SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping
The increasing frequency of hazardous events due to climate change underscores an urgent need for effective hazard monitoring systems. Current approaches rely on expensive labelled datasets, struggle to distinguish hazardous changes from seasonal changes, or require multiple observations for to conf...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11033198/ |
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| author | Samuel Garske Konrad Heidler Bradley Evans Kee Choon Wong Xiao Xiang Zhu |
| author_facet | Samuel Garske Konrad Heidler Bradley Evans Kee Choon Wong Xiao Xiang Zhu |
| author_sort | Samuel Garske |
| collection | DOAJ |
| description | The increasing frequency of hazardous events due to climate change underscores an urgent need for effective hazard monitoring systems. Current approaches rely on expensive labelled datasets, struggle to distinguish hazardous changes from seasonal changes, or require multiple observations for to confirm when a potential hazardous event has occurred. To address these challenges, this work presents Self-Supervised Change Monitoring for Hazard Detection and Mapping—SHAZAM. SHAZAM uses a lightweight conditional UNet model to generate images of a region of interest (ROI) for any day of the year, allowing for the direct modelling of seasonal changes. A modified structural similarity measure compares the generated images with real satellite observations to compute both image-level anomaly scores, and pixel-level hazard maps. In addition, a seasonal threshold is introduced to further reduce the need for dataset-specific optimisation. SHAZAM was evaluated on four diverse datasets that contain wildfires, burned regions, extreme and out-of-season snowfall, floods, droughts, algal blooms, and deforestation, and achieved F1 score improvements of between 0.066 and 0.234 over existing methods. This was achieved primarily through a higher recall over other models, while using only 473 K parameters. SHAZAM demonstrates superior mapping capabilities through a higher spatial resolution and the suppression of background features for both immediate and gradual hazards. SHAZAM offers an effective and generalisable solution for hazard detection and mapping across different geographical regions and a diverse range of hazards, and highlights the potential of further self-supervised method contributions for hazard detection and mapping. |
| format | Article |
| id | doaj-art-a78f662f33a34f759fd1fa7e6c467b4d |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a78f662f33a34f759fd1fa7e6c467b4d2025-08-20T02:38:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118154561547610.1109/JSTARS.2025.357933011033198SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and MappingSamuel Garske0https://orcid.org/0000-0002-0622-8255Konrad Heidler1https://orcid.org/0000-0001-8226-0727Bradley Evans2https://orcid.org/0000-0001-6675-3118Kee Choon Wong3https://orcid.org/0000-0002-4977-5611Xiao Xiang Zhu4https://orcid.org/0000-0001-8107-9096School of Aerospace, Mechanical, and Mechatronic Engineering, The University of Sydney, Sydney, NSW, AustraliaSiPEO, Technical University of Munich, Munich, GermanySchool of Environmental and Rural Science, The University of New England, Armidale, NSW, AustraliaSchool of Aerospace, Mechanical, and Mechatronic Engineering, The University of Sydney, Sydney, NSW, AustraliaChair of Data Science in Earth Observation (SiPEO), Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich, Munich, GermanyThe increasing frequency of hazardous events due to climate change underscores an urgent need for effective hazard monitoring systems. Current approaches rely on expensive labelled datasets, struggle to distinguish hazardous changes from seasonal changes, or require multiple observations for to confirm when a potential hazardous event has occurred. To address these challenges, this work presents Self-Supervised Change Monitoring for Hazard Detection and Mapping—SHAZAM. SHAZAM uses a lightweight conditional UNet model to generate images of a region of interest (ROI) for any day of the year, allowing for the direct modelling of seasonal changes. A modified structural similarity measure compares the generated images with real satellite observations to compute both image-level anomaly scores, and pixel-level hazard maps. In addition, a seasonal threshold is introduced to further reduce the need for dataset-specific optimisation. SHAZAM was evaluated on four diverse datasets that contain wildfires, burned regions, extreme and out-of-season snowfall, floods, droughts, algal blooms, and deforestation, and achieved F1 score improvements of between 0.066 and 0.234 over existing methods. This was achieved primarily through a higher recall over other models, while using only 473 K parameters. SHAZAM demonstrates superior mapping capabilities through a higher spatial resolution and the suppression of background features for both immediate and gradual hazards. SHAZAM offers an effective and generalisable solution for hazard detection and mapping across different geographical regions and a diverse range of hazards, and highlights the potential of further self-supervised method contributions for hazard detection and mapping.https://ieeexplore.ieee.org/document/11033198/Anomaly detectionchange detectiondeep learninghazard detectionsatellite image time series (SITS)self-supervised learning |
| spellingShingle | Samuel Garske Konrad Heidler Bradley Evans Kee Choon Wong Xiao Xiang Zhu SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Anomaly detection change detection deep learning hazard detection satellite image time series (SITS) self-supervised learning |
| title | SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping |
| title_full | SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping |
| title_fullStr | SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping |
| title_full_unstemmed | SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping |
| title_short | SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping |
| title_sort | shazam self supervised change monitoring for hazard detection and mapping |
| topic | Anomaly detection change detection deep learning hazard detection satellite image time series (SITS) self-supervised learning |
| url | https://ieeexplore.ieee.org/document/11033198/ |
| work_keys_str_mv | AT samuelgarske shazamselfsupervisedchangemonitoringforhazarddetectionandmapping AT konradheidler shazamselfsupervisedchangemonitoringforhazarddetectionandmapping AT bradleyevans shazamselfsupervisedchangemonitoringforhazarddetectionandmapping AT keechoonwong shazamselfsupervisedchangemonitoringforhazarddetectionandmapping AT xiaoxiangzhu shazamselfsupervisedchangemonitoringforhazarddetectionandmapping |