{
  "id": "2305.04499",
  "version": "v1",
  "published": "2023-05-08T06:50:05.000Z",
  "updated": "2023-05-08T06:50:05.000Z",
  "title": "Building Footprint Extraction with Graph Convolutional Network",
  "authors": [
    "Yilei Shi",
    "Qinyu Li",
    "Xiaoxiang Zhu"
  ],
  "comment": "4 pages. arXiv admin note: text overlap with arXiv:1911.03165",
  "doi": "10.1109/IGARSS.2019.8898764",
  "categories": [
    "cs.CV",
    "eess.IV"
  ],
  "abstract": "Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. Recent developments in deep convolutional neural networks (DCNNs) have enabled accurate pixel-level labeling tasks. One central issue remains, which is the precise delineation of boundaries. Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to progressive downsampling. In this work, we have proposed a end-to-end framework to overcome this issue, which uses the graph convolutional network (GCN) for building footprint extraction task. Our proposed framework outperforms state-of-the-art methods.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-05-08T06:50:05.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "graph convolutional network",
      "building footprint extraction",
      "framework outperforms state-of-the-art methods",
      "deep convolutional neural networks",
      "central issue remains"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "IEEE"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 4,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}