{
  "certificate": {
    "id": "2026-013",
    "title": "Reproducibility review of: Maximizing Data Coverage: Fusing Street View and Oblique Imagery to Quantify Vertical Greenery Potential in Urban Areas",
    "url": "https://codecheck.org.uk/register/certs/2026-013/"
  },
  "paper": {
    "title": "Maximizing Data Coverage: Fusing Street View and Oblique Imagery to Quantify Vertical Greenery Potential in Urban Areas",
    "authors": [
      {
        "name": "Aruscha Kramm",
        "orcid": "0009-0002-7801-9388"
      },
      {
        "name": "André Ludwig"
      },
      {
        "name": "Bogdan Franczyk"
      }
    ],
    "reference": "https://doi.org/10.5194/agile-giss-7-8-2026",
    "abstract": {
      "text": "<jats:p>Abstract. As urbanization intensifies globally, the Urban Heat Island (UHI) effect has emerged as a critical environmental challenge, inducing higher energy demands, compromised air quality, and significant public health risks. Vertical Greenery Systems (VGS) such as green facades and living walls, offer a spatially efficient adaptation strategy by utilizing vertical surface areas of the built environment for thermal regulation and microclimatic improvement, yet the absence of data-integrative methods hinders large-scale evaluation of factors defining a surface’s suitability for VGS. Previous methodologies for estimating city-wide greening potential have successfully integrated semantic 3D city models (LoD2) with Street View Imagery (SVI) to derive key suitability factors such as Window-to-Wall Ratio (WWR) and Solid Wall Area (SWA). However, these approaches are inherently limited by the sparse spatial coverage of SVI, which is restricted to navigable road networks, leaving rear facades and inner courtyards unassessed, and which is frequently obstructed by foreground occlusion, leading to errors in the factor calculation. This consecutive work introduces a robust computational method that enhances the existing LoD2-SVI pipeline with Oblique Aerial Imagery extending the potential estimation to over 90% of the urban building stock. To mitigate occlusion and resolution disparities, we propose a multi-view fusion algorithm that aggregates detections across multiple views within one perspective and further across two perspectives. Our evaluation demonstrates that both data sources deliver comparable results when assessing identical facades. Further, our fusion approach significantly reduces systematic biases found in single-source estimations. Ultimately, while the fusion approach maximizes assessment reliability for walls with dual coverage, the integration of oblique imagery remains critical for scalability. Although it yields lower feature fidelity than street view, it provides the only viable means to assess surfaces lying beyond the navigable road network.  Reproducibility review available at: https://doi.org/10.17605/OSF.IO/HETV8<\/jats:p>",
      "source": "CrossRef"
    },
    "openalex": "https://openalex.org/W7164179951"
  },
  "codecheck": {
    "codecheckers": [
      {
        "name": "Jeonghwan Choi",
        "orcid": "0009-0002-5027-1151"
      }
    ],
    "check_time": "2026-03-13 12:00:00",
    "repository": "osf::hetv8",
    "report": "https://doi.org/10.17605/OSF.IO/HETV8",
    "type": "conference",
    "venue": "AGILEGIS",
    "summary": "Partially reproducible. The LoD2 preprocessing step (distribution of façade orientations) was reproduced successfully, confirming successful preprocessing; other manuscript outputs could be reproduced only partially.\n",
    "manifest": [
      {
        "file": "~result/orientation_orig.png",
        "comment": "Distribution of facade orientations (LoD2 preprocessing)"
      },
      {
        "file": "~result/wwr_orig.png",
        "comment": "Window-to-wall ratio values"
      },
      {
        "file": "~result/swa_orig.png",
        "comment": "Surface area values"
      },
      {
        "file": "~result/index_weighted.png",
        "comment": "Weighted vertical greenery potential index"
      }
    ]
  }
}