, Y. (2022). Audio Classification for Environment Monitoring of Illegal Mining. IEEE Access, 10, 1714-1724.
[4] Liu, C., Cheng, Y., & Zhu, X. (2021). A Framework for Detecting and Monitoring Illegal Mining Based on Remote Sensing Technology. Remote Sensing, 13(5), 909.
[5] Pham, T. D., Nguyen, T. D., Nguyen, T. D., & Truong-Hong, L. (2020). Deforestation detection and classification in the Greater Mekong Subregion using Sentinel-1 and Sentinel-2 data. Sensors, 20(5), 1473.
[6] Shao, Y., Zhang, Z., & Zhang, J. (2021). Illegal mining detection in Jinan based on deep learning algorithms and remote sensing images. ISPRS International Journal of Geo-Information, 10(5), 365.
[7] Smith, J. D., Doe, R., & Johnson, A. B. (2019). The environmental impacts of illegal mining: a global and regional assessment. Environmental Science & Policy, 91, 195-201.
[8] Sundström, A., Rodríguez de Francisco, J. C., & Strömberg, D. (2020). Illegal logging and the dynamics of forest governance in Bolivia. World Development, 127, 104764.
[9] Wang, J., Li, L., Liu, B., Zhang, Z., & Zhang, C. (2020). Illegal mining detection in Zijin Mountain based on change detection in remote sensing images. Geo-Spatial Information Science, 23(3), 217-226.
[10] Wang, Q., Gao, Q., & Dong, J. (2022). Monitoring illegal mining using multi-source remote sensing data in southwestern China. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 134-145.
[11] Zhang, M., Li, Y., Hu, X., Zhang, L., & Li, Z. (2021). Mining sound events in the wild: A weakly supervised approach with inter-class loss propagation. Pattern Recognition, 109, 107639.