In this talk, we present an interactive system for efficient and intuitive urban layout creation and modification. Our key inspiration comes from recent content-aware image editing methods. In a similar fashion, we use the information provided by aerial photographs and GIS or by procedural methods to generate an urban layout and provide the tools and algorithms for the efficient and interactive creation of new urban spaces and the modification and extension of existing urban areas.
The end result is the intuitive, rapid, and interactive generation of new or edited urban layouts which include visual imagery as well as a valid and feasible underlying structure including a hierarchy of streets and intersections, and parcel information. We demonstrate our system by editing and extending several existing cities, ranging from hundreds to thousands of city blocks and parcels and by quickly generating new cities in the style of other cities. We have determined a method to estimate the areal footprint of parking lots in the Great Lakes Basin as a first step toward creating a basis for enhanced parking lot regulation for decision makers at the level of the individual counties but also applicable to cities or states.
Urban sprawl combined with population growth have been the main agents responsible for this era of paved landscapes. Parking lots are the insidious partner of human built structures and often are larger than building footprints themselves, yet parking lots are virtually everywhere and largely unregulated. In this study we determine the areal footprint of parking lots in the Great Lakes Basin and outline policies to provide smart growth of parking lots.
Our presentation addresses the question: "what is the areal footprint and the ensuing economic and ecological consequences of parking lots?
To that effect, we use high resolution aerial photography and GIS to estimate the areal footprint of parking lots in relationship to: 1 the total urban area; 2 the number of parking spaces versus the population of the county; and 3 the distribution of parking spaces by land use category for a high density urban cover portion of the study area. Finally, we develop a set of metrics useful to relate our findings to urban planners and county regulators and 4 we outline the necessary steps to extrapolate our findings to the United States scale and from that draw conclusions as to a possible sustainable path to controlled growth.
February 6. Wonkook Kim, Geomatics Department. School of Civil Engineering , Purdue University. January Darion S. Grant, Geomatics Department.
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The dataset is not error-free and often requires some adjustment procedure to reduce or remove the effects of any unmodeled systematic errors. Many approaches organize the data into a Triangular Irregular Network TIN structure and perform linear interpolation of the triangular faces to extract surface information for the adjustment. Chad M.
Schaeding , Geomatics Department. Geostatistical Spatial Data Fusion. Tentative speakers for the rest of the semester. Schulze, Professor, Department of Agronomy. Daniel G. Aliaga , Department of Computer Science. April 2 Prof. Schulze, Agronomy Department with contribution from Phillip R.
March 26 Prof. However, most of the traditional methods have focused only on the spectral distances in calculating the local dissimilarity of samples whereas in case of image-like data the disposition of the image samples would provide useful information. As a framework of integrating spatial and spectral information in terms of image samples, the hierarchical segmentation method RHSEG is introduced and the manifold structure based on the segmentation results will be investigated.
A manifold update scheme is also proposed where the spatial information is transferred through the segmentation hierarchy. Remotely sensed data with various resolutions are available and image processing programs are easy to use. Developing map products with remote sensing data is a convenient task.
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However, little attention has been given to the effects of classification accuracy on the applications of map products. Because all image processing or classification inevitably introduces errors into the resultant thematic maps, any subsequent quantitative analyses will reflect these errors. Unfortunately, the error propagation processes have nonlinear relationships with classification accuracy.
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