A Platform for Swimming Pool Detection and Legal Verification Using a Multi-Agent System and Remote Image Sensing

  1. Héctor Sánchez San Blas 1
  2. Antía Carmona Balea 1
  3. André Sales Mendes 1
  4. Luís Augusto Silva 1
  5. Gabriel Villarrubia González 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Journal:
IJIMAI

ISSN: 1989-1660

Year of publication: 2023

Volume: 8

Issue: 4

Pages: 153-165

Type: Article

DOI: 10.9781/IJIMAI.2023.01.002 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: IJIMAI

Abstract

Spain is the second country in Europe with the most swimming pools. However, the legal literature estimates that 20% of swimming pools are not declared or irregular.The administration has a corps of people who manually analyze satellite or drone images to detect illegal or irregular structures. This method is costly in terms of effort and time, and it is also a method based on the subjectivity of the person carrying it out. This proposal aims to design a platform that allows the automatic detection of irregular pools. Using geographic information tools (GIS) based on orthophotography, combined with advanced machine learning techniques for object detection, allows this work. Furthermore, using a multi-agent architecture allows the system to be modular, with the possibility of the different parts of the system working together, balancing the workload. The proposed system has been validated by testing it in different towns in Spain. The system has shown promising results in performing this task, with an F1-Score of 97.1%.

Bibliographic References

  • D. Tien, T. Rudra, A. B. Hope, “Swimming pool identification from digital sensor imagery using SVM,” Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007, pp. 523–527, 2007, doi: 10.1109/DICTA.2007.4426841.
  • W. Passos, E. Silva, S. Netto, J. Martins, Y. Costa, G. Araujo, A. Lima, “Detecção de Potenciais Focos do Aedes aegypti em Vídeos Aéreos Usando Redes Neurais,” pp. 22–25, 2020, doi: 10.14209/sbrt.2020.1570661555.
  • P. C. Gray, K. C. Bierlich, S. A. Mantell, A. S. Friedlaender, J. A. Goldbogen, D. W. Johnston, “Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry,” Methods in Ecology and Evolution, vol. 10, no. 9, pp. 1490–1500, 2019, doi: 10.1111/2041-210X.13246.
  • M. I. Habibie, T. Ahamed, R. Noguchi, S. Matsushita, “Deep Learning Algorithms to determine Drought prone Areas Using Remote Sensing and GIS,” pp. 69– 73, 2020, doi: 10.1109/AGERS51788.2020.9452752.
  • I. Goodfellow, Y. Bengio, A. Courville, Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.
  • R. Girshick, J. Donahue, T. Darrell, J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
  • R. Girshick, “Fast R-CNN,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 Inter, pp. 1440–1448, 2015, doi: 10.1109/ICCV.2015.169.
  • H. Rampersad, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” Total Performance Scorecard, pp. 159–183, 2020, doi: 10.4324/9780080519340-12.
  • K. He, G. Gkioxari, P. Dollár, R. Girshick, “Mask r- cnn,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 386–397, 2020, doi: 10.1109/TPAMI.2018.2844175.
  • R. Girshick, I. Radosavovic, G. Gkioxari, P. Dollár, K. He, “Detectron.” https://github.com/ facebookresearch/detectron, 2018.
  • Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, R. Girshick, “Detectron2.” https://github.com/ facebookresearch/detectron2, 2019.
  • W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, “SSD: Single Shot MultiBox Detector,” 12 2015, doi: 10.1007/978-3-319-46448-0_2.
  • J. Redmon, S. Divvala, R. Girshick, A. Farhadi, “You only look once: Unified, real-time object detection,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016- Decem, pp. 779–788, 2016, doi: 10.1109/CVPR.2016.91.
  • J. Redmon, A. Farhadi, “YOLO9000: Better, faster, stronger,” Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 6517–6525, 2017, doi: 10.1109/CVPR.2017.690.
  • J. Redmon, A. Farhadi, “Yolov3: An incremental improvement,” CoRR, vol. abs/1804.02767, 2018, doi: http://arxiv.org/abs/1804.02767.
  • A. Bochkovskiy, C. Wang, H. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” CoRR, vol. abs/2004.10934, 2020, doi: https://arxiv.org/abs/2004.10934.
  • S.-H. Chen, C.-W. Wang, I.-H. Tai, K.-P. Weng, Y.-Chen, K.-S. Hsieh, “Modified yolov4-densenet algorithm for detection of ventricular septal defects in ultrasound images,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 7, pp. 101-108, doi: 10.9781/ijimai.2021.06.001.
  • L. A. Silva, H. S. S. Blas, D. P. García, A. S. Mendes, G. V. González, “An architectural multi-agent system for a pavement monitoring system with pothole recognition in uav images,” Sensors, vol. 20, no. 21, pp. 1–23, 2020, doi: 10.3390/s20216205.
  • C. Galindo, P. Moreno, J. Gonzalez, V. Arévalo, “Swimming pools localization in colour high- resolution satellite images,” vol. 4, pp. IV–510–IV–513, 2009, doi: 10.1109/IGARSS.2009.5417425.
  • M. Kim, J. B. Holt, R. J. Eisen, K. Padgett, W. K. Reisen, J. B. Croft, “Detection of swimming pools by geographic object-based image analysis to support west nile virus control efforts,” Photogrammetric Engineering and Remote Sensing, vol. 77, no. 11, pp. 103– 113, 2011, doi: 10.14358/pers.77.11.1169.
  • B. Rodríguez-Cuenca, M. C. Alonso, “Semi-automatic detection of swimming pools from aerial high- resolution images and LIDAR data,” Remote Sensing, vol. 6, no. 4, pp. 2628–2646, 2014, doi: 10.3390/rs6042628.
  • C. Ferner, G. Eibl, A. Unterweger, S. Burkhart, S. Wegenkittl, “Pool detection from smart metering data with convolutional neural networks,” Energy Informatics, vol. 2, pp. 1–9, 2019, doi: 10.1186/s42162-019-0097-8.
  • Z. Domozi, A. Molnar, “Surveying private pools in suburban areas with neural network based on drone photos,” EUROCON 2019 - 18th International Conference on Smart Technologies, pp. 1–6, 2019, doi: 10.1109/EUROCON.2019.8861770.
  • B. Lima, L. Ferreira, J. M. Moura, “Helping to detect legal swimming pools with deep learning and data visualization,” Procedia Computer Science, vol. 181, no. 2019, pp. 1058–1065, 2021, doi: 10.1016/j.procs.2021.01.301.
  • C. Zato, G. Villarrubia, A. Sanchez, I. Barri, E. Rubión, A. Fernández, C. Sánchez, J. Cabo, T. Álamos, J. Sanz, J. Seco, J. Bajo, J. Corchado Rodríguez, “Pangea - platform for automatic construction of organizations of intelligent agents,” vol. 151, 01 2012, doi: 10.1007/978- 3-642-28765-7_27.
  • J. Schwartz, et al., “Bing maps tile system,” 2009. http://msdn.microsoft.com/en-us/library/ bb259689.aspx, (accessed in: 13/09/2021).
  • “Google maps.” https://maps.google.com, (accessed in: 13/09/2021).
  • “Open street maps.” https://www.openstreetmap. org/, (accessed in: 13/09/2021).
  • “Esri world imagery.” https://www.arcgis. com/apps/mapviewer/index.html?layers= 10df2279f9684e4a9f6a7f08febac2a9, (accessed in: 13/09/2021).
  • “Wikimedia maps.” https://maps.wikimedia.org/, (accessed in: 13/09/2021).
  • “Nasa gibs.” https://map1.vis.earthdata.nasa.gov/, (accessed in: 13/09/2021).
  • “Carto light.” https://cartodb-basemaps-c. global.ssl.fastly.net/, (accessed in: 13/09/2021).
  • “Stamen toner b & w.” https://stamen.com/, (accessed in: 13/09/2021).
  • “Sentinel.” https://www.sentinel-hub.com/, (accessed in: 13/09/2021).
  • “Roboflow.” https://roboflow.com, (accessed in: 13/09/2021).
  • “Swimming Pool Detect.” https://github.com/ Hectorssb/SwimmingPoolDetection, (accessed in: 02/11/2022).
  • “YoloV4 cfg.” https://github.com/Hectorssb/ SwimmingPoolDetection/blob/main/Yolov4/cfg/ yolov4-obj.cfg, (accessed in: 02/11/2022).
  • “Train Mask-RCNN Model on Custom Data.” https://colab.research.google.com/drive/1rBuhT8AjP2td20otdUnpuF7CCMmjRb4O, (accessed in: 13/09/2021).
  • M. Everingham, L. Van Gool, C. K. I. Williams, Winn, A. Zisserman, “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results.” http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html.
  • “Detectron2 Beginner’s Tutorial.” https://colab.research.google.com/drive/1n-_ nulKMxxCF6Jg4WMw20mO8R6rqC078, (accessed in: 13/09/2021).
  • T.-Y. Lin, M. Maire, S. Belongie, L. Bourdev, R. Girshick, J. Hays, P. Perona, D. Ramanan, C. L. Zitnick, P. Dollár, “Microsoft coco: Common objects in context,” 2014. [Online]. Available: https://arxiv.org/abs/1405.0312, doi: 10.48550/ARXIV.1405.0312.
  • Detectron2, “faster_rcnn_X_101_32x8d_FPN_3x.” https://github.com/facebookresearch/ detectron2/blob/main/configs/COCO-Detection/ faster_rcnn_X_101_32x8d_FPN_3x.yaml, (accessed in: 13/09/2021).
Data source: Dialnet