Advertisement

A road mobile mapping device for supervised classification of amphibians on roads

  • Neftalí SilleroEmail author
  • Hélder Ribeiro
  • Marc Franch
  • Cristiano Silva
  • Gil Lopes
Original Article
Part of the following topical collections:
  1. Road Ecology

Abstract

We present the classification results of a supervised algorithm of road images containing amphibians. We used a prototype of a mobile mapping system composed of a scanning system attached to a traction vehicle capable of recording road surface images at speed up to 30 km/h. We tested the algorithm in three test situations (two control and one real): with plastic models of amphibians; with dead specimens of amphibians; and with real specimens of amphibians in a road survey. The classification results of the algorithm changed among tests, but in any case, it was able to detect more than 80% of the amphibians (more than 90% in control tests). Unfortunately, the algorithm presented as well a high rate of false-positive detections, varying from 80% in the real test to 14% in the control test with dead specimens. The Mobile Mapping Systems (MMS) is ideal for passive surveys and can work by day or night. This is the first study presenting an automatic solution to detect amphibians on roads. The classification algorithm can be adapted to any animal group. Robotics and computer vision are opening new horizons for wildlife conservation.

Keywords

Robotics Computer vision Conservation Road ecology Mobile Mapping System 

Notes

Funding information

This work is funded by the Life LINES project LIFE14 NAT/PT/001081. Previous work was financed by FEDER Funds, through the Operational Programme for Competitiveness Factors—COMPETE, and by National Funds through FCT—Foundation for Science and Technology of Portugal, under the project PTDC/BIA/BIC/4296/2012: Roadkills – Intelligent systems for mapping amphibian mortality on Portuguese roads. NS is supported by an IF contract by FCT (IF/01526/2013). HR and MF are supported by research grants by Life LINES. CS and MF were supported by research grants by FCT (UMINHO/BI/172/2013 and UMINHO/BI/175/2013 respectively).

Supplementary material

10344_2018_1236_MOESM1_ESM.docx (1 mb)
ESM 1 (DOCX 1058 kb)

References

  1. Bossler J, Goad C, Johnson P, Novak K (1991) GPS and GIS map of the national highways. GeoInfo Systems Magazine, pp 26–37Google Scholar
  2. Bradski GR, Pisarevsky V (2000) Intel’s computer vision library: applications in calibration, stereo, segmentation, tracking, gesture, face and object recognition. IEEE Computer Vision and Pattern Recognition (CVPR) II, pp 796–797Google Scholar
  3. Carretero MA, Rosell C (2000) Incidencia del atropello de anfibios, reptiles y otros vertebrados en un tramo de carretera de construcción reciente. Bol Asoc Herpetol Esp 1:39–43Google Scholar
  4. Ceia-Hasse A, Borda-de-Água L, Grilo C, Pereira HM (2017) Global exposure of carnivores to roads. Glob Ecol Biogeogr 26:592–600CrossRefGoogle Scholar
  5. Clarke GP, White PCL, Harris S (1998) Effects of roads on badger Meles meles populations in south-west England. Biol Conserv 86(2):117–124CrossRefGoogle Scholar
  6. El-Sheimy N (1996) A mobile multi-sensor system for GIS applications in urban centers. Int Arch Photogramm Remote Sens 31:95–100Google Scholar
  7. Forman RTT, Alexander LE (1998) Roads and their major ecological effects. Annu Rev Ecol Syst 29(1):207–231CrossRefGoogle Scholar
  8. Forman RTT, Deblinger RD (2000) The ecological road-effect zone of a Massachusetts (U.S.A.) suburban highway. Conserv Biol 14(1):36–46CrossRefGoogle Scholar
  9. Franch M, Silva C, Lopes G, Ribeiro F, Trigueiros P, Seco L, Sillero N (2015) Where to look when identifying roadkilled amphibians? Acta Herpetol 10:103–110Google Scholar
  10. Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. European conference on computational learning theory. Springer, Berlin, pp 23–37Google Scholar
  11. Hauer S, Ansorge H, Zinke O (2002) Mortality patterns of otters (Lutra lutra) from eastern Germany. J Zool 256(3):361–368CrossRefGoogle Scholar
  12. Hels T, Buchwald E (2001) The effect of road kills on amphibian populations. Biol Conserv 99(3):331–340CrossRefGoogle Scholar
  13. Ibisch P, Hoffmann MT, Kreft S, Pe’er G, Kati V, Biber-Freudenberger L, DellaSala DA, Vale MM, Hobson PR, Selva N (2016) A global map of roadless areas and their conservation status. Science 354:1423–1427CrossRefGoogle Scholar
  14. Lopes G, Ribeiro A, Sillero N, Gonçalves-Seco L, Silva C, Franch M, Trigueiros P (2016) High resolution trichromatic road surface scanning with a line scan camera and light emitting diode lighting for road-kill detection. Sensors 16:1–16CrossRefGoogle Scholar
  15. Malo JE, Suáez F, Díez A (2004) Can we mitigate animal-vehicle accidents using predictive models? J Appl Ecol 41(4):701–710CrossRefGoogle Scholar
  16. Matos C, Sillero N, Argaña E (2012) Spatial analysis of amphibian road mortality levels in northern Portugal country roads. Amphibia-Reptilia 33:469–483CrossRefGoogle Scholar
  17. Matos C, Sillero N, Argaña E (2013) Spatial analysis of amphibian road mortality levels in northern Portugal country roads. FrogLog 469:2013Google Scholar
  18. Petrie G (2010) An introduction to the technology mobile mapping systems. GEOInformatics Magazine 13:32–43Google Scholar
  19. Santos SM, Carvalho F, Mira A (2011) How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. PLoS One 6:e25383CrossRefGoogle Scholar
  20. Sialat M, Khlifat N, Bremond F, Hamrouni K (2009) People detection in complex scene using a cascade of boosted classifiers based on Haar-like Features. In: Proc. IEEE Int. Symposium on Intelligent Vehicles, pp 83–87Google Scholar
  21. Sillero N (2008) Amphibian mortality levels on Spanish country roads: descriptive and spatial analysis. Amphibia-Reptilia 29:337–347CrossRefGoogle Scholar
  22. Spellerberg IF (1998) Ecological effects of roads and traffic: a literature review. Glob Ecol Biogeogr:317–333Google Scholar
  23. Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51CrossRefGoogle Scholar
  24. Trigueiros P, Ribeiro F, Lopes G (2011) Vision-based hand segmentation techniques for human-robot interaction for real-time applications. VIPIMAGE, III Eccomas thematic conference on computational vision and medical image processingGoogle Scholar
  25. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In Computer vision and pattern recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE computer society conference 1: I–IGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.CICGE Centro de Investigação em Ciências Geo-Espaciais, Observatório Astronómico Prof. Manuel de Barros, Alameda do Monte da VirgemFaculdade de Ciências da Universidade do Porto (FCUP)Vila Nova de GaiaPortugal
  2. 2.School of Engineering of the University of MinhoGuimarãesPortugal

Personalised recommendations