Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution

Abstract

Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of Île-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide.

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• Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications
• Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification
• Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient
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• Cross-Modal Learning of Housing Quality in Amsterdam
• On What Depends the Robustness of Multi-source Models to Missing Data in Earth Observation?
• Learning a Dynamic Map of Visual Appearance