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Multiscale Based Characterization and Classification of Urban Land-Use...

by Jacob W Arndt, Wadzanai D Lunga, Jeanette E Weaver, St Thomas M Ledoux, Sarah A Tennille
Publication Type
Conference Paper
Journal Name
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Publication Date
Page Numbers
9470 to 9473
Conference Name
2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019)
Conference Location
Yokohama, Japan
Conference Sponsor
Conference Date

Machine learning and, more recently, deep learning provide a means for generating urban land-use maps with relatively little human effort compared to manually digitizing images. This becomes important when supporting global initiatives focused on sustainability, planning, health, pro-poor policy, infrastructure, and population distribution estimates. Many of these initiatives work in areas where geospatial data is scarce, such as the global south, and often use land-use maps to help achieve their goals. In this study, we develop a typology for automated labeling of urban land-use data that captures the variation in structural patterns within cities. A comparison of classification accuracy between convolutional neural networks (CNNs) and support vector machines (SVMs) coupled with handcrafted features is conducted. Through experimental validation on two highly dense cities in Africa, we report on new insights and the potential benefits offered by both handcrafted multiscale feature functions and multiscale-CNNs even with limited training data.