[
    {
        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/pubinfo",
        "@graph": [
            {
                "@id": "https://orcid.org/0000-0002-1784-2920",
                "http://xmlns.com/foaf/0.1/name": [
                    {
                        "@value": "Anne Fouilloux"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0",
                "http://purl.org/dc/terms/created": [
                    {
                        "@value": "2026-01-06T10:11:08+00:00",
                        "@type": "http://www.w3.org/2001/XMLSchema#dateTime"
                    }
                ],
                "http://purl.org/dc/terms/creator": [
                    {
                        "@id": "https://orcid.org/0000-0002-1784-2920"
                    }
                ],
                "http://purl.org/dc/terms/license": [
                    {
                        "@id": "https://creativecommons.org/licenses/by/4.0/"
                    }
                ],
                "http://purl.org/nanopub/x/introduces": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/machine-learning-algorithms-for-wildfire-detection"
                    }
                ],
                "http://purl.org/nanopub/x/wasCreatedAt": [
                    {
                        "@id": "https://nanodash.knowledgepixels.com/"
                    }
                ],
                "http://www.w3.org/2000/01/rdf-schema#label": [
                    {
                        "@value": "PICO Research Question: Machine Learning Algorithms for Wildfire Detection and Burned Area Mappin..."
                    }
                ],
                "https://w3id.org/np/o/ntemplate/wasCreatedFromProvenanceTemplate": [
                    {
                        "@id": "https://w3id.org/np/RA7lSq6MuK_TIC6JMSHvLtee3lpLoZDOqLJCLXevnrPoU"
                    }
                ],
                "https://w3id.org/np/o/ntemplate/wasCreatedFromPubinfoTemplate": [
                    {
                        "@id": "https://w3id.org/np/RA0J4vUn_dekg-U1kK3AOEt02p9mT2WO03uGxLDec1jLw"
                    },
                    {
                        "@id": "https://w3id.org/np/RAoTD7udB2KtUuOuAe74tJi1t3VzK0DyWS7rYVAq1GRvw"
                    },
                    {
                        "@id": "https://w3id.org/np/RAukAcWHRDlkqxk7H2XNSegc1WnHI569INvNr-xdptDGI"
                    }
                ],
                "https://w3id.org/np/o/ntemplate/wasCreatedFromTemplate": [
                    {
                        "@id": "https://w3id.org/np/RA5e5XeXy_-aNK5giB7kBAEQslTLVydHeM4YYEzhmEE2w"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/sig",
                "http://purl.org/nanopub/x/hasAlgorithm": [
                    {
                        "@value": "RSA"
                    }
                ],
                "http://purl.org/nanopub/x/hasPublicKey": [
                    {
                        "@value": "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAosxbitQQzLXi1949Zd9JmSkGfYHHlj/CZZ7iiYs1TrZ5/Jk/wGA7kHEv7f9NtsinOdBo9EtHj/jgHE5W2Vv404JbOAY280PvH5Jns5ObWdVZmtHeCw0ZIdPEqNrurrEweKhzcTJW/YRpYWPwVPo47XyIW6IAcmx6gfdtmdPddMpplqExrP6G99ksXfXlZI0InQtZJRSGK5lYLLNzaofFtupPI5OAAGjooDyHijp0Ap2HIXH6WpO4S44cFPKU34pH2xhIY4/XT5DG1X5UoiVHs2Yoo30BHFudj/kAFwdzcy6Yh4tMDaB3ox6p7pi267d7n0y7kypC0Nt+hfgHQ1FpgwIDAQAB"
                    }
                ],
                "http://purl.org/nanopub/x/hasSignature": [
                    {
                        "@value": "KpV6CO4JE6MBySWTsHULx3ctMFogFcuRKb5/7ECwxqXmMzExpw7yiqwkT7QiJMUEXjXdtUNZUWtqbYC+tVvE69m0RibQyOqNauqY1YSLnN3I+0lh05ZObBHQGcWiedQgPFw2zf9eeVSWtVFRA51PNK0LE7Ed1x/bhVWprNTymwd3GmJ1n98hSTAJv5TujRtZEHPB8693rg/mCVirI7Zp60H+yG8AxwckKKXn0fwS0+cNbFpPE2IBzPnUEiuQy9Q74UQwqhAZ8tYG90kugEGDiJq6gZIIjO0eZlOXIZYGUliS9v3dgUO8HqPlk1+acWouH1oRk5tJ4urV75k7C/J56g=="
                    }
                ],
                "http://purl.org/nanopub/x/hasSignatureTarget": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0"
                    }
                ],
                "http://purl.org/nanopub/x/signedBy": [
                    {
                        "@id": "https://orcid.org/0000-0002-1784-2920"
                    }
                ]
            }
        ]
    },
    {
        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/provenance",
        "@graph": [
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/assertion",
                "http://www.w3.org/ns/prov#wasAttributedTo": [
                    {
                        "@id": "https://orcid.org/0000-0002-1784-2920"
                    }
                ]
            }
        ]
    },
    {
        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/assertion",
        "@graph": [
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/comparatorGroup",
                "http://purl.org/dc/terms/description": [
                    {
                        "@value": "Different ML/DL architectures compared against each other; comparison of input data configurations (spectral bands, indices, temporal features); validation approaches (cross-validation, independent test sets, spatial holdout); and where available, comparison with traditional remote sensing methods (thresholding, spectral indices)"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/interventionGroup",
                "http://purl.org/dc/terms/description": [
                    {
                        "@value": "Machine learning and deep learning algorithms applied to Sentinel-2 multispectral imagery for wildfire applications, including convolutional neural networks (CNN, U-Net, ResNet, EfficientNet), random forest, support vector machines, gradient boosting methods, and attention-based architectures. Includes both uni-temporal and bi-temporal approaches, as well as fusion with Sentinel-1 SAR data"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/machine-learning-algorithms-for-wildfire-detection",
                "http://data.cochrane.org/ontologies/pico/comparatorGroup": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/comparatorGroup"
                    }
                ],
                "http://data.cochrane.org/ontologies/pico/interventionGroup": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/interventionGroup"
                    }
                ],
                "http://data.cochrane.org/ontologies/pico/outcomeGroup": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/outcomeGroup"
                    }
                ],
                "http://data.cochrane.org/ontologies/pico/population": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/population"
                    }
                ],
                "http://purl.org/dc/terms/description": [
                    {
                        "@value": "What machine learning algorithms have been developed and validated for wildfire detection, risk prediction, and burned area mapping using Sentinel-2 imagery, and what are their reported performance metrics, geographic coverage, and application readiness?"
                    }
                ],
                "@type": [
                    "http://data.cochrane.org/ontologies/pico/PICO",
                    "https://w3id.org/sciencelive/o/terms/DescriptiveResearchQuestion"
                ],
                "http://www.w3.org/2000/01/rdf-schema#label": [
                    {
                        "@value": "Machine Learning Algorithms for Wildfire Detection and Burned Area Mapping Using Sentinel-2 Imagery: A Systematic Review"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/outcomeGroup",
                "http://purl.org/dc/terms/description": [
                    {
                        "@value": "Algorithm performance metrics (accuracy, precision, recall, F1-score, IoU, overall accuracy, kappa coefficient), geographic transferability, computational requirements, input data requirements, code and model availability, and operational readiness for wildfire management applications"
                    }
                ]
            },
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/population",
                "http://purl.org/dc/terms/description": [
                    {
                        "@value": "Geographic regions affected by wildfires globally, with focus on areas where Sentinel-2 multispectral imagery has been applied for wildfire-related studies, including Mediterranean Europe, California, Australia, Canada, and other fire-prone ecosystems"
                    }
                ]
            }
        ]
    },
    {
        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/Head",
        "@graph": [
            {
                "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0",
                "http://www.nanopub.org/nschema#hasAssertion": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/assertion"
                    }
                ],
                "http://www.nanopub.org/nschema#hasProvenance": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/provenance"
                    }
                ],
                "http://www.nanopub.org/nschema#hasPublicationInfo": [
                    {
                        "@id": "https://w3id.org/np/RAjO8tdVOla9I77PeXF4iY92ULngrpx5_ZSKFkVrCmsW0/pubinfo"
                    }
                ],
                "@type": [
                    "http://www.nanopub.org/nschema#Nanopublication"
                ]
            }
        ]
    }
]