DLR - Earth Observation Center - DLR-SkyScapes: Aerial Semantic Segmentation Dataset for HD-mapping

High-Definition(HD) mapping is in many applications from autonomous driving to infrastructure monitoring, and urban management essential for the understanding of complex urban infrastructure with centimeter-level accuracy. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling.

Robin Lovelace (@[email protected])

Attached: 1 image Aerial/satellite imagery from Google and other providers is now good enough to allow estimation of road widths. Anyone with Google Earth Engine experience up for helping to figure our how? Existing codebase for rivers: https://github.com/seanyx/RivWidthCloudPaper #geo #geocomputation #geocompx

Geoportal.gov.pl

Search and Retrieve Spatial Data from GUGiK

Automatic open data acquisition from resources of Polish Head Office of Geodesy and Cartography (Główny Urząd Geodezji i Kartografii) (<https://www.gov.pl/web/gugik>). Available datasets include various types of numeric, raster and vector data, such as orthophotomaps, digital elevation models (digital terrain models, digital surface model, point clouds), state register of borders, spatial databases, geometries of cadastral parcels, 3D models of buildings, and more. It is also possible to geocode addresses or objects using the geocodePL_get() function.