Skip to main content
SpringerLink
Log in

Towards Fire Identification Model in Satellite Images Using HPC Embedded Systems and AI

  • Conference paper
  • First Online:
High Performance Computing (CARLA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1660))

Included in the following conference series:

Abstract

Forest fires and environmental disasters that are rarely avoided due to Forest fires are environmental disasters is a crucial problem to resolve with High Performance Computing (HPC) due to the real-time need to avoid the reaction of the control agencies and the community. One of the strategies to support early warnings related to forest fires is using space technology and realtime image treatment. However, the large amount of data given by the satellite images, the cost of the satellite technology, and the difficulty of accessing remote places information make it challenging to deal with the problem. This project presents the development of a solution that fights fires through identification supported using artificial intelligence (AI), mainly Convolutional Neural Networks (CNN) and Computer Vision (CV). Space technology captures images in various spectral frequency ranges by optical instruments onboard artificial satellites. In addition, the solution deploys on a low-cost and easily accessible open-source embedded system, which allows its scope to be extended for use on mobile device applications such as robots, and uncrewed aerial vehicles, among others. This paper reflects the progress achieved within the project, mainly the creation of an open-source dataset of satellite images for fire classification, the election, conditioning, and training of the CNN.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    More information in: https://www.sc3.uis.edu.co.

References

  1. Feng, X., Jiang, Y., Yang, X., Du, M., Li, X.: Computer vision algorithms and hardware implementations: a survey. Integration 69, 309–320 (2019)

    Article  Google Scholar 

  2. Brozek, T., ed.: Micro-and nanoelectronics: emerging device challenges and solutions. CRC Press (2014)

    Google Scholar 

  3. Nano, N.J.: https://nvidia.com/en-us/autonomus-machines/embedded-systems/jetson-nano/. Accessed 23 Oct 2021

  4. Petrescu, R.V., et al.: NASA satellites help us to quickly detect forest fires. Am. J. Eng. Appl. Sci. 11(1), 288–296 (2018)

    Article  Google Scholar 

  5. Van Etten, A.: Satellite imagery multi-scale rapid detection with windowed networks. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 735–743. IEEE (2019)

    Google Scholar 

  6. Van Etten, A.: You only look twice. Rapid multi-scale object detection in satellite imagery. arXiv preprint arXiv:1805.09512 (2018)

  7. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  8. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. CoRR abs/1506.01497 (2015). http://arxiv.org/abs/1506.01497

  9. Graphic Processing Unit (GPU). https://www.intel.la/content/www/xl/es/products/docs/processors/what-is-a-gpu.html. Accessed 13 July 2020

  10. Field Programmable Gate Array (FPGA). https://www.arm.com/glossary/fpga. Accessed 14 July 2020

  11. Wang, X., Ng, H.W., Liang, J.: Lapped convolutional neural networks for embedded systems. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1135–1139. IEEE (2017)

    Google Scholar 

  12. De Silva, D., Alahakoon, D.: An artificial intelligence life cycle: From conception to production. Patterns, 100489 (2022)

    Google Scholar 

  13. National Aeronautics and Space Administration, data visualization application WORLDVIEW. https://worldview.earthdata.nasa.gov/. Accessed 20 Oct 2020

  14. Cao, C., Xiong, J., Blonski, S., Liu, Q., Uprety, S., Shao, X., Weng, F.: Suomi NPP VIIRS sensor data record verification, validation, and long-term performance monitoring. J. Geophys. Res. Atmos. 118(20), 664–678 (2013)

    Article  Google Scholar 

  15. Cao, C., et al.: NOAA-20 VIIRS on-orbit performance, data quality, and operational Cal/Val support. In: Earth Observing Missions and Sensors: Development, Implementation, and Characterization V, vol. 10781, p. 107810K. International Society for Optics and Photonics (2018)

    Google Scholar 

  16. Weng, F.: Advanced Technology Microwave Sounder Calibration and Validation. Liang, S.: Comprehensive Remote Sensing, pp. 42–63. Elsevier (2018). ISBN 9780128032213. https://doi.org/10.1016/B978-0-12-409548-9.10393-8

  17. National Oceanic and Atmospheric Administration. NOAA Technical Report NESDIS 142 (2017). https://ncc.nesdis.noaa.gov/documents/documentation/viirs-users-guide-tech-report-142a-v1.3.pdf. https://doi.org/10.1016/B978-0-12-409548-9.10393-8. Accessed 15 Apr 2020

  18. Zoph, B., Cubuk, E., Ghiasi, G., Lin, T., Shlens, J., Le, Q.: Learning data augmentation strategies for object detection. arXiv (2019)

    Google Scholar 

  19. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)

    Google Scholar 

  20. Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  21. D. Shah, «Early Fire detection system using deep learning and OpenCV,» 2020, https://towardsdatascience.com/early-fire-detection-system-using-deep-learning-and-opencv-6cb60260d54a. Accessed 20 Jun 2021

  22. Sahoo, S.: How to Train MobileNetV2 On a Custom Dataset (2021). https://blog.roboflow.com/how-to-train-mobilenetv2-on-a-custom-dataset/. Accessed 24 Jun 2021

  23. Sarkar, D., Bali, R., Ghosh, T.: Hands-on Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing (2018)

    Google Scholar 

  24. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  25. Pang, B., Nijkamp, E., Wu, Y.N.: Deep learning with tensorflow: a review. Journal of Educational and Behavioral Statistics 45(2), 227–248 (2020)

    Article  Google Scholar 

  26. Roboflow Platform. https://roboflow.com/. Access 30 Oct 2021

  27. Github. https://github.com/jhonesis/Proyecto-IGNIS. Access 13 Jun 2022

Download references

Author information

Authors and Affiliations

  1. Universidad Industrial de Santander (UIS), Bucaramanga, Colombia

    Jhon Deivy Perez Arguello, Carlos J. Barrios Hernández & Julián Rodriguez Ferreira

  2. High Performance and Scientific Computing Center (SC3UIS), Bucaramanga, Colombia

    Jhon Deivy Perez Arguello, Carlos J. Barrios Hernández & Julián Rodriguez Ferreira

  3. Electronic Control, Simulation and Modelling Group (CEMOS), Bucaramanga, Colombia

    Julián Rodriguez Ferreira

  4. Advanced and Large Scale Computing Group (CAGE), Bucaramanga, Colombia

    Jhon Deivy Perez Arguello, Carlos J. Barrios Hernández & Julián Rodriguez Ferreira

Authors
  1. Jhon Deivy Perez Arguello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos J. Barrios Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julián Rodriguez Ferreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jhon Deivy Perez Arguello .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Philippe Navaux

  2. Universidad Industrial de Santander, Bucaramanga, Colombia

    Carlos J. Barrios H.

  3. Laboratório Nacional de Computação Científica, Petrópolis, Brazil

    Carla Osthoff

  4. Laboratorio Nacional de Computación de Alto Rendimiento, Santiago, Chile

    Ginés Guerrero

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arguello, J.D.P., Hernández, C.J.B., Ferreira, J.R. (2022). Towards Fire Identification Model in Satellite Images Using HPC Embedded Systems and AI. In: Navaux, P., Barrios H., C.J., Osthoff, C., Guerrero, G. (eds) High Performance Computing. CARLA 2022. Communications in Computer and Information Science, vol 1660. Springer, Cham. https://doi.org/10.1007/978-3-031-23821-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23821-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23820-8

  • Online ISBN: 978-3-031-23821-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation