Cartographic representation
Cartographic representation
part of the Hydrography and Multiscale representation themes from CEGIS
Cartographic representation, or symbolization, is how information is displayed on a map.
Cartographic representation, or symbolization, is how information is displayed on a map.
Symbols on topographic maps represent features on the earth’s surface such as elevation, water, vegetation, roads, and buildings. Symbols are drawn with a level of detail that legibly displays the features within a map extent, which is the area on the ground shown in the map.
Water has long been represented on maps with blue lines for streams and blue areas for lakes and other water bodies. Feature symbols are stored digitally and can have descriptive information attached to them, such as stream name or width. Digital information displayed on maps is a type of geospatial data.
Feature symbolization changes as we zoom in or out on a map to see features at different scales. The process of determining how many and which symbols, and what level of detail you see at a particular zoom level, is called cartographic generalization.
Generalization requires many calculations and is done differently for different data types. For example, generalization of a stream symbol could provide a very detailed line when zoomed in close, but a much simpler line when zoomed out, which shows more of its length but represents the same stream.
Publications
You will find here a sampling of publications. More are available and are being published throughout the year.
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At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution
Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a...Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska
The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different...Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines Generalization quality metrics to support multiscale mapping: Hausdorff and average distance between polylines
Large geospatial datasets must often be generalized for analysis and display at reduced scales. Automated methods including artificial intelligence and deep learning are being applied to this problem, but the results are often analyzed on the basis of limited and subjective measures. To better support automation, a project is underway to develop a robust Python toolkit for computing...Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska
The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution...Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems...
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CEGIS - Denver, Colorado
CEGIS - Rolla, Missouri