{
  "annotation_id": "da4a25d7-3e3f-4bcb-87db-ca6a8fc5cccc",
  "date_created": "2026-02-17T05:53:20.221000Z",
  "date_modified": "2026-02-17T05:53:20.221000Z",
  "file_hash": "366ad0d0e2f361e0ac228a55226b514599def5a2a13eab252c11205a342d48b5",
  "private": false,
  "record": {
    "abstract": "Delineating wildfire affected areas using satellite imagery remains challenging due to irregular and spatially heterogeneous spectral changes across the electromagnetic spectrum. While recent deep learning approaches achieve high accuracy when high-resolution multispectral data are available, their applicability in operational settings, where a quick delineation of the burn scar shortly after a wildfire incident is required, is limited by the trade-off between spatial resolution and temporal revisit frequency of current satellite systems. To address this limitation, we propose a novel deep learning model, namely BAM-MRCD, which employs multi-resolution, multi-source satellite imagery (MODIS and Sentinel-2) for the timely production of detailed burnt area maps with high spatial and temporal resolution. Our model manages to detect even small scale wildfires with high accuracy, surpassing similar change detection models as well as solid baselines. All data and code are available in the GitHub repository: https://github.com/Orion-AI-Lab/BAM-MRCD.",
    "arxiv_id": "2601.09262",
    "authors": [
      "Maria Sdraka",
      "Dimitrios Michail",
      "Ioannis Papoutsis"
    ],
    "categories": [
      "cs.CV",
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Magnifying change: Rapid burn scar mapping with multi-resolution, multi-source satellite imagery",
    "url": "https://arxiv.org/abs/2601.09262",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "989c09ef-40c9-44f9-bee2-192bb6942349",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}