{
  "abstract": "Urban heat is a growing concern for public health, energy demand, and urban liveability. High-resolution air temperature (Ta) data are needed for developing effective, localised adaptation strategies, but obtaining such data at the city scale remains a challenge as weather stations are often sparse and unevenly distributed. To address this data gap, we developed a machine learning (ML) framework that creates high-resolution, gridded Ta maps using crowdsourced observations and diverse geospatial datasets describing the city, and evaluated its accuracy, transferability, and performance under varying data availability conditions. This framework uses satellite-derived land surface temperature (LST), urban form and fabric datasets, and meteorological variables to train a convolutional neural network (CNN) model. The approach was implemented in Sydney, Australia, producing 30 m gridded Ta estimates (at ~10:00 a.m. local time) with high accuracy on an independent held-out test set (R² = 0.97; RMSE = 0.91 °C), surpassing previously reported ML performance. We further assessed the generalisation and spatial transferability of this method, showing that the CNN model maintained strong predictive accuracy for unseen locations across the city (R² = 0.91–0.93; RMSE = 1.27–1.44 °C). Model performance also remained stable when the number of Ta stations was significantly reduced by ~80%. Performance was more variable for unseen days (R² = 0.66–0.93; RMSE = 1.52–2.60 °C), indicating that ML-based Ta mapping requires training data representative of diverse weather conditions. We find that high-resolution city-descriptive datasets are beneficial but not essential, as comparable accuracy was achieved using only globally available predictors. These results indicate that the proposed framework is transferable to other cities, including those in data-sparse regions. The study provides an effective and scalable approach for city-scale air temperature mapping, which is urgently needed for urban heat assessment, climate adaptation, and public health planning.",
  "abstract_shown": true,
  "affiliations": [
    "Climate Change Research Centre, University of New South Wales, Sydney, Australia",
    "Australian Research Council Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, Australia",
    "Australian Research Council Centre of Excellence for the 21st Century Weather, University of Tasmania, Hobart, Australia",
    "Australian Research Council Centre of Excellence for the 21st Century Weather, University of New South Wales, Sydney, Australia",
    "Department of Geography, Ruhr-University Bochum, Bochum, Germany",
    "School of Built Environment, University of New South Wales, Sydney, Australia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Marzie Naserikia"
    },
    {
      "affs": [
        1
      ],
      "name": "Melissa Anne Hart"
    },
    {
      "affs": [
        2
      ],
      "name": "Elyas Asadi Shamsabadi"
    },
    {
      "affs": [
        3
      ],
      "name": "Katrin Meissner"
    },
    {
      "affs": [
        4
      ],
      "name": "Sanaa Hobeichi"
    },
    {
      "affs": [
        5
      ],
      "name": "Benjamin Bechtel"
    },
    {
      "name": "Negin Nazarian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/esz589f6shb75.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pclm.0001050",
  "fulltext_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001050",
  "id": "esz589f6shb75",
  "issns": [
    "2767-3200"
  ],
  "landing_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001050",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2305|Urban Heat Island Mitigation"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Geographic areas/Urban areas"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Human geography/Urban geography/Urban areas"
    ],
    [
      "plos",
      "/Earth sciences/Marine and aquatic sciences/Oceanography/Ocean temperature"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Terrestrial environments/Urban environments"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Electromagnetic radiation/Solar radiation"
    ],
    [
      "plos",
      "/Research and analysis methods/Research facilities/Weather stations"
    ],
    [
      "plos",
      "/Social sciences/Human geography/Urban geography/Urban areas"
    ]
  ],
  "openalex": "W7211985782",
  "pdf_url": "https://journals.plos.org/climate/article/file?id=10.1371/journal.pclm.0001050&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/esz589f6shb75.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pclm.0001050",
      "source": "plos",
      "url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001050"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "plos:oceanography",
      "subfield": "oceanography"
    }
  ],
  "title": "Machine learning-based temperature mapping in Sydney: Accuracy, transferability, and scalability",
  "title_text": "Machine learning-based temperature mapping in Sydney: Accuracy, transferability, and scalability",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/esz589f6shb75",
  "venue": "PLOS Climate",
  "versions": []
}