Abstract
Urban Functional Zone Mapping With a Bibranch Neural Network via Fusing Remote Sensing and Social Sensing Data
Highlights
Urban functional zones (UFZs) are the partitions of urban spaces according to the distribution of various human activities and social services and are the basic units of urban planning and resource allocation
Various machine learning (ML)-based and deep learning (DL)-based methods were proposed for automatic UFZ mapping and achieved remarkable results
We further proposed a bi-branch deep neural network (DNN), namely the bi-branch neural network (BibNet), which utilizes two different neural network branches to comprehensively learn features of remote sensing images and POI data and fuses these features to map the UFZ more accurately
Summary
UFZs are the partitions of urban spaces according to the distribution of various human activities and social services and are the basic units of urban planning and resource allocation. Various ML-based and DL-based methods were proposed for automatic UFZ mapping and achieved remarkable results These methods efficiently learn the mapping function of samples and labels from the labeled training data set in a supervised learning manner and use the learned model to make predictions. Some studies adopted the semisupervised learning strategy to leverage large amounts of unlabelled data to track the lack of labeled samples problem These semi-supervised learning-based methods select samples, automatically generate a new dataset from large amounts of unlabeled data, and achieve competitive performance through training on a large number of labeled samples. We proposed conceptual mapping rules to map the land use data in the crowdsourced OSM knowledge base to the predefined UFZ categories to generate training samples using remote sensing and social sensing data.
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