Skip to main content

Currently Skimming:

2 NGA CORE AREAS AND CROSS-CUTTING THEMES
Pages 11-20

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 11...
... , and radar data. For example, photogrammetry now routinely encompasses digital aerial imaging systems, with a recent emphasis on the development of medium format, single and multi-sensor cameras; high-resolution satellite imagery with better than 0.5-meter resolution; airborne LiDAR with increasing pulse and scan frequency and full waveform recording; and airborne and spaceborne radar, including imagery and InSAR for digital elevation model (DEM)
From page 12...
... and LiDAR; sensor orientation modeling using rigorous sensor modeling versus rational polynomial functions for multi-scene processing of high-resolution satellite imagery; automatic feature extraction, particularly for building extraction, topographic mapping, and utility mapping; monoplotting in the absence of stereo for close range threedimensional object reconstruction via single images and a digital elevation model; forensic measurement with consumer-grade cameras (van den Hout and Alberink, 2010) ; image sequence processing and analysis; enhanced object modeling and classification via full waveform LiDAR; biomass estimation via radar and LiDAR technologies (Kellndorfer et al., 2010)
From page 13...
... In summary, Dr. Crawford outlined future research opportunities for advanced optical remote sensing, including interdisciplinary research in data exploitation, sophisticated visualization techniques and integration with data analysis, new computational paradigms for analysis and modeling, sensor integration and sensor web applications, and integration of advanced optical sensor data with three-dimensional and four-dimensional GIS functionality.
From page 14...
... Cartography, Geodesy, and GIS and Geospatial Analysis The committee invited Robert McMaster, Dru Smith, and May Yuan to provide an overview of cartography, geodesy, and geospatial analysis, respectively, and to offer their thoughts on future research directions. This section summarizes their presentations and the discussion that followed.
From page 15...
... McMaster revisited research priorities outlined by the University Consortium on Geographic Information Science in the late 1990s. The long-term challenges were listed as spatial ontologies, geographic representation, spatial data acquisition and integration, the use of remotely acquired data in GIScience, scale, spatial cognition, analysis and modeling of spacetime data, dealing with uncertainty, and visualization.
From page 16...
... Scale itself needs to be transformed from a purely cartographic focus to include semantics and temporal dimension, a theme echoed in the discussion of GIS and geospatial analysis. Similarly, the incorporation of volunteered geographic information was raised as an important issue for both cartographic research and GIS.
From page 17...
... Regarding GIS and geospatial analysis, several breakout groups stressed the importance of the temporal dimension. A truly comprehensive space-time GIS and geospatial analytical framework remains to be developed.
From page 18...
... The working group participants noted that challenges in forecasting include predicting human behavior; the lack of theory; the prior lack of integration with geospatial data; and the incorporation of spatial analytic methods, validation techniques, and
From page 19...
... Carley's presentation, entitled "Human Terrain -- Assessing, Visualizing, Reasoning, Forecasting," and the discussion that followed. Data fusion is the aggregation, integration, and conflation of geospatial data across time and space with the goal of removing the effects of data measurement systems and facilitating spatial analysis and synthesis across information sources.
From page 20...
... Fusion challenges identified by the working group participants were integrating data across spatial scales; dealing with semantic interoperability; conflation; dealing with sensors with different resolutions or spatial frameworks; and integrating at the data, information, and knowledge levels. Workshop participants also indicated that data fusion needs are still critical in remote sensing and are complicated by sensor networks.


This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.