{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:00:52Z","timestamp":1791248452621,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2011,9,26]],"date-time":"2011-09-26T00:00:00Z","timestamp":1316995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity\/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts.<\/jats:p>","DOI":"10.3390\/rs3102128","type":"journal-article","created":{"date-parts":[[2011,9,26]],"date-time":"2011-09-26T11:55:52Z","timestamp":1317038152000},"page":"2128-2147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Optimizing Spatial Resolution of Imagery for Urban Form Detection\u2014The Cases of France and Vietnam"],"prefix":"10.3390","volume":"3","author":[{"given":"Thi Dong-Binh","family":"Tran","sequence":"first","affiliation":[{"name":"Image, Ville, Environment Laboratory CNRS ERL 7230, University of Strasbourg, 3, rue de l\u2019Argonne, F-67083 Strasbourg Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Puissant","sequence":"additional","affiliation":[{"name":"Image, Ville, Environment Laboratory CNRS ERL 7230, University of Strasbourg, 3, rue de l\u2019Argonne, F-67083 Strasbourg Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominique","family":"Badariotti","sequence":"additional","affiliation":[{"name":"Image, Ville, Environment Laboratory CNRS ERL 7230, University of Strasbourg, 3, rue de l\u2019Argonne, F-67083 Strasbourg Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiane","family":"Weber","sequence":"additional","affiliation":[{"name":"Image, Ville, Environment Laboratory CNRS ERL 7230, University of Strasbourg, 3, rue de l\u2019Argonne, F-67083 Strasbourg Cedex, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2011,9,26]]},"reference":[{"key":"ref_1","unstructured":"Spence, M., Clarke, A., and Buckley, R.M. 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