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Modificado el valor del campo
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a2026-06-25
en Mobile mapping system (MMS2) for detecting roadkills. -
Modificado el valor del campo
modified
del recurso Acceso al recurso a2026-06-25
(anteriormente2026-06-23
) en Mobile mapping system (MMS2) for detecting roadkills.
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| 78 | "name": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", | 92 | "name": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", | ||
| 79 | "notes": "Roads affect negatively wildlife, from direct mortality to | 93 | "notes": "Roads affect negatively wildlife, from direct mortality to | ||
| 80 | habitat fragmentation. Mortality caused by collision with vehicles on | 94 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 81 | roads is a major threat to many species. Monitoring animal road-kills | 95 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 82 | is essential to stablish correct road mitigation measures. Many | 96 | is essential to stablish correct road mitigation measures. Many | ||
| 83 | countries have national monitoring systems for identifying mortality | 97 | countries have national monitoring systems for identifying mortality | ||
| 84 | hotspots. We present here an improved version of the mobile mapping | 98 | hotspots. We present here an improved version of the mobile mapping | ||
| 85 | system (MMS2) for detecting Roadkills not only for amphibians but | 99 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 86 | small birds as well. It is composed by two stereo multi-spectral and | 100 | small birds as well. It is composed by two stereo multi-spectral and | ||
| 87 | high definition camera (ZED), a high-power processing laptop, a GPS | 101 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 88 | device connected to the laptop, and a small support device attachable | 102 | device connected to the laptop, and a small support device attachable | ||
| 89 | to the back of any vehicle. The system is controlled by several | 103 | to the back of any vehicle. The system is controlled by several | ||
| 90 | applications that manage all the video recording steps as well as the | 104 | applications that manage all the video recording steps as well as the | ||
| 91 | GPS acquisition, merging everything in a single final file, ready to | 105 | GPS acquisition, merging everything in a single final file, ready to | ||
| 92 | be examine by an algorithm at posterior. We used the state-of-the-art | 106 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 93 | machine learning computer vision algorithm (CNN: Convolutional Neural | 107 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 94 | Network) to automatically detect animals on roads. This self-learning | 108 | Network) to automatically detect animals on roads. This self-learning | ||
| 95 | algorithm needs a large number of images with alive animals, | 109 | algorithm needs a large number of images with alive animals, | ||
| 96 | road-killed animals and any objects likely to be found on roads (e.g. | 110 | road-killed animals and any objects likely to be found on roads (e.g. | ||
| 97 | garbage thrown away by drivers) in order to be trained. The greater | 111 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 98 | the image database, the greater the detection efficiency. This | 112 | the image database, the greater the detection efficiency. This | ||
| 99 | improved version of the mobile mapping system presents very good | 113 | improved version of the mobile mapping system presents very good | ||
| 100 | results. The algorithm has a good effectiveness in detecting small | 114 | results. The algorithm has a good effectiveness in detecting small | ||
| 101 | birds and amphibians.", | 115 | birds and amphibians.", | ||
| 102 | "notes_translated": { | 116 | "notes_translated": { | ||
| 103 | "en": "Roads affect negatively wildlife, from direct mortality to | 117 | "en": "Roads affect negatively wildlife, from direct mortality to | ||
| 104 | habitat fragmentation. Mortality caused by collision with vehicles on | 118 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 105 | roads is a major threat to many species. Monitoring animal road-kills | 119 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 106 | is essential to stablish correct road mitigation measures. Many | 120 | is essential to stablish correct road mitigation measures. Many | ||
| 107 | countries have national monitoring systems for identifying mortality | 121 | countries have national monitoring systems for identifying mortality | ||
| 108 | hotspots. We present here an improved version of the mobile mapping | 122 | hotspots. We present here an improved version of the mobile mapping | ||
| 109 | system (MMS2) for detecting Roadkills not only for amphibians but | 123 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 110 | small birds as well. It is composed by two stereo multi-spectral and | 124 | small birds as well. It is composed by two stereo multi-spectral and | ||
| 111 | high definition camera (ZED), a high-power processing laptop, a GPS | 125 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 112 | device connected to the laptop, and a small support device attachable | 126 | device connected to the laptop, and a small support device attachable | ||
| 113 | to the back of any vehicle. The system is controlled by several | 127 | to the back of any vehicle. The system is controlled by several | ||
| 114 | applications that manage all the video recording steps as well as the | 128 | applications that manage all the video recording steps as well as the | ||
| 115 | GPS acquisition, merging everything in a single final file, ready to | 129 | GPS acquisition, merging everything in a single final file, ready to | ||
| 116 | be examine by an algorithm at posterior. We used the state-of-the-art | 130 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 117 | machine learning computer vision algorithm (CNN: Convolutional Neural | 131 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 118 | Network) to automatically detect animals on roads. This self-learning | 132 | Network) to automatically detect animals on roads. This self-learning | ||
| 119 | algorithm needs a large number of images with alive animals, | 133 | algorithm needs a large number of images with alive animals, | ||
| 120 | road-killed animals and any objects likely to be found on roads (e.g. | 134 | road-killed animals and any objects likely to be found on roads (e.g. | ||
| 121 | garbage thrown away by drivers) in order to be trained. The greater | 135 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 122 | the image database, the greater the detection efficiency. This | 136 | the image database, the greater the detection efficiency. This | ||
| 123 | improved version of the mobile mapping system presents very good | 137 | improved version of the mobile mapping system presents very good | ||
| 124 | results. The algorithm has a good effectiveness in detecting small | 138 | results. The algorithm has a good effectiveness in detecting small | ||
| 125 | birds and amphibians.", | 139 | birds and amphibians.", | ||
| 126 | "es": "Roads affect negatively wildlife, from direct mortality to | 140 | "es": "Roads affect negatively wildlife, from direct mortality to | ||
| 127 | habitat fragmentation. Mortality caused by collision with vehicles on | 141 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 128 | roads is a major threat to many species. Monitoring animal road-kills | 142 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 129 | is essential to stablish correct road mitigation measures. Many | 143 | is essential to stablish correct road mitigation measures. Many | ||
| 130 | countries have national monitoring systems for identifying mortality | 144 | countries have national monitoring systems for identifying mortality | ||
| 131 | hotspots. We present here an improved version of the mobile mapping | 145 | hotspots. We present here an improved version of the mobile mapping | ||
| 132 | system (MMS2) for detecting Roadkills not only for amphibians but | 146 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 133 | small birds as well. It is composed by two stereo multi-spectral and | 147 | small birds as well. It is composed by two stereo multi-spectral and | ||
| 134 | high definition camera (ZED), a high-power processing laptop, a GPS | 148 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 135 | device connected to the laptop, and a small support device attachable | 149 | device connected to the laptop, and a small support device attachable | ||
| 136 | to the back of any vehicle. The system is controlled by several | 150 | to the back of any vehicle. The system is controlled by several | ||
| 137 | applications that manage all the video recording steps as well as the | 151 | applications that manage all the video recording steps as well as the | ||
| 138 | GPS acquisition, merging everything in a single final file, ready to | 152 | GPS acquisition, merging everything in a single final file, ready to | ||
| 139 | be examine by an algorithm at posterior. We used the state-of-the-art | 153 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 140 | machine learning computer vision algorithm (CNN: Convolutional Neural | 154 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 141 | Network) to automatically detect animals on roads. This self-learning | 155 | Network) to automatically detect animals on roads. This self-learning | ||
| 142 | algorithm needs a large number of images with alive animals, | 156 | algorithm needs a large number of images with alive animals, | ||
| 143 | road-killed animals and any objects likely to be found on roads (e.g. | 157 | road-killed animals and any objects likely to be found on roads (e.g. | ||
| 144 | garbage thrown away by drivers) in order to be trained. The greater | 158 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 145 | the image database, the greater the detection efficiency. This | 159 | the image database, the greater the detection efficiency. This | ||
| 146 | improved version of the mobile mapping system presents very good | 160 | improved version of the mobile mapping system presents very good | ||
| 147 | results. The algorithm has a good effectiveness in detecting small | 161 | results. The algorithm has a good effectiveness in detecting small | ||
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