{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:51:27Z","timestamp":1760241087391,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2019,11,14]],"date-time":"2019-11-14T00:00:00Z","timestamp":1573689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0501404"],"award-info":[{"award-number":["2016YFB0501404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAS Earth Big Data Science Project","award":["XDA19060303"],"award-info":[{"award-number":["XDA19060303"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41671436"],"award-info":[{"award-number":["41671436"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Innovation Project of LREIS","award":["O88RAA01YA"],"award-info":[{"award-number":["O88RAA01YA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object\u2019s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.<\/jats:p>","DOI":"10.3390\/rs11222664","type":"journal-article","created":{"date-parts":[[2019,11,14]],"date-time":"2019-11-14T10:56:34Z","timestamp":1573728994000},"page":"2664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9755-0687","authenticated-orcid":false,"given":"Fengshuo","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6776-2910","authenticated-orcid":false,"given":"Zhihua","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1643-8480","authenticated-orcid":false,"given":"Xiaomei","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Centre for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yueming","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Geological Engineering and Institute of Surveying and Mapping, Chang\u2019an University, Xi\u2019an 710054, China"}]},{"given":"Junmei","family":"Kang","sequence":"additional","affiliation":[{"name":"Geological Engineering and Institute of Surveying and Mapping, Chang\u2019an University, Xi\u2019an 710054, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Seto, K.C., Fragkias, M., Gueneralp, B., and Reilly, M.K. (2011). A Meta-Analysis of Global Urban Land Expansion. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0023777"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, Z., Xu, R., Du, W., Wang, L., and Lu, D. (2019). High-Resolution Urban Land Mapping in China from Sentinel 1A\/2 Imagery Based on Google Earth Engine. Remote Sens., 11.","DOI":"10.3390\/rs11070752"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.rse.2018.02.055","article-title":"High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform","volume":"209","author":"Liu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1617","DOI":"10.1109\/LGRS.2013.2272953","article-title":"Urban Footprint Processor-Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission","volume":"10","author":"Esch","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., Julea, A., Kemper, T., Soille, P., and Syrris, V. (2016). Operating Procedure for the Production of the Global Human Settlement Layer from Landsat Data of the Epochs 1975, 1990, 2000, and 2014, Publications Office of the European Union.","DOI":"10.1109\/IGARSS.2016.7730897"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A global human settlement layer from optical HR\/VHR RS data: Concept and first results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Balk, D., Leyk, S., Jones, B., Montgomery, M.R., and Clark, A. (2018). Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990\u20132010. PLoS ONE., 13.","DOI":"10.1371\/journal.pone.0208487"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.landurbplan.2018.07.009","article-title":"Up and out: A multifaceted approach to characterizing urbanization in Greater Saigon, 2000\u20132009","volume":"187","author":"Balk","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, M., Zhu, Y., Lu, G., Chen, M., and Qiao, W. (2019). Spatial distribution of Global Cultivated Land and Its Variation between 2000 and 2010, from Both Agro-Ecological and Geopolitical Perspectives. Sustainability, 11.","DOI":"10.3390\/su11051242"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Freire, S., Kemper, T., Pesaresi, M., Florczyk, A., and Syrris, V. (2015, January 26\u201331). Conmbining GHSL and GPW to improve global population mapping. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326329"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Merkens, J.-L., and Vafeidis, A.T. (2018). Using information on settlement patterns to improve the spatial distribution of population in coastal impact assessments. Sustainability, 10.","DOI":"10.3390\/su10093170"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/LGRS.2016.2615318","article-title":"Assessing and Improving the Accuracy of GlobeLand30 Data for Urban Area Delineation by Combining Multisource Remote Sensing Data","volume":"13","author":"Huang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, X., Tong, X., Liu, S., Luo, X., Xie, H., and Li, C. (2017). Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas. Remote Sens., 9.","DOI":"10.3390\/rs9030236"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7339","DOI":"10.3390\/rs6087339","article-title":"Urban built-up area extraction from landsat, TM\/ETM+ images using spectral information, and multivariate texture","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.isprsjprs.2016.12.011","article-title":"Quantifying annual changes in built-up area in complex urban-rural landscapes from analyses of PALSAR and Landsat images","volume":"124","author":"Qin","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1111\/j.1745-5871.2011.00732.x","article-title":"Integrating Multi-Sensor Remote Sensing Data for Land Use\/Cover Mapping in a Tropical Mountainous Area in Northern Thailand","volume":"50","author":"Wang","year":"2012","journal-title":"Geogr. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1007\/s00343-019-8266-y","article-title":"Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster","volume":"37","author":"Yang","year":"2019","journal-title":"J. Oceanol. Limnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2196","DOI":"10.1109\/JSTARS.2013.2252329","article-title":"Robust extraction of urban land cover information from hsr multi-spectral and lidar data","volume":"6","author":"Berger","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2012.09.009","article-title":"Lidar-landsat data fusion for large-area assessment of urban land cover: balancing spatial resolution, data volume and mapping accuracy","volume":"74","author":"Singh","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.3390\/rs10091406","article-title":"JAXA High-Resolution Land Use\/Land Cover Map for Central Vietnam in 2007 and 2017","volume":"10","author":"Nasahara","year":"2018","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.compenvurbsys.2017.02.002","article-title":"Employing crowdsourced geographic data and multi-temporal\/multi-sensor satellite imagery to monitor land cover change: A case study in an urbanizing region of the Philippines","volume":"64","author":"Johnson","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Haeufel, G., Bulatov, D., Pohl, M., and Lucks, L. (2018, January 22\u201327). Generation of training examples using OSM data applied for remote sensed landcover classification. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518311"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.apgeog.2015.12.006","article-title":"Integrating OpenStreetMap crowdsourced data and Landsat time series imagery for rapid land use\/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines","volume":"67","author":"Johnson","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mueck, M., Klotz, M., and Taubenboeck, H. (2017, January 6\u20138). Validation of the DLR Global Urban Footprint in rural areas: A case study for Burkina Faso. Proceedings of the 2017 Joint Urban Remote Sensing Event, Dubai, UAE.","DOI":"10.1109\/JURSE.2017.7924618"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sliuzas, R., Kuffer, M., and Kemper, T. (2017, January 6\u20138). Assessing the quality of Global Human Settlement Layer products for Kampala, Uganda. Proceedings of the 2017 Joint Urban Remote Sensing Event, Dubai, UAE.","DOI":"10.1109\/JURSE.2017.7924569"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TGRS.2010.2091644","article-title":"Characterization of Land Cover Types in TerraSAR-X Images by Combined Analysis of Speckle Statistics and Intensity Information","volume":"49","author":"Esch","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"061702","DOI":"10.1117\/1.JRS.6.061702","article-title":"TanDEM-X mission-new perspectives for the inventory and monitoring of global settlement patterns","volume":"6","author":"Esch","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TGRS.2009.2037144","article-title":"Delineation of Urban Footprints From TerraSAR-X Data by Analyzing Speckle Characteristics and Intensity Information","volume":"48","author":"Esch","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3915","DOI":"10.1109\/TGRS.2009.2023909","article-title":"PALSAR Radiometric and Geometric Calibration","volume":"47","author":"Shimada","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.rse.2012.08.022","article-title":"A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA","volume":"127","author":"Dong","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/j.scib.2019.04.024","article-title":"40-Year (1978\u20132017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing","volume":"64","author":"Gong","year":"2019","journal-title":"Chin. Sci. Bull."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.rse.2015.06.007","article-title":"A 30-year (1984\u20132013) record of annual urban dynamics of Beijing City derived from Landsat data","volume":"166","author":"Li","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"024008","DOI":"10.1088\/1748-9326\/9\/2\/024008","article-title":"Expansion and growth in Chinese cities, 1978\u20132010","volume":"9","author":"Schneider","year":"2014","journal-title":"Environ. Res. Lett."},{"key":"ref_37","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., and Harlan, J.C. (1974). Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation, NASA\/GSFC. Type III, Final Report."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/0034-4257(96)00039-9","article-title":"Mapping land surface emissivity from ndvi: application to european, african, and south american areas","volume":"57","author":"Valor","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_39","first-page":"589","article-title":"A study on information extraction of water body with the modified normalized difference water index (MNDWI)","volume":"9","author":"Xu","year":"2005","journal-title":"J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1080\/01431161.2017.1410297","article-title":"Exponentially sampling scale parameters for the efficient segmentation of remote-sensing images","volume":"39","author":"Wang","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","first-page":"88","article-title":"A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use\/cover change databases using high spatial resolution images","volume":"69","author":"Wang","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_45","first-page":"87","article-title":"A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments","volume":"49","author":"Li","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Foody, G.M., Pal, M., Rocchini, D., Garzon-Lopez, C.X., and Bastin, L. (2016). The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5110199"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5273","DOI":"10.1080\/01431160903130937","article-title":"Sample size determination for image classification accuracy assessment and comparison","volume":"30","author":"Foody","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.rse.2010.05.003","article-title":"Assessing the accuracy of land cover change with imperfect ground reference data","volume":"114","author":"Foody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/2150704X.2013.798708","article-title":"Ground reference data error and the mis-estimation of the area of land cover change as a function of its abundance","volume":"4","author":"Foody","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4010","DOI":"10.1109\/JSTARS.2017.2706747","article-title":"Urban Impervious Surfaces Estimation From Optical and SAR Imagery: A Comprehensive Comparison","volume":"10","author":"Xu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1080\/01431161003745608","article-title":"Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects","volume":"32","author":"Kim","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5655","DOI":"10.1080\/014311602331291215","article-title":"Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery","volume":"25","author":"Wang","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2013.01.002","article-title":"Boundary-constrained multi-scale segmentation method for remote sensing images","volume":"78","author":"Zhang","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","first-page":"240","article-title":"GlobeLand30 maps show four times larger gross than net land change from 2000 to 2010 in Asia","volume":"78","author":"Minaei","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTARS.2015.2398032","article-title":"Scaling up to national\/regional urban extent mapping using landsat data","volume":"8","author":"Trianni","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2664\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:34:29Z","timestamp":1760189669000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/22\/2664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,14]]},"references-count":57,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2019,11]]}},"alternative-id":["rs11222664"],"URL":"https:\/\/doi.org\/10.3390\/rs11222664","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,11,14]]}}}