Advanced search
Publication Cover
Big Earth Data Volume 5, 2021 - Issue 4: Big Data in Support of the Sustainable Development Goals (Part B): A celebration of the establishment of the International Research Center of Big Data for Sustainable Development Goals (CBAS)
Submit an article Journal homepage
4,119
Views
4
CrossRef citations to date
0
Altmetric
Original Research Article

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

Zahra AssarkhanikiDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne, AustraliaORCID Iconhttps://orcid.org/0000-0001-9769-0404View further author information
ORCID Icon,
Soheil SabriDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne, AustraliaORCID Iconhttps://orcid.org/0000-0002-2167-2717View further author information
ORCID Icon &
Abbas RajabifardDepartment of Infrastructure Engineering, The University of Melbourne, Melbourne, AustraliaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-4256-3173View further author information
ORCID Icon
Pages 497-526 | Received 14 Apr 2021, Accepted 21 Jun 2021, Published online: 20 Sep 2021

References

  • Abdi, A. M. (2020). Land Cover and Land Use Classification Performance of Machine Learning Algorithms in a Boreal Landscape Using Sentinel-2 Data. GIScience and Remote Sensing, 57(1), 1–20.
  • Alzamil, W. S. (2018). Evaluating Urban Status of Informal Settlements in Indonesia: A Comparative Analysis of Three Case Studies in North Jakarta. Journal of Sustainable Development, 11(4), 148.
  • Assarkhaniki, Z., Rajabifard, A., & Sabri, S. (2020). The Conceptualisation of Resilience Dimensions and Comprehensive Quantification of the Associated Indicators: A Systematic Approach. International Journal of Disaster Risk Reduction, 51(December), 101840. doi: https://doi.org/10.1016/j.ijdrr.2020.101840.
  • Bank, T. W. (2015). DKI Jakarta: Informal Settlement Mapping Jakarta. http://pubdocs.worldbank.org/en/408201444418421010/Output-DKI-JAKARTA-INFORMAL-SETTLEMENT-MAPPING.
  • Bank, T. W. (2016). Indonesia: Improving Infrastructure for Millions of Urban Poor 2016. https://www.worldbank.org/en/news/press-release/2016/07/12/indonesia-improving-infrastructure-for-millions-of-urban-poor.
  • Barrington-Leigh, C., & Millard-Ball., A. (2017). The World’s User-Generated Road Map Is More than 80% Complete. PLoS ONE, 12(8), 1–20.
  • Blaschke, T. (2010). Object Based Image Analysis for Remote Sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
  • BPS-Statistics Indonesia. (2005). Badan Pusat Statistik. https://www.bps.go.id/.
  • Breiman, L. (1996). Bagging Predictions. Machine Learning, 24, 123–140.
  • Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
  • Brodley, C. E., & Friedl, M. A. (1997). Decision Tree Classification of Land Cover from Remotely Sensed Data. Remote Sensing of Environment, 61(3), 399–409.
  • Campos-Taberner, M., Francisco Javier, G. H., Beatriz, M., Emma, I.-V., Clement, A., Gustau, C.-V., & María, A. G. (2020). Understanding Deep Learning in Land Use Classification Based on Sentinel-2 Time Series. Scientific Reports, 10(1), 1–12.
  • Carleer, A. P., & Wolff, E. (2006). Urban Land Cover Multi ‐ Level Region ‐ Based Classification of VHR Data by Selecting Relevant Features. International Journal of Remote Sensing, 1161. doi:https://doi.org/10.1080/01431160500297956.
  • Counihan, C. H. (2017). An Incremental Intervention. In Jakarta : An Empowering Infrastructural Approach For Upgrading Informal Settlements, Masters Theses. 500. doi: https://doi.org/10.7275/10099007.
  • Dittakan, K., Coenen, F., Christley, R., & Wardeh, M. (2013). Population Estimation Mining Using Satellite Imagery. In Data Warehousing and Knowledge Discovery: 15th International Conference DaWaK, Prague, Czech Republic, August 26-29, 2013, L. Bellatreche & M. K. Mohania. (Eds), 8057 LNCS:285–96. Springer. doi:https://doi.org/10.1007/978-3-642-40131-2_25.
  • Dixon, B., & Candade, N. (2008). Multispectral Landuse Classification Using Neural Networks and Support Vector Machines : One or the Other, or Both? International Journal of Remote Sensing, 29, 4.
  • Dovey, K., & King, R. (2011). Forms of Informality: Morphology and Visibility of Informal Settlements. Built Environment, 37(1), 11–29.
  • Du, S., Zhang, F., & Zhang, X. (2015). Semantic Classification of Urban Buildings Combining VHR Image and GIS Data: An Improved Random Forest Approach. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 107–119.
  • Dubovyk, O., Sliuzas, R., & Flacke, J. (2011). Spatio-Temporal Modelling of Informal Settlement Development in Sancaktepe District, Istanbul, Turkey. ISPRS Journal of Photogrammetry and Remote Sensing, 66(2), 235–246.
  • Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery. Remote Sensing of Environment, 118, 259–272.
  • Estima, J., & Painho, M. (2015). Investigating the Potential of OpenStreetMap for Land Use/Land Cover Production: A Case Study for Continental Portugal. In J. J. Arsanjani, A. ZipfPeter, & H. MooneyMarco (Eds), OpenStreetMap in GIScience: Experiences, Research, and Applications (pp. 273–293). Springer Switzerland.
  • Fallatah, A., Jones, S., & Mitchell, D. (2020). Object-Based Random Forest Classification for Informal Settlements Identification in the Middle East: Jeddah a Case Study. International Journal of Remote Sensing, 41(11), 4421–4445.
  • Filho, L., Walter, C. S., Paço, A., Mifsud, M., Ávila, L. V., Brandli, L. L., Molthan-Hill, P., et al. (2019). Sustainable Development Goals and Sustainability Teaching at Universities: Falling behind or Getting Ahead of the Pack? Journal of Cleaner Production, 232, 285–294.
  • Foody, G. M., & Arora, M. K. (1997). An Evaluation of Some Factors Affecting the Accuracy of Classification by an Artificial Neural Network. International Journal of Remote Sensing, 18(4), 799–810.
  • Forget, Y., Linard, C., & Gilbert, M. (2018). Supervised Classification of Built-up Areas in Sub-Saharan African Cities Using Landsat Imagery and Openstreetmap. Remote Sensing, 10(7), 1–16.
  • Gamba, P., Member, S., Acqua, F. D., Lisini, G., Trianni, G., & Member, S. (2007). Improved VHR Urban Area Mapping Exploiting Object Boundaries. IEEE Transactions on Geoscience and Remote Sensing, 45(8), 2676–2682.
  • Garr, D. (1986). Kampung Improvement Program: Policy Issues and Local Impacts for Secondary Cities. JPER, 9(1), 79–83.
  • Goodchild, M. F. (2007). Citizens as Sensors: The World of Volunteered Geography. GeoJournal, 69(4), 211–221.
  • Gram-Hansen, B. J., Azam, F., Helber, P., Coca-Castro, A., Bilinski, P., Varatharajan, I., & Kopackova, V. (2019). Mapping Informal Settlements in Developing Countries Using Machine Learning and Low Resolution Multi-Spectral Data. In AI/ACM Conference on AI, Ethics, and Society, 361–368. Honolulu, doi:https://doi.org/10.1145/3306618.3314253.
  • Haklay, M. (2010). How Good Is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning. B, Planning & Design, 37(4), 682–703.
  • Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., & Hobart, G. W. (2018). Disturbance-Informed Annual Land Cover Classification Maps of Canada’s Forested Ecosystems for a 29-Year Landsat Time Series. Canadian Journal of Remote Sensing, 44(1), 67–87.
  • Hernandez, I., Ruiz, E., & Shi, W. (2018). A Random Forests Classification Method for Urban Land-Use Mapping Integrating Spatial Metrics and Texture Analysis. International Journal of Remote Sensing, 39, 4.
  • Hidayati, I., Yamu, C., & Tan, W. (2020). Realised Pedestrian Accessibility of an Informal Settlement in Jakarta, Indonesia. Journal of Urbanism, 1–23. doi: https://doi.org/10.1080/17549175.2020.1814391.
  • Hillson, R., Coates, A., Alejandre, J. D., Jacobsen, K. H., Ansumana, R., Bockarie, A. S., … Stenger, D. A. (2019). Estimating the Size of Urban Populations Using Landsat Images: A Case Study of Bo, Sierra Leone, West Africa. International Journal of Health Geographics, 18, 1–24. doi:https://doi.org/10.1186/s12942-019-0180-1.
  • Hofmann, P., Strobl, J., Blaschke, T., & Kux, H. (2008). Detecting Informal Settlements from QuickBird Data in Rio de Janeiro Using an Object-Based Approach. In Object-Based Image Analysis, T. Blaschke, S. Lang, & G. J. Hay. (Eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/https://doi.org/10.1007/978-3-540-77058-9_29.
  • Jain, S. (2007). Use of IKONOS Satellite Data to Identify Informal Settlements in Dehradun, India. International Journal of Remote Sensing, 28(15), 3227–3233.
  • Jokar Arsanjani, J., Helbich, M., & Bakillah, M. (2013). Exploiting Volunteered Geographic Information To Ease Land Use Mapping of an Urban Landscape. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4/W1, 51–55. doi:https://doi.org/10.5194/isprsarchives-xl-4-w1-51-2013.
  • Jones, P. (2017). Formalizing the Informal: Understanding the Position of Informal Settlements and Slums in Sustainable Urbanization Policies and Strategies in Bandung, Indonesia. Sustainability. doi:https://doi.org/10.3390/su9081436.
  • Jones, P. (2019). The Shaping of Form and Structure in Informal Settlements: A Case Study of Order and Rules in Lebak Siliwangi, Bandung, Indonesia. Journal of Regional and City Planning, 30(1), 43–61.
  • Kamalipour, H. (2020). Improvising Places: The Fluidity of Space in Informal Settlements. Sustainability (Switzerland), 12, 6.
  • Karlsson, A. (2003). Classification of High Resolution Satellite Images Chalmers University of Technology.
  • Khorram, S., Nelson, S., & Cakir, H. (2013). Digital Image Processing: Post-Processing and Data Integration. In J. N. Pelton, S. Madry, & S. Camacho-Lara (Eds), Handbook of Satellite Applications (pp. 839–864). New York: Springer. doi:https://doi.org/10.1007/978-1-4419-7671-0.
  • Kit, O., Lüdeke, M., & Reckien, D. (2012). Texture-Based Identification of Urban Slums in Hyderabad, India Using Remote Sensing Data. Applied Geography, 32(2), 660–667.
  • Lavigne, P. D., & Durand-Lasserve., A. (2009). Land Governance and Security of Tenure in Developing Countries. white paper. French Development Cooperation.
  • Loyd, C. (2013). Landsat 8 Bands. NASA Landsat Science, 2013 https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands
  • Lu, Z., Szafron, D., Greiner, R., Lu, P., Wishart, D. S., Poulin, B., … Eisner, R. (2004). Predicting Subcellular Localization of Proteins Using Machine-Learned Classifiers. Bioinformatics, 20(4), 547–556.
  • Lu, Z., Jungho, I., Quackenbush, L., & Halligan, K. (2010). Population Estimation Based on Multi-Sensor Data Fusion. International Journal of Remote Sensing, 1161. doi:https://doi.org/10.1080/01431161.2010.496801.
  • Lv, Z., Liu, T., & Benediktsson, J. A. (2020). Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 58(9), 6524–6533.
  • Mahabir, R., Agouris, P., Stefanidis, A., Croitoru, A., T.Crooks, A. (2020). Detecting and mapping slums using open data: a case study in Kenya. International Journal of Digital Earth, 13(6), 683–707. DOI: https://doi.org/10.1080/17538947.2018.1554010.
  • Makinde, E. O., Salami, A. T., Olaleye, J. B., & Okewusi, O. C. (2016). Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria. Geoinformatics FCE CTU, 15(2), 59–70.
  • Mayunga, S. D., Coleman, D. J., & Zhang, Y. (2007). A Semi-Automated Approach for Extracting Buildings from QuickBird Imagery Applied to Informal Settlement Mapping. International Journal of Remote Sensing, 28(10), 2343–2357.
  • Mclaren, R., Coleman, D., & Mayunga, S. (2005). Sustainable Management of Mega Growth in Megacities. Proceedings of the FIG Working Week, Cairo, Egypt, http://www.fig.net/pub/cairo/papers/ts_19/ts19_01_mclaren_etal.pdf.
  • Ming, D., Zhou, T., Wang, M., & Tan, T. (2016). Land Cover Classification Using Random Forest with Genetic Algorithm-Based Parameter Optimization. Journal of Applied Remote Sensing, 10(3), 035021.
  • Movia, A., Beinat, A., & Crosilla, F. (2016). Shadow Detection and Removal in RGB VHR Images for Land Use Unsupervised Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 485–495.
  • Nilsson, N. J. (1965). Learning Machines. New York: McGrawHill.
  • Noi, P. T., & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 18, 18.
  • Novack, T., Kux, H. J. H., Feitosa, R. Q., Costa, G. A., Sensing, R., José, S., … Iv, W. G. (2003). Per Block Urban Land Use Interpretation Using Optical VHR Data and the Knowledge-Based System Interimage. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 38, 4/C7.
  • Ogrosky, C. E. (1975). Population Estimates from Satellite Imagery. Photogrammetric Engineering and Remote Sensing, 41, 707–712.
  • Ouyang, Z.-T., Zhang, M.-Q., Xie, X., Shen, Q., Guo, H.-Q., & Zhao, B. (2011). A Comparison of Pixel-Based and Object-Oriented Approaches to VHR Imagery for Mapping Saltmarsh Plants. Ecological Informatics, 6(2), 136–146.
  • Owen, K. K., & Wong, D. W. (2013). An Approach to Differentiate Informal Settlements Using Spectral, Texture, Geomorphology and Road Accessibility Metrics. Applied Geography, 38(1), 107–118.
  • Pal, M. (2005). Random Forest Classifier for Remote Sensing Classification. International Journal of Remote Sensing, 26, 1.
  • Pal, M., & Mather, P. M. (2003). An Assessment of the Effectiveness of Decision Tree Methods for Land Cover Classification. Remote Sensing of Environment, 86(4), 554–565.
  • Pangeran, A., & Akbar, R. J. 2020. Cities for Marginal Community: Lesson Learned from Indonesia’s Slum Alleviation Program.
  • Persello, C., & Stein, A. (2017). Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images. IEEE Geoscience and Remote Sensing Letters, 14(12), 2325–2329.
  • Pontarollo, N., & Mendieta Muñoz, R. (2020). Land Consumption and Income in Ecuador: A Case of an Inverted Environmental Kuznets Curve. Ecological Indicators, 108. doi:https://doi.org/10.1016/j.ecolind.2019.105699.
  • Prabhu, R., Parvathavarthini, B., & Alagu Raja, R. A. (2021). Slum Extraction from High Resolution Satellite Data Using Mathematical Morphology Based Approach. International Journal of Remote Sensing, 42(1), 172–190.
  • Ranguelova, E., Weel, B., Roy, D., Kuffer, M., Pfeffer, K., & Lees, M. (2019). Image Based Classification of Slums, Built-up and Non-Built-up Areas in Kalyan and Bangalore, India. European Journal of Remote Sensing, 52(sup1), 40–61.
  • Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine Learning Predictive Models for Mineral Prospectivity: An Evaluation of Neural Networks, Random Forest, Regression Trees and Support Vector Machines. Ore Geology Reviews, 71, 804–818.
  • Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67(1), 93–104.
  • Rodriguez-Galiano, V. F., & Chica-Rivas., M. (2014). Evaluation of Different Machine Learning Methods for Land Cover Mapping of a Mediterranean Area Using Multi-Seasonal Landsat Images and Digital Terrain Models. International Journal of Digital Earth, 7(6), 492–509.
  • Rubinowicz, P. (2000). Chaos and Geometric Order in Architecture and Design Journal for Geometry and Graphics, 4(2), 197–207.
  • Rukmana, D. (2008). The Growth of Jakarta Metropolitan Area and the Sustainability of Urban Development in Indonesia. The International Journal of Environmental, Cultural, Economic, and Social Sustainability: Annual Review, 4(1), 99–106.
  • Rumbach, A., & Shirgaokar, M. (2017). Predictors of Household Exposure to Monsoon Rain Hazards in Informal Settlements. Natural Hazards, 85(2), 709–728.
  • Rumelhart, D. E., Hintont, G. E., Hiinton, & Williams, R. J. (1986). Learning Representations by Back-Propagating Errors. Nature, 323(9), 533–536.
  • Soman, S., Beukes, A., Nederhood, C., Marchio, N., & Bettencourt, L. (2020). Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps. ISPRS International Journal of Geo-Information, 9(11), 685.
  • Statistics Indonesia- Badan Pusat Statistik (BPS). (2020). Population Jakarta. https://www.bps.go.id/.
  • Suhartini, N., & Jones, P. (2020). Better Understanding Self-Organizing Cities: A Typology of Order and Rules in Informal Settlements. Journal of Regional and City Planning, 31(3), 237–263.
  • Sweeting, D. 2017. The Informal City and Rights in South East Asian Cities: The Cases of Kampung Improvement Programme and Baan Mankong, no. 192. https://www.ucl.ac.uk/bartlett/development/sites/bartlett/files/wp192_david_sweeting.pdf.
  • Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., … Huete, A. (2018). 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. ISPRS Journal of Photogrammetry and Remote Sensing, 144(August), 325–340.
  • Toll, L. (1984). An Evaluation of Simulated Thematic Mapper Data and Landsat MSS Data For- Isc Criminating Suburban and Regional Land Use and Land Cover. Photogrammetric Engineering and Remote Sensing, 50, 12.
  • U.S. Geological Survey. (2016). Landsat 8 Data Users Handbook NASA 8. https://landsat.usgs.gov/documents/Landsat8DataUsersHandbook.pdf.
  • UNDP. (2016). Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators.
  • UN-Habitat. (2020). World Cities Report 2020: The Value of Sustainable Urbanization. Nairobi.
  • UN-Habitat. (2007). UN-HABITAT and the Kenya Slum Upgrading Programme Nairobi.
  • UN-Habitat. (2013a). State of the World’s Cities 2012–2013: Prosperity of Cities Nairobi.
  • UN-Habitat. (2013b). Streets as Public Spaces and Drivers of Urban Prosperit. Nairobi.
  • UN-Habitat. (2014). Promoting Resilient Housing and Secure Tenure in a Changing Climate
  • UN-Habitat. (2015). The State of Asian and Pacific Cities 2015: Urban Transformations Shifting from Quantity to Quality. https://unhabitat.org/books/the-state-of-asian-and-pacific-cities–2015/.
  • United Nations. (2015a). Paris Agreement. New York.
  • United Nations. (2015b). Addis Ababa Action Agenda. New York.
  • United Nations. (2015c). Sendai Framework for Disaster Risk Reduction 2015–2030. https://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf.
  • United Nations. (2015d). Transforming Our World: The 2030 Agenda for Sustainable Development
  • United Nations. (2017). New Urban Agenda. United Nations (Habitat III). www.habitat3.org.
  • United Nations. (2018). World Urbanization Prospects: The 2018 Revision. Department of Economic & Social Affairs. New York. doi:https://doi.org/10.4054/demres.2005.12.9.
  • UNSD. (2020). Global Indicator Framework for the Sustainable Development Goals and Targets of the 2030 Agenda for Sustainable Development. Work of the Statistical Commission Pertaining to the 2030 Agenda for Sustainable Development. https://unstats.un.org/sdgs/indicators/Global.Indicator.Framework.after.2019.refinement_Eng.pdf%0Ahttps://unstats.un.org/sdgs/indicators/Global.Indicator.Framework_A.RES.71.313.Annex.pdf.
  • Verma, D., Jana, A., & Ramamritham, K. (2019). Transfer Learning Approach to Map Urban Slums Using High and Medium Resolution Satellite Imagery. Habitat International, 88(April), 101981.
  • Volpi, M., Tuia, D., Bovolo, F., Kanevski, M., & Bruzzone, L. (2013). Supervised Change Detection in VHR Images Using Contextual Information and Support Vector Machines. International Journal of Applied Earth Observations and Geoinformation, 20, 77–85.
  • Voorde, T., Van, D., William, D. G., & Canters, F. (2007). Improving Pixel-Based VHR Land-Cover Classifications of Urban Areas with Post-Classification Techniques. In Photogramm. Eng. Remote Sensing (pp. 1017–1027). American Society for Photogrammetry and Remote Sensing.
  • Wang, M.-X., Huang, D., Wang, G., & Dian-Qing, L. (2020). SS-XGBoost: A Machine Learning Framework for Predicting Newmark Sliding Displacements of Slopes. Journal of Geotechnical and Geoenvironmental Engineering, 146(9), 04020074.
  • Weir, D., McQuillan, D., & Francis, R. A. (2019). Civilian Science: The Potential of Participatory Environmental Monitoring in Areas Affected by Armed Conflicts. Environmental Monitoring and Assessment, 191(10). doi:https://doi.org/10.1007/s10661-019-7773-9
  • The World Bank. (2000). Transition: World Bank Urban and Local Government Strategy Washington.
  • The World Bank. (2011). Jakarta Urban Challenges in a Changing Climate Jakarta. http://documents.worldbank.org/curated/en/132781468039870805/pdf/650180WP0Box360ange0Jakarta0English.pdf.
  • Wurm, M., Taubenböck, H., Weigand, M., & Schmitt, A. (2017). Slum Mapping in Polarimetric SAR Data Using Spatial Features. Remote Sensing of Environment, 194, 190–204.
  • Xia, L. I. (2007). Technical Note. A Method to Improve Classification with Shape Information. International Journal of Remote Sensing , 17, 8.
  • Zhang, H. K., & Roy, D. P. (2017). Remote Sensing of Environment Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Classfication. Remote Sensing of Environment, 197, 15–34.
  • Zhang, Y., Peng, D., & Huang, X. (2018). Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis. IEEE Transactions on Geoscience and Remote Sensing Letters, 15(1), 13–17.
  • Zhou, B., Okin, G. S., & Zhang, J. (2020). Leveraging Google Earth Engine (GEE) and Machine Learning Algorithms to Incorporate in Situ Measurement from Different Times for Rangelands Monitoring. Remote Sensing of Environment, 236, 111521.
  • Zhu, Z., Gallant, A. L., Woodcock, C. E., Pengra, B., Pontus Olofsson, T. R., Loveland, S. J., … Auch, R. F. (2016). Optimizing Selection of Training and Auxiliary Data for Operational Land Cover Classification for the LCMAP Initiative. ISPRS Journal of Photogrammetry and Remote Sensing, 122, 206–221.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.