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1723854 Vol 10 · Issue 4 Download Paper

GEOGUARD: Satellite-Based Illegal Mining Detection System

Nandhini S Sharvitha GV Shri Rithanya S Sri Vatsan V Subhitcha S Sudharsana B

Subject area: Science,Engineering and Technology  ·  Area of research: Machine Learning

Abstract

GeoGuard is a satellite-based illegal mining detection system designed to identify and monitor unauthorized mining activities in protected forests, no-mining zones, and environmentally sensitive areas. Illegal mining can cause deforestation, land degradation, and significant damage to natural resources, making continuous monitoring essential. The system uses satellite imagery, remote sensing, and image analysis techniques to detect changes in land surfaces over time. Sentinel-2 satellite images are analyzed to identify indicators such as vegetation loss, exposed soil, and excavation-like disturbances. The detected changes are compared with protected-zone and authorized mining boundaries to identify potentially illegal mining activities. The system provides location-based alerts and visualizes suspicious areas on an interactive map, including the affected region and detected changes. By reducing dependence on manual field inspections, GeoGuard enables faster and more efficient monitoring of large geographical areas. Overall, the proposed system supports environmental protection, improves mining surveillance, and assists authorities in taking timely action against unauthorized mining activities.

Keywords

illegal mining detection; NDVI; remote sensing; satellite imagery; Sentinel-2; geospatial analysis; vegetation monitoring; land cover change; environmental monitoring; GIS; image processing

References

[1] Sonter W, Barrett DJ, Soares-Filho BS, Ferreira LJPR. Mining drives extensive deforestation in the Brazilian Amazon. Nature Communications. 2017;8.

[2] Assis EDVD, Oliveira ACS, Souza MAF. Remote sensing and GIS techniques for monitoring environmental impacts of mining activities. Remote Sensing Applications: Society and Environment. 2021.

[3] Maus V, Giljum S, et al. An update on global mining land use. Scientific Data. 2022;9.

[4] Alqurashi AT, Kumar L. Land use and land cover change detection using remote sensing and GIS techniques. Advances in Remote Sensing. 2013.

[5] Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017;202:18–27.

[6] Drusch M, et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment. 2012;120:25–36.

[7] Claverie M, et al. The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment. 2018;219:145–161.

[8] Benediktsson JA, Chanussot J, Fauvel M. Multiple classifiers for hyperspectral data analysis. IEEE Transactions on Geoscience and Remote Sensing. 2007;45(12):3807–3819.

[9] Blaschke T. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing. 2010;65(1):2–16.

[10] Singh A. Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing. 1989;10(6):989–1003.

[11] Jensen JR. Remote sensing of the environment: an earth resource perspective. 2nd ed. Pearson Education; 2007.

[12] Lillesand TM, Kiefer RW, Chipman JW. Remote sensing and image interpretation. 7th ed. Wiley; 2015.

[13] Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 1979;8(2):127–150.

[14] Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment. 2002;83(1):195–213.

[15] Chen CF, et al. Monitoring land cover and land use changes using multi-temporal satellite imagery. Remote Sensing. 2019.

[16] Sathyabama P, Karthik DB. A multi-modal AI framework for real-time illegal sand mining detection and prevention using SAR satellite imagery and IoT-enabled seismic sensors in India. Shanlax International Journal of Arts, Science and Humanities. 2026;13.

[17] Mensah JJ, et al. A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana. Remote Sensing of Environment. 2021.

[18] Kushwaha SPS. Impact of mining activities on land use land cover in the Jharia Coalfield, India. Elsevier. 2015.

[19] Singh SK, Singh RK. Remote sensing based monitoring of mining-induced land cover changes. International Journal of Remote Sensing. 2018.

[20] Jain SK, Singh AK, Sharma AK. Remote sensing and GIS based assessment of environmental impacts of mining. Environmental Monitoring and Assessment. 2019.

[21] Roy P, Roy S, Chakraborty A. Land use and land cover change analysis using satellite imagery and GIS. Journal of Earth System Science. 2015.

[22] Mishra SK, Kumar R. Application of GIS and remote sensing for monitoring mining areas. International Journal of Geoinformatics. 2018.

[23] Wu J, et al. Remote sensing monitoring of mining areas and ecological environmental changes. Remote Sensing. 2020.

[24] Pandey R, Nathawat PK. Land use and land cover mapping through digital image processing and GIS. International Journal of Remote Sensing. 2011.

[25] Srivastava SK. Remote sensing based change detection for environmental monitoring. In: IEEE International Geoscience and Remote Sensing Symposium. 2017.

[26] Chen JM, Pavlic G, Brown LL, Cihlar J, Leblanc SG, White JH. Derivation and validation of Canada-wide coarse-resolution satellite-based land cover. Remote Sensing of Environment. 2004.

[27] Bruzzone L, Fernández Prieto D. An adaptive parcel-based technique for unsupervised change detection. International Journal of Remote Sensing. 2000;21(4):817–822.

[28] Nielsen A. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing. 2007;16(2):463–478.

[29] Camps-Valls G, Gómez-Chova L, Muñoz-Marí J, Rojo-Álvarez JL, Martínez-Ramón M. Kernel-based framework for multi-temporal remote sensing image classification and change detection. IEEE Transactions on Geoscience and Remote Sensing. 2008.

[30] Ghosh S, Roy PD, Das AK. Satellite image classification and land cover change detection using machine learning. IEEE Access. 2020.

[31] Ma A, Zhang Y, Li X. Deep learning for remote sensing image classification and change detection. IEEE Geoscience and Remote Sensing Magazine. 2019.

[32] Skidmore AK, et al. Environmental monitoring using remote sensing and geospatial technologies. Remote Sensing of Environment. 2021.

[33] Goodchild MF. Geographic information systems and science: The foundations of geospatial analysis. International Journal of Geographical Information Science. 2018.

[34] Ministry of Mines, Government of India. Mining Surveillance System (MSS): Satellite-based monitoring system for detection of illegal mining activities. Government of India.

[35] Tatem AJ, Noor AM, von Hagen C, Di Gregorio A, Hay SI. High resolution population maps in remote sensing and GIS applications. Remote Sensing of Environment. 2007.

How to cite this paper

Nandhini S, Sharvitha GV, Shri Rithanya S, Sri Vatsan V, Subhitcha S; Sudharsana B "GEOGUARD: Satellite-Based Illegal Mining Detection System" Iconic Research And Engineering Journals Volume 10 Issue 4 2026 Page 1506-1519
Nandhini S, Sharvitha GV, Shri Rithanya S, Sri Vatsan V, Subhitcha S; Sudharsana B "GEOGUARD: Satellite-Based Illegal Mining Detection System" Iconic Research And Engineering Journals, vol. 10, no. 4, Oct. 2026
Nandhini S, Sharvitha GV, Shri Rithanya S, Sri Vatsan V, Subhitcha S; Sudharsana B (2026). GEOGUARD: Satellite-Based Illegal Mining Detection System. Iconic Research And Engineering Journals, 10(4).
Nandhini S, Sharvitha GV, Shri Rithanya S, Sri Vatsan V, Subhitcha S; Sudharsana B "GEOGUARD: Satellite-Based Illegal Mining Detection System" Iconic Research And Engineering Journals, vol. 10, no. 4, Oct. 2026.
@article{1723854,
      author = {Nandhini S, Sharvitha GV, Shri Rithanya S, Sri Vatsan V, Subhitcha S; Sudharsana B},
      title = {GEOGUARD: Satellite-Based Illegal Mining Detection System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {1506-1519},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723854.pdf},
      abstract = {GeoGuard is a satellite-based illegal mining detection system designed to identify and monitor unauthorized mining activities in protected forests, no-mining zones, and environmentally sensitive areas. Illegal mining can cause deforestation, land degradation, and significant damage to natural resources, making continuous monitoring essential. The system uses satellite imagery, remote sensing, and image analysis techniques to detect changes in land surfaces over time. Sentinel-2 satellite images are analyzed to identify indicators such as vegetation loss, exposed soil, and excavation-like disturbances. The detected changes are compared with protected-zone and authorized mining boundaries to identify potentially illegal mining activities. The system provides location-based alerts and visualizes suspicious areas on an interactive map, including the affected region and detected changes. By reducing dependence on manual field inspections, GeoGuard enables faster and more efficient monitoring of large geographical areas. Overall, the proposed system supports environmental protection, improves mining surveillance, and assists authorities in taking timely action against unauthorized mining activities.},
      keywords = {illegal mining detection; NDVI; remote sensing; satellite imagery; Sentinel-2; geospatial analysis; vegetation monitoring; land cover change; environmental monitoring; GIS; image processing},
      month = {October},
  }