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Sustain. Soc."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Greenspaces in communities are critical for mitigating effects of climate change and have important impacts on health. Today, the availability of satellite imagery data combined with deep learning methods allows for automated greenspace analysis at high resolution. We propose a novel green color augmentation for deep learning model training to better detect and delineate types of greenspace (trees, grass) with satellite imagery. Our method outperforms gold standard methods, which use vegetation indices, by 33.1% (accuracy) and 77.7% Intersection over Union (IoU). The proposed augmentation technique also shows improvement over state-of-the-art deep learning based methods by 13.4% (IoU) and 3.11% (accuracy) for greenspace segmentation. We apply the method to high-resolution (0.27\n            <jats:italic>m<\/jats:italic>\n            \/pixel) satellite images covering Karachi, Pakistan, and illuminate an important need; Karachi has 4.17\n            <jats:italic>m<\/jats:italic>\n            <jats:sup>2<\/jats:sup>\n            of greenspace per capita, which significantly lags World Health Organization recommendations. Moreover, greenspaces in Karachi are often in areas of economic development (Pearson\u2019s correlation coefficient shows a 0.352 correlation between greenspaces and roads,\n            <jats:italic>p<\/jats:italic>\n            &lt; 0.001), and correspond to higher land surface temperature in localized areas. Our greenspace analysis and how it relates to infrastructure and climate is relevant to urban planners, public health and government professionals, and ultimately the public, for improved allocation and development of greenspaces.\n          <\/jats:p>","DOI":"10.1145\/3716370","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T09:09:52Z","timestamp":1739005792000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantifying Greenspace with Satellite Images in Karachi, Pakistan, Using a New Data Augmentation Paradigm"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1869-9118","authenticated-orcid":false,"given":"Miao","family":"Zhang","sequence":"first","affiliation":[{"name":"New York University, New York, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9206-739X","authenticated-orcid":false,"given":"Hajra","family":"Arshad","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6834-2328","authenticated-orcid":false,"given":"Manzar","family":"Abbas","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6790-773X","authenticated-orcid":false,"given":"Hamzah","family":"Jehanzeb","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7620-5561","authenticated-orcid":false,"given":"Izza","family":"Tahir","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9831-8305","authenticated-orcid":false,"given":"Javerya","family":"Hassan","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2422-3199","authenticated-orcid":false,"given":"Zainab","family":"Samad","sequence":"additional","affiliation":[{"name":"The Aga Khan University, Karachi, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5346-7259","authenticated-orcid":false,"given":"Rumi","family":"Chunara","sequence":"additional","affiliation":[{"name":"New York University, New York, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"Amin Ahmed. 2020. 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