{
  "id": "2310.11622",
  "version": "v1",
  "published": "2023-10-17T23:20:36.000Z",
  "updated": "2023-10-17T23:20:36.000Z",
  "title": "High-Resolution Building and Road Detection from Sentinel-2",
  "authors": [
    "Wojciech Sirko",
    "Emmanuel Asiedu Brempong",
    "Juliana T. C. Marcos",
    "Abigail Annkah",
    "Abel Korme",
    "Mohammed Alewi Hassen",
    "Krishna Sapkota",
    "Tomer Shekel",
    "Abdoulaye Diack",
    "Sella Nevo",
    "Jason Hickey",
    "John Quinn"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Mapping buildings and roads automatically with remote sensing typically requires high-resolution imagery, which is expensive to obtain and often sparsely available. In this work we demonstrate how multiple 10 m resolution Sentinel-2 images can be used to generate 50 cm resolution building and road segmentation masks. This is done by training a `student' model with access to Sentinel-2 images to reproduce the predictions of a `teacher' model which has access to corresponding high-resolution imagery. While the predictions do not have all the fine detail of the teacher model, we find that we are able to retain much of the performance: for building segmentation we achieve 78.3% mIoU, compared to the high-resolution teacher model accuracy of 85.3% mIoU. We also describe a related method for counting individual buildings in a Sentinel-2 patch which achieves R^2 = 0.91 against true counts. This work opens up new possibilities for using freely available Sentinel-2 imagery for a range of tasks that previously could only be done with high-resolution satellite imagery.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-10-17T23:20:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "road detection",
      "high-resolution building",
      "high-resolution imagery",
      "high-resolution teacher model accuracy",
      "high-resolution satellite imagery"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}