{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/thraws-a-novel-dataset-for-thermal-hotspots","title":"Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence","arxiv_id":"2305.11891","date":"2023-05-12","proceeding":null,"authors":["Gabriele Meoni","Roberto Del Prete","Federico Serva","Alix De Beussche","Olivier Colin","Nicolas Longépé"],"abstract":"Nowadays, there is growing interest in applying Artificial Intelligence (AI) on board Earth Observation (EO) satellites for time-critical applications, such as natural disaster response. However, the unavailability of raw satellite data currently hinders research on lightweight pre-processing techniques and limits the exploration of end-to-end pipelines, which could offer more efficient and accurate extraction of insights directly from the source data. To fill this gap, this work presents a novel methodology to automate the creation of datasets for the detection of target events (e.g., warm thermal hotspots) or objects (e.g., vessels) from Sentinel-2 raw data and other multispectral EO pushbroom raw imagery. The presented approach first processes the raw data by applying a pipeline consisting of spatial band registration and georeferencing of the raw data pixels. Then, it detects the target events by leveraging event-specific state-of-the-art algorithms on the Level-1C products, which are mosaicked and cropped on the georeferenced correspondent raw granule area. The detected events are finally re-projected back onto the corresponding raw images. We apply the proposed methodology to realize THRawS (Thermal Hotspots in Raw Sentinel-2 data), the first dataset of Sentinel-2 raw data containing warm thermal hotspots. THRawS includes 1090 samples containing wildfires, volcanic eruptions, and 33,335 event-free acquisitions to enable thermal hotspot detection and general classification applications. This dataset and associated toolkits provide the community with both an immediately useful resource as well as a framework and methodology acting as a template for future additions. With this work, we hope to pave the way for research on energy-efficient pre-processing algorithms and AI-based end-to-end processing systems on board EO satellites.","url_abs":"https://arxiv.org/abs/2305.11891v2","url_pdf":"https://arxiv.org/pdf/2305.11891v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"thraws-a-novel-dataset-for-thermal-hotspots","repo_url":"https://github.com/esa-philab/pyraws","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"disaster-response","task_name":"Disaster Response"},{"task_slug":"earth-observation","task_name":"Earth Observation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"thraws","name":"THRawS","full_name":"Thermal Hotspots in Raw Sentinel-2 data"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}