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En el instante 23 de junio de 2026, 15:46:29 UTC,
-
Modificado el valor del campo
spatial_coverage
a[{'bbox': '{"type": "Polygon", "coordinates": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}', 'centroid': '{"type": "Point", "coordinates": [-6.92, 35.715]}', 'text': 'España', 'uri': 'http://datos.gob.es/recurso/sector-publico/territorio/Pais/España'}]
en Mobile mapping system (MMS2) for detecting roadkills.
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| 57 | "2020 IENE International Conference. Abstract book. Vol. 4.1.2", | 57 | "2020 IENE International Conference. Abstract book. Vol. 4.1.2", | ||
| 58 | "Num. 3", | 58 | "Num. 3", | ||
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| 77 | "modified": "2026-06-23", | 77 | "modified": "2026-06-23", | ||
| 78 | "name": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", | 78 | "name": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", | ||
| 79 | "notes": "Roads affect negatively wildlife, from direct mortality to | 79 | "notes": "Roads affect negatively wildlife, from direct mortality to | ||
| 80 | habitat fragmentation. Mortality caused by collision with vehicles on | 80 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 81 | roads is a major threat to many species. Monitoring animal road-kills | 81 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 82 | is essential to stablish correct road mitigation measures. Many | 82 | is essential to stablish correct road mitigation measures. Many | ||
| 83 | countries have national monitoring systems for identifying mortality | 83 | countries have national monitoring systems for identifying mortality | ||
| 84 | hotspots. We present here an improved version of the mobile mapping | 84 | hotspots. We present here an improved version of the mobile mapping | ||
| 85 | system (MMS2) for detecting Roadkills not only for amphibians but | 85 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 86 | small birds as well. It is composed by two stereo multi-spectral and | 86 | 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 | 87 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 88 | device connected to the laptop, and a small support device attachable | 88 | device connected to the laptop, and a small support device attachable | ||
| 89 | to the back of any vehicle. The system is controlled by several | 89 | 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 | 90 | applications that manage all the video recording steps as well as the | ||
| 91 | GPS acquisition, merging everything in a single final file, ready to | 91 | 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 | 92 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 93 | machine learning computer vision algorithm (CNN: Convolutional Neural | 93 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 94 | Network) to automatically detect animals on roads. This self-learning | 94 | Network) to automatically detect animals on roads. This self-learning | ||
| 95 | algorithm needs a large number of images with alive animals, | 95 | 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. | 96 | 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 | 97 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 98 | the image database, the greater the detection efficiency. This | 98 | the image database, the greater the detection efficiency. This | ||
| 99 | improved version of the mobile mapping system presents very good | 99 | improved version of the mobile mapping system presents very good | ||
| 100 | results. The algorithm has a good effectiveness in detecting small | 100 | results. The algorithm has a good effectiveness in detecting small | ||
| 101 | birds and amphibians.", | 101 | birds and amphibians.", | ||
| 102 | "notes_translated": { | 102 | "notes_translated": { | ||
| 103 | "en": "Roads affect negatively wildlife, from direct mortality to | 103 | "en": "Roads affect negatively wildlife, from direct mortality to | ||
| 104 | habitat fragmentation. Mortality caused by collision with vehicles on | 104 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 105 | roads is a major threat to many species. Monitoring animal road-kills | 105 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 106 | is essential to stablish correct road mitigation measures. Many | 106 | is essential to stablish correct road mitigation measures. Many | ||
| 107 | countries have national monitoring systems for identifying mortality | 107 | countries have national monitoring systems for identifying mortality | ||
| 108 | hotspots. We present here an improved version of the mobile mapping | 108 | hotspots. We present here an improved version of the mobile mapping | ||
| 109 | system (MMS2) for detecting Roadkills not only for amphibians but | 109 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 110 | small birds as well. It is composed by two stereo multi-spectral and | 110 | 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 | 111 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 112 | device connected to the laptop, and a small support device attachable | 112 | device connected to the laptop, and a small support device attachable | ||
| 113 | to the back of any vehicle. The system is controlled by several | 113 | 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 | 114 | applications that manage all the video recording steps as well as the | ||
| 115 | GPS acquisition, merging everything in a single final file, ready to | 115 | 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 | 116 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 117 | machine learning computer vision algorithm (CNN: Convolutional Neural | 117 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 118 | Network) to automatically detect animals on roads. This self-learning | 118 | Network) to automatically detect animals on roads. This self-learning | ||
| 119 | algorithm needs a large number of images with alive animals, | 119 | 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. | 120 | 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 | 121 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 122 | the image database, the greater the detection efficiency. This | 122 | the image database, the greater the detection efficiency. This | ||
| 123 | improved version of the mobile mapping system presents very good | 123 | improved version of the mobile mapping system presents very good | ||
| 124 | results. The algorithm has a good effectiveness in detecting small | 124 | results. The algorithm has a good effectiveness in detecting small | ||
| 125 | birds and amphibians.", | 125 | birds and amphibians.", | ||
| 126 | "es": "Roads affect negatively wildlife, from direct mortality to | 126 | "es": "Roads affect negatively wildlife, from direct mortality to | ||
| 127 | habitat fragmentation. Mortality caused by collision with vehicles on | 127 | habitat fragmentation. Mortality caused by collision with vehicles on | ||
| 128 | roads is a major threat to many species. Monitoring animal road-kills | 128 | roads is a major threat to many species. Monitoring animal road-kills | ||
| 129 | is essential to stablish correct road mitigation measures. Many | 129 | is essential to stablish correct road mitigation measures. Many | ||
| 130 | countries have national monitoring systems for identifying mortality | 130 | countries have national monitoring systems for identifying mortality | ||
| 131 | hotspots. We present here an improved version of the mobile mapping | 131 | hotspots. We present here an improved version of the mobile mapping | ||
| 132 | system (MMS2) for detecting Roadkills not only for amphibians but | 132 | system (MMS2) for detecting Roadkills not only for amphibians but | ||
| 133 | small birds as well. It is composed by two stereo multi-spectral and | 133 | 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 | 134 | high definition camera (ZED), a high-power processing laptop, a GPS | ||
| 135 | device connected to the laptop, and a small support device attachable | 135 | device connected to the laptop, and a small support device attachable | ||
| 136 | to the back of any vehicle. The system is controlled by several | 136 | 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 | 137 | applications that manage all the video recording steps as well as the | ||
| 138 | GPS acquisition, merging everything in a single final file, ready to | 138 | 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 | 139 | be examine by an algorithm at posterior. We used the state-of-the-art | ||
| 140 | machine learning computer vision algorithm (CNN: Convolutional Neural | 140 | machine learning computer vision algorithm (CNN: Convolutional Neural | ||
| 141 | Network) to automatically detect animals on roads. This self-learning | 141 | Network) to automatically detect animals on roads. This self-learning | ||
| 142 | algorithm needs a large number of images with alive animals, | 142 | 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. | 143 | 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 | 144 | garbage thrown away by drivers) in order to be trained. The greater | ||
| 145 | the image database, the greater the detection efficiency. This | 145 | the image database, the greater the detection efficiency. This | ||
| 146 | improved version of the mobile mapping system presents very good | 146 | improved version of the mobile mapping system presents very good | ||
| 147 | results. The algorithm has a good effectiveness in detecting small | 147 | results. The algorithm has a good effectiveness in detecting small | ||
| 148 | birds and amphibians." | 148 | birds and amphibians." | ||
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