@prefix adms: <http://www.w3.org/ns/adms#> .
@prefix cnt: <http://www.w3.org/2011/content#> .
@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dcatap: <http://data.europa.eu/r5r/> .
@prefix dcmitype: <http://purl.org/dc/dcmitype/> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix gsp: <http://www.opengis.net/ont/geosparql#> .
@prefix locn: <http://www.w3.org/ns/locn#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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    dct:description "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 …"@en,
        """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
 Palabras clave: Amphibian"""@es ;
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