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Found 6 papers, 1 papers with code
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations.
Mapping Vulnerable Populations with AI
Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery, to automatically identify different buildings functions and retrieve further information such as the number of building stories.
Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient
Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas.
Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution
Landuse characterization is important for urban planning.
Correcting rural building annotations in OpenStreetMap using convolutional neural networks
Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas.
An Iterative Spanning Forest Framework for Superpixel Segmentation
Superpixel segmentation has become an important research problem in image processing.
