{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:12:39Z","timestamp":1774962759074,"version":"3.50.1"},"reference-count":110,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and reliable informal settlement maps are fundamental decision-making tools for planning, and for expediting informed management of cities. However, extraction of spatial information for informal settlements has remained a mammoth task due to the spatial heterogeneity of urban landscape components, requiring complex analytical processes. To date, the use of Google Earth Engine platform (GEE), with cloud computing prowess, provides unique opportunities to map informal settlements with precision and enhanced accuracy. This paper leverages cloud-based computing techniques within GEE to integrate spectral and textural features for accurate extraction of the location and spatial extent of informal settlements in Durban, South Africa. The paper aims to investigate the potential and advantages of GEE\u2019s innovative image processing techniques to precisely depict morphologically varied informal settlements. Seven data input models derived from Sentinel 2A bands, band-derived texture metrics, and spectral indices were investigated through a random forest supervised protocol. The main objective was to explore the value of different data input combinations in accurately mapping informal settlements. The results revealed that the classification based on spectral bands + textural information yielded the highest informal settlement identification accuracy (94% F-score). The addition of spectral indices decreased mapping accuracy. Our results confirm that the highest spatial accuracy is achieved with the \u2018textural features\u2019 model, which yielded the lowest root-mean-square log error (0.51) and mean absolute percent error (0.36). Our approach highlights the capability of GEE\u2019s complex integrative data processing capabilities in extracting morphological variations of informal settlements in rugged and heterogeneous urban landscapes, with reliable accuracy.<\/jats:p>","DOI":"10.3390\/rs14205130","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information"],"prefix":"10.3390","volume":"14","author":[{"given":"Dadirai","family":"Matarira","sequence":"first","affiliation":[{"name":"School of Agriculture, Earth and Environmental Science, University of KwaZulu-Natal, P. Bag X01 Scottsville, Pietermaritzburg 3209, South Africa"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"Department of Geography, University of KwaZulu-Natal, P. Bag X01 Scottsville, Pietermaritzburg 3209, South Africa"}]},{"given":"Maheshvari","family":"Naidu","sequence":"additional","affiliation":[{"name":"Department of Humanities, School of Social Sciences, University of KwaZulu-Natal, Durban 4041, South Africa"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Samper, J., Shelby, J., and Behary, D. (2020). The Paradox of Informal Settlements Revealed in an ATLAS of Informality: Findings from Mapping Growth in the Most Common Yet Unmapped Forms of Urbanization. Sustainability, 12.","DOI":"10.3390\/su12229510"},{"key":"ref_2","unstructured":"UNDP (2018). Human Development Indices and Indicators: 2018 Statistical Updatep, UNDP."},{"key":"ref_3","unstructured":"United-Nations (2019). World Urbanization Prospects 2018, United Nations."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Fallatah, A., Jones, S., Wallace, L., and Mitchell, D. (2022). Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification. Remote Sens., 14.","DOI":"10.3390\/rs14051226"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mboga, N., Persello, C., Bergado, J.R., and Stein, A. (2017). Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks. Remote Sens., 9.","DOI":"10.3390\/rs9111106"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/LGRS.2017.2763738","article-title":"Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images","volume":"14","author":"Persello","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.compenvurbsys.2018.08.007","article-title":"The role of spatial heterogeneity in detecting urban slums","volume":"73","author":"Wang","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6688","DOI":"10.1080\/01431161.2021.1943039","article-title":"An enhanced approach for informal settlement extraction from optical data using morphological profile-guided filters: A case study of madurai city","volume":"42","author":"Prabhu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102836","DOI":"10.1016\/j.ijdrr.2022.102836","article-title":"Using local and indigenous knowledge in selecting indicators for mapping flood vulnerability in informal settlement contexts","volume":"71","author":"Membele","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.oneear.2020.02.002","article-title":"Building Resilience to Climate Change in Informal Settlements","volume":"2","author":"Satterthwaite","year":"2020","journal-title":"One Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.worlddev.2013.08.005","article-title":"The Political Economy of Slums: Theory and Evidence from Sub-Saharan Africa","volume":"54","author":"Fox","year":"2014","journal-title":"World Dev."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Winter, S.C., Obara, L.M., and McMahon, S. (2020). Intimate partner violence: A key correlate of women\u2019s physical and mental health in informal settlements in Nairobi, Kenya. PLoS ONE, 15.","DOI":"10.31219\/osf.io\/hs2dv"},{"key":"ref_13","first-page":"257","article-title":"A hybrid methodology to map informal settlements in Durban, South Africa","volume":"173","author":"Loggia","year":"2020","journal-title":"Proc. Inst. Civ. Eng. Eng. Sustain."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1080\/08882746.2019.1622346","article-title":"Measuring deprivations in the slums of Bangladesh: Implications for achieving sustainable development goals","volume":"46","author":"Patel","year":"2019","journal-title":"Hous. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117923","DOI":"10.1016\/j.jclepro.2019.117923","article-title":"Globalization and the shifting centers of gravity of world\u2019s human dynamics: Implications for sustainability","volume":"239","author":"Li","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Parnell, S., and Crankshaw, O. (2009). Urban exclusion and the (false) assumptions of spatial policy reform in South Africa. Megacities, Zed Books Ltd.","DOI":"10.5040\/9781350221345.ch-009"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Quesada-Rom\u00e1n, A. (2022). Disaster Risk Assessment of Informal Settlements in the Global South. Sustainability, 14.","DOI":"10.3390\/su141610261"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1109\/JSTARS.2016.2538563","article-title":"Extraction of Slum Areas From VHR Imagery Using GLCM Variance","volume":"9","author":"Kuffer","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","first-page":"100801","article-title":"Towards understanding informal settlement growth patterns: Contribution to SDG reporting and spatial planning","volume":"27","author":"Mudau","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.3390\/rs5094209","article-title":"Transferability of Object-Oriented Image Analysis Methods for Slum Identification","volume":"5","author":"Kohli","year":"2013","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mudau, N., and Mhangara, P. (2021). Investigation of Informal Settlement Indicators in a Densely Populated Area Using Very High Spatial Resolution Satellite Imagery. Sustainability, 13.","DOI":"10.3390\/su13094735"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102766","DOI":"10.1016\/j.ijdrr.2021.102766","article-title":"Examining flood vulnerability mapping approaches in developing countries: A scoping review","volume":"69","author":"Membele","year":"2021","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"012042","DOI":"10.1088\/1755-1315\/98\/1\/012042","article-title":"Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine","volume":"98","author":"Farda","year":"2017","journal-title":"IOP Conf. Series Earth Environ. Sci."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Mugiraneza, T., Nascetti, A., and Ban, Y. (2019). WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices. Remote Sens., 11.","DOI":"10.3390\/rs11182128"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.1109\/JSTARS.2020.3018862","article-title":"Satellite-Based Mapping of Urban Poverty With Transfer-Learned Slum Morphologies","volume":"13","author":"Stark","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mananze, S., P\u00f4\u00e7as, I., and Cunha, M. (2020). Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique. Remote Sens., 12.","DOI":"10.3390\/rs12081279"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.habitatint.2010.09.006","article-title":"A review of physical and socio-economic characteristics and intervention approaches of informal settlements","volume":"35","author":"Wekesa","year":"2011","journal-title":"Habitat Int."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kuffer, M., Pfeffer, K., Sliuzas, R., Baud, I., and Van Maarseveen, M. (2017). Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai. Remote Sens., 9.","DOI":"10.3390\/rs9040384"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kuffer, M., Wang, J., Nagenborg, M., Pfeffer, K., Kohli, D., Sliuzas, R., and Persello, C. (2018). The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110428"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Leonita, G., Kuffer, M., Sliuzas, R., and Persello, C. (2018). Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia. Remote Sens., 10.","DOI":"10.3390\/rs10101522"},{"key":"ref_32","first-page":"14509","article-title":"Slum Extraction Approaches From High Resolution Satellite Data\u2014A Case Study Of Madurai City","volume":"119","author":"Girija","year":"2018","journal-title":"Int.J. Pure. Appl. Math."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1080\/14498596.2016.1138247","article-title":"Urban slum detection using texture and spatial metrics derived from satellite imagery","volume":"61","author":"Kohli","year":"2016","journal-title":"J. Spat. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1007\/s12524-018-0869-9","article-title":"Urban Slum Detection Approaches from High-Resolution Satellite Data Using Statistical and Spectral Based Approaches","volume":"46","author":"Prabhu","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.jart.2016.12.009","article-title":"A comparative study of the use of local directional pattern for texture-based informal settlement classification","volume":"15","author":"Shabat","year":"2017","journal-title":"J. Appl. Res. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ansari, R.A., Malhotra, R., and Buddhiraju, K.M. (2019, January 9\u201311). Texture Based Identification of Informal Settlements in Contourlet Feature Space. Proceedings of the 2019 IEEE International Symposium on Multimedia, San Diego, CA, USA.","DOI":"10.1109\/ISM46123.2019.00061"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1080\/22797254.2019.1565419","article-title":"Textural segmentation of remotely sensed images using multiresolution analysis for slum area identification","volume":"52","author":"Ansari","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1080\/17538947.2018.1485753","article-title":"Mapping informal settlement indicators using object-oriented analysis in the Middle East","volume":"12","author":"Fallatah","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.isprsjprs.2013.06.009","article-title":"Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery","volume":"83","author":"Kit","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.apgeog.2011.07.016","article-title":"Texture-based identification of urban slums in Hyderabad, India using remote sensing data","volume":"32","author":"Kit","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.apgeog.2012.11.016","article-title":"An approach to differentiate informal settlements using spectral, texture, geomorphology and road accessibility metrics","volume":"38","author":"Owen","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1109\/JSTARS.2012.2190383","article-title":"Image Based Characterization of Formal and Informal Neighborhoods in an Urban Landscape","volume":"5","author":"Graesser","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1080\/10106049.2012.734533","article-title":"Exploring structural differences between rural and urban informal settlements from imagery: Thebasurerosof Cob\u00e1n","volume":"28","author":"Owen","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6574","DOI":"10.1080\/01431161.2012.691612","article-title":"A comparative assessment of different methods for Landsat 7\/ETM+\u2009 pansharpening","volume":"33","author":"Mateos","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2019). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1080\/01431160310001618464","article-title":"Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case","volume":"25","author":"Chen","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1080\/15481603.2021.1947623","article-title":"Google Earth Engine for large-scale land use and land cover mapping: An object-based classification approach using spectral, textural and topographical factors","volume":"58","author":"Khazaei","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, K., Dong, X., Liu, Z., Gao, W., Hu, Z., and Wu, G. (2019). Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China\u2019s Eastern Coastal Zone circa 2015. Remote Sens., 11.","DOI":"10.3390\/rs11080924"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., White, L., Banks, S., Montgomery, J., and Hopkinson, C. (2019). Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results. Remote Sens., 11.","DOI":"10.3390\/rs11070842"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-Oriented LULC Classification in Google Earth Engine Combining SNIC, GLCM, and Machine Learning Algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_53","first-page":"199","article-title":"Multitemporal settlement and population mapping from Landsat using Google Earth Engine","volume":"35","author":"Patel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101542","DOI":"10.1016\/j.compenvurbsys.2020.101542","article-title":"Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)","volume":"84","author":"Liang","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Liu, Q., Huang, C., and Li, H. (2020). Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery. Appl. Sci., 10.","DOI":"10.3390\/app10113673"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.deveng.2018.03.001","article-title":"Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam","volume":"3","author":"Goldblatt","year":"2018","journal-title":"Dev. Eng."},{"key":"ref_60","first-page":"42","article-title":"Mapping built-up land and settlements: A comparison of machine learning algorithms in Google Earth engine","volume":"12082","author":"Rudiastuti","year":"2021","journal-title":"Seventh Geoinf. Sci. Symp."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kelley, L.C., Pitcher, L., and Bacon, C. (2018). Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua. Remote Sens., 10.","DOI":"10.3390\/rs10060952"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tingzon, I., Dejito, N., Flores, R.A., De Guzman, R., Carvajal, L., Erazo, K.Z., Cala, I.E.C., Villaveces, J., Rubio, D., and Ghani, R. (2020, January 21\u201325). Mapping New Informal Settlements Using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis. Proceedings of the IEEE\/ITU International conference on Artificial Intellugence for Good (AI4G), Geneva, Switzerland.","DOI":"10.1109\/AI4G50087.2020.9311041"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.habitatint.2019.01.002","article-title":"Mapping the visibility of informal settlements","volume":"85","author":"Kamalipour","year":"2019","journal-title":"Habitat Int."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.landurbplan.2014.11.009","article-title":"Measuring intra-urban poverty using land cover and texture metrics derived from remote sensing data","volume":"135","author":"Duque","year":"2015","journal-title":"Landsc. Urban Plan."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Matarira, D., Mutanga, O., and Naidu, M. (2022). Texture analysis approaches in modelling informal settlements: A review. Geocarto Int., 1\u201328. accepted.","DOI":"10.1080\/10106049.2022.2082541"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ella, L.A., van den Bergh, F., and van Wyk, B.J. (2008, January 7\u201311). A comparison of texture feature algorithms for urban settlement classification. Proceedings of the IGARSS 2008 International Geoscience and Remote Sensing Symposium, Boston, MA, USA.","DOI":"10.1109\/IGARSS.2008.4779599"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Khumalo, P.P., Tapamo, J.R., and van den Bergh, F. (2011, January 24\u201329). Rotation invariant texture feature algorithms for urban settlement classification. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049177"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Van den Bergh, F. (2011, January 24\u201329). The effects of viewing-and illumination geometry on settlement type classification of quickbird images. Proceedings of the International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049333"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Williams, D.S., Costa, M.M., Celliers, L., and Sutherland, C. (2018). Informal Settlements and Flooding: Identifying Strengths and Weaknesses in Local Governance for Water Management. Water, 10.","DOI":"10.3390\/w10070871"},{"key":"ref_71","unstructured":"Marx, C., and Charlton, S. (2003). Urban Slums Reports: The case of Durban, South Africa. UN-Habitat, The Challenge of Slums: Global Report on Human Settlements (2003), United Nations Human Settlements Programme, Earthscan Publications Ltd."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote. Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Firozjaei, M.K., Sedighi, A., Kiavarz, M., Qureshi, S., Haase, D., and Alavipanah, S.K. (2019). Automated Built-Up Extraction Index: A New Technique for Mapping Surface Built-Up Areas Using LANDSAT 8 OLI Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11171966"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2016.10.030","article-title":"Fusion of Sentinel-2 images","volume":"187","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Li, Q., Qiu, C., Ma, L., Schmitt, M., and Zhu, X.X. (2020). Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12040602"},{"key":"ref_77","unstructured":"Gandhi, U., and End-to-End Google Earth Engine Course (2022, August 02). Spatial Thoughts. Available online: https:\/\/coursesspatialthoughtscom\/end-to-end-geehtml."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1844","DOI":"10.23953\/cloud.ijarsg.64","article-title":"Identification and Area Measurement of the Built-up Area with the Built-up Index (BUI)","volume":"5","author":"Kaimaris","year":"2016","journal-title":"Int. J. Adv. Remote Sens. GIS"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/2150704X.2019.1690792","article-title":"Improving accuracy of Landsat-8 OLI classification using image composite and multisource data with Google Earth Engine","volume":"11","author":"Adepoju","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s41976-019-00020-y","article-title":"Mapping Urbanization Trends in a Forested Landscape Using Google Earth Engine","volume":"2","author":"Zurqani","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1080\/01431161.2016.1278314","article-title":"Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales","volume":"38","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431161.2017.1395968","article-title":"A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis","volume":"39","author":"Shi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"7366","DOI":"10.1038\/s41598-017-07951-w","article-title":"The Effects of GLCM parameters on LAI estimation using texture values from Quickbird Satellite Imagery","volume":"7","author":"Zhou","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_85","first-page":"65","article-title":"Texture Analysis for Urban Areas Classification in High Resolution Satellite Imagery","volume":"2","author":"Giannini","year":"2012","journal-title":"Appl. Remote Sens. J."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1179\/136821909X12581187860130","article-title":"Texture analysis of IKONOS satellite imagery for urban land use and land cover classification","volume":"58","author":"Kabir","year":"2010","journal-title":"Imaging Sci. J."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lan, Z., and Liu, Y. (2018). Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7050175"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2012.09.042","article-title":"Multi-scale gray level co-occurrence matrices for texture description","volume":"120","author":"Schwartz","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Trianni, G., Angiuli, E., Lisini, G., and Gamba, P. (2014, January 13\u201318). Human settlements from Landsat data using Google Earth Engine. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symbosium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946715"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.rse.2017.03.030","article-title":"Slum mapping in polarimetric SAR data using spatial features","volume":"194","author":"Wurm","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"108529","DOI":"10.1016\/j.ecolind.2021.108529","article-title":"Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period","volume":"135","author":"Zhao","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Shetty, S., Gupta, P., Belgiu, M., and Srivastav, S. (2021). Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13081433"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.sajb.2022.08.014","article-title":"Landsat-8 based coastal ecosystem mapping in South Africa using random forest classification in Google Earth Engine","volume":"150","author":"Bessinger","year":"2022","journal-title":"S. Afr. J. Bot."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"5347","DOI":"10.3390\/rs70505347","article-title":"Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA","volume":"7","author":"Hao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_98","unstructured":"Albretch, F., Lang, S., and Holbling, D. (July, January 29). Spatial accuracy assessment of object boundaries for object-based image analysis. ISPRS\u2014International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XXXVIII-4\/C7, Ghent, Belgium."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"317","DOI":"10.5194\/isprs-annals-III-3-317-2016","article-title":"Classification of informal settlements through the integration of 2D and 3D features extracted from UAV data","volume":"3","author":"Gevaert","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W., Cheng, X., and Wang, L. (2016). A Comparison of Machine Learning Algorithms for Mapping of Complex Surface-Mined and Agricultural Landscapes Using ZiYuan-3 Stereo Satellite Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8060514"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Lin, J., Jin, X., Ren, J., Liu, J., Liang, X., and Zhou, Y. (2021). Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13071245"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Chen, W., Li, X., He, H., and Wang, L. (2018). Assessing Different Feature Sets\u2019 Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10010023"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Dolean, B.-E., Bila\u0219co, \u0218., Petrea, D., Moldovan, C., Vescan, I., Ro\u0219ca, S., and Fodorean, I. (2020). Evaluation of the Built-Up Area Dynamics in the First Ring of Cluj-Napoca Metropolitan Area, Romania by Semi-Automatic GIS Analysis of Landsat Satellite Images. Appl. Sci., 10.","DOI":"10.3390\/app10217722"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1080\/15481603.2014.912874","article-title":"Comparison of NAIP orthophotography and RapidEye satellite imagery for mapping of mining and mine reclamation","volume":"51","author":"Maxwell","year":"2014","journal-title":"GIScience Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.apgeog.2018.02.002","article-title":"The morphology of the Arrival City\u2014A global categorization based on literature surveys and remotely sensed data","volume":"92","author":"Kraff","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"111207","DOI":"10.1016\/j.rse.2019.05.026","article-title":"Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes","volume":"230","author":"Virtanen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Kerr, R.B., Lupafya, E., and Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sens., 13.","DOI":"10.3390\/rs13040700"},{"key":"ref_109","first-page":"102937","article-title":"An operational framework for large-area mapping of active cropland and short-term fallows in smallholder landscapes using PlanetScope data","volume":"112","author":"Rufin","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14112628"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5130\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:53:46Z","timestamp":1760144026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/20\/5130"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,14]]},"references-count":110,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14205130"],"URL":"https:\/\/doi.org\/10.3390\/rs14205130","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,14]]}}}