{"help": "https://pro.iepnb.gob.es/catalogo/api/3/action/help_show?name=package_show", "success": true, "result": {"access_rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "alternate_identifier": "ISBN: 978-972-778-182-9", "author": "Ribeiro, H., Sillero, N. y Guedes, D.", "author_name": "Ribeiro, H., Sillero, N. y Guedes, D.", "classification_variables": {"es": ""}, "conforms_to": ["https://www.boe.es/eli/es/res/2013/02/19/(4)"], "contact_email": "organismo@example.org", "contact_name": "Organismo publicador del Cat\u00e1logo", "contact_role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "contact_uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "contact_url": "https://organismo.example.org/", "created": "2025-05-23", "creator_user_id": "d24a314a-79ce-48c0-abd9-54cb42706da4", "dataset_scope": "non_spatial_dataset", "dcat_type": "http://purl.org/dc/dcmitype/Text", "encoding": "UTF-8", "featured": false, "graphic_overview": "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRb54JGfwMSPjkYlDyIL_yeW3XeePRl4EebQDu0igf2yw&s", "hvd": "non_hvd", "id": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", "identifier": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", "inspire_id": "", "isopen": true, "language": "http://publications.europa.eu/resource/authority/language/SPA", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "lineage_process_steps": [], "lineage_source": ["2020 IENE International Conference. Abstract book. Vol. 4.1.2", "Num. 3", "pag. 73"], "maintainer": "", "maintainer_name": "", "metadata_created": "2026-06-23T14:47:24.576246", "metadata_modified": "2026-06-25T12:25:15.164175", "metadata_profile": ["https://www.w3.org/TR/vocab-dcat-3/"], "miteco_data_population": {"es": ""}, "miteco_data_territory": {"es": ""}, "miteco_dataset_type": "http://publications.europa.eu/resource/authority/dataset-type/STATISTICAL", "miteco_geo_level": "1", "modified": "2026-06-25", "name": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", "notes": "Roads affect negatively wildlife, from direct mortality to habitat fragmentation. Mortality caused by collision with vehicles on roads is a major threat to many species. Monitoring animal road-kills is essential to stablish correct road mitigation measures. Many countries have national monitoring systems for identifying mortality hotspots. We present here an improved version of the mobile mapping system (MMS2) for detecting Roadkills not only for amphibians but small birds as well. It is composed by two stereo multi-spectral and high definition camera (ZED), a high-power processing laptop, a GPS device connected to the laptop, and a small support device attachable to the back of any vehicle. The system is controlled by several applications that manage all the video recording steps as well as the GPS acquisition, merging everything in a single final file, ready to be examine by an algorithm at posterior. We used the state-of-the-art machine learning computer vision algorithm (CNN: Convolutional Neural Network) to automatically detect animals on roads. This self-learning algorithm needs a large number of images with alive animals, road-killed animals and any objects likely to be found on roads (e.g. garbage thrown away by drivers) in order to be trained. The greater the image database, the greater the detection efficiency. This improved version of the mobile mapping system presents very good results. The algorithm has a good effectiveness in detecting small birds and amphibians.", "notes_translated": {"en": "Roads affect negatively wildlife, from direct mortality to habitat fragmentation. Mortality caused by collision with vehicles on roads is a major threat to many species. Monitoring animal road-kills is essential to stablish correct road mitigation measures. Many countries have national monitoring systems for identifying mortality hotspots. We present here an improved version of the mobile mapping system (MMS2) for detecting Roadkills not only for amphibians but small birds as well. It is composed by two stereo multi-spectral and high definition camera (ZED), a high-power processing laptop, a GPS device connected to the laptop, and a small support device attachable to the back of any vehicle. The system is controlled by several applications that manage all the video recording steps as well as the GPS acquisition, merging everything in a single final file, ready to be examine by an algorithm at posterior. We used the state-of-the-art machine learning computer vision algorithm (CNN: Convolutional Neural Network) to automatically detect animals on roads. This self-learning algorithm needs a large number of images with alive animals, road-killed animals and any objects likely to be found on roads (e.g. garbage thrown away by drivers) in order to be trained. The greater the image database, the greater the detection efficiency. This improved version of the mobile mapping system presents very good results. The algorithm has a good effectiveness in detecting small birds and amphibians.", "es": "Roads affect negatively wildlife, from direct mortality to habitat fragmentation. Mortality caused by collision with vehicles on roads is a major threat to many species. Monitoring animal road-kills is essential to stablish correct road mitigation measures. Many countries have national monitoring systems for identifying mortality hotspots. We present here an improved version of the mobile mapping system (MMS2) for detecting Roadkills not only for amphibians but small birds as well. It is composed by two stereo multi-spectral and high definition camera (ZED), a high-power processing laptop, a GPS device connected to the laptop, and a small support device attachable to the back of any vehicle. The system is controlled by several applications that manage all the video recording steps as well as the GPS acquisition, merging everything in a single final file, ready to be examine by an algorithm at posterior. We used the state-of-the-art machine learning computer vision algorithm (CNN: Convolutional Neural Network) to automatically detect animals on roads. This self-learning algorithm needs a large number of images with alive animals, road-killed animals and any objects likely to be found on roads (e.g. garbage thrown away by drivers) in order to be trained. The greater the image database, the greater the detection efficiency. This improved version of the mobile mapping system presents very good results. The algorithm has a good effectiveness in detecting small birds and amphibians."}, "num_resources": 1, "num_tags": 2, "organization": {"id": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "name": "iepnb", "title": "", "type": "organization", "description": "", "image_url": "", "created": "2026-06-23T14:36:08.942021", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "private": false, "provenance": {"en": "", "es": ""}, "publisher_email": "buzon-bdatos@miteco.es", "publisher_name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto Demogr\u00e1fico", "publisher_type": "http://purl.org/adms/publishertype/NationalAuthority", "publisher_uri": "https://iepnb.es/catalogo/organization/iepnb", "publisher_url": "https://www.miteco.gob.es/", "purpose": {"en": "", "es": ""}, "reference": [], "schemingdcat_xls_metadata_template": false, "source": "", "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}", "spatial_resolution_in_meters": "", "spatial_uri": "http://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a", "state": "active", "study_variables": {"es": ""}, "tag_uri": ["http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment"], "thematic_area": ["especies_silvestres"], "theme_es": ["http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"], "title": "Mobile mapping system (MMS2) for detecting roadkills.", "title_translated": {"en": "", "es": "Mobile mapping system (MMS2) for detecting roadkills."}, "topic": "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", "type": "dataset", "url": "https://iepnb.es:443/catalogo/dataset/f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", "version": "", "version_notes": {"en": "", "es": ""}, "contact": [{"email": "organismo@example.org", "name": "Organismo publicador del Cat\u00e1logo", "role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "url": "https://organismo.example.org/"}], "creator": [{"name": "Ribeiro, H., Sillero, N. y Guedes, D."}], "groups": [{"description": "La fragmentaci\u00f3n del h\u00e1bitat se define como el proceso durante el cual una gran extensi\u00f3n de h\u00e1bitat se transforma en una serie de parches m\u00e1s peque\u00f1os de menor superficie total aislados entre s\u00ed por una matriz de h\u00e1bitats distinta de la original", "display_name": "Fragmentaci\u00f3n del h\u00e1bitat", "id": "10439641-6830-4144-a3ab-117add931cd5", "image_display_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/Green_infrastructure_2010_UE_Illustr.jpg/320px-Green_infrastructure_2010_UE_Illustr.jpg", "name": "fragmentacion-habitat", "title": "Fragmentaci\u00f3n del h\u00e1bitat"}], "publisher": [{"email": "buzon-bdatos@miteco.es", "name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto Demogr\u00e1fico", "type": "http://purl.org/adms/publishertype/NationalAuthority", "uri": "https://iepnb.es/catalogo/organization/iepnb", "url": "https://www.miteco.gob.es/"}], "resources": [{"access_url": "https://iepnb.es:443/catalogo/dataset/f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f/resource/6a59a17c-c9f9-49f9-be40-f831f7f16b29", "availability": "http://publications.europa.eu/resource/authority/planned-availability/AVAILABLE", "cache_last_updated": null, "cache_url": null, "created": "2021-01-14T00:00:00", "dataset_id": "aa4616fd-f672-43ee-b1f8-590ddc670c75", "datastore_active": false, "datastore_contains_all_records_of_source_file": false, "description": "Art\u00edculo en libro", "download_url": "https://www.iene.info/content/uploads/iene2020-abstract-book.pdf", "encoding": "UTF-8", "format": "HTML", "hash": "", "id": "6a59a17c-c9f9-49f9-be40-f831f7f16b29", "issued": "", "language": "http://publications.europa.eu/resource/authority/language/ENG", "last_modified": null, "license": "http://creativecommons.org/licenses/by/4.0/", "license_id": "cc-by", "metadata_modified": "2026-06-25T12:25:15.168221", "mimetype": "https://www.iana.org/assignments/media-types/text/html", "mimetype_inner": null, "modified": "2026-06-25", "name": "Acceso al recurso", "package_id": "f8fb6d24-5bc3-54b5-9cd8-0a386c43ea0f", "position": 0, "protocol": null, "resource_type": null, "rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "size": 0, "state": "active", "status": "http://purl.org/adms/status/Completed", "url": "https://www.iene.info/content/uploads/iene2020-abstract-book.pdf", "url_type": null}], "spatial_coverage": [{"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": "Espa\u00f1a", "uri": "http://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a"}], "tags": [{"display_name": "accidentes-y-atropellos-de-fauna", "id": "629c6d17-b04f-404c-900a-8c31fae0ecc4", "name": "accidentes-y-atropellos-de-fauna", "state": "active", "vocabulary_id": null}, {"display_name": "fragmentacion", "id": "627f9c0a-a199-4c69-b563-0c8d626e3194", "name": "fragmentacion", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}}