The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 17–21, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-17-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 17–21, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-17-2020

  21 Aug 2020

21 Aug 2020

LAND USE CLASSIFICATION USING DEEP MULTITASK NETWORKS

J. R. Bergado, C. Persello, and A. Stein J. R. Bergado et al.
  • Department of Observation Science, ITC, University of Twente, The Netherlands

Keywords: Land Use Classification, VHR Imagery, Multitask Learning, Convolutional Networks

Abstract. Updated information on urban land use allows city planners and decision makers to conduct large scale monitoring of urban areas for sustainable urban growth. Remote sensing data and classification methods offer an efficient and reliable way to update such land use maps. Features extracted from land cover maps are helpful on performing a land use classification task. Such prior information can be embedded in the design of a deep learning based land use classifier by applying a multitask learning setup—simultaneously solving a land use and a land cover classification task. In this study, we explore a fully convolutional multitask network to classify urban land use from very high resolution (VHR) imagery. We experimented with three different setups of the fully convolutional network and compared it against a baseline random forest classifier. The first setup is a standard network only predicting the land use class of each pixel in the image. The second setup is a multitask network that concatenates the land use and land cover class labels in the same output layer of the network while the other setup accept as an input the land cover predictions, predicted by a subpart of the network, concatenated to the original input image patches. The two deep multitask networks outperforms the other two classifiers by at least 30% in average F1-score.