Semi-Automatic Modeling of Buildings from Digital Surface Models

Semi-automatic building detection and extraction is a topic of growing interest due to its potential application in such areas as cadastral information systems, cartographic revision, and GIS. One of the existing strategies for building extraction is to use a digital surface model (DSM) represented by a cloud of known points on a visible surface, and comprising features such as trees or buildings. Conventional surface modeling using stereo-matching techniques has its drawbacks, the most obvious being the effect of building height on perspective, shadows, and occlusions. The laser scanner, a recently developed technological tool, can collect accurate DSMs with high spatial frequency. This paper presents a methodology for semi-automatic modeling of buildings which combines a region-growing algorithm with line-detection methods applied over the DSM.     

An Automated System for Image-to-Vector Georeferencing

Although modern imagery usually has latitude and longitude or similar coordinates to provide true world location, much potentially useful imagery lacks this information. This includes historical imagery, as well as modern imagery that may have lost its locational information through inappropriate processing. Its provision through the process of georeferencing is still primarily a manual procedure. This paper proposes an efficient, fully- automated solution for massively asymmetric image-to-vector georeferencing whereby an image of a relatively small geographic area is automatically located relative to a substantially larger vector map base. For control points, road intersections are automatically extracted from high resolution aerial or satellite imagery using the new concepts of reference circles and central pixels. For automated control point pair identification between image and vector map, an invariant point pattern matching approach is proposed based on shape invariance for similarity transforms and invariant area ratios for affine transforms. The matching algorithm necessitates only a small subset of image points and requires no additional information beyond pixel and map coordinates. Further, it tolerates inaccurate, missing and spurious points, and provides high performance with linear scalability. A final step performs transformation verification, globalization and optimization based upon an Iterative Closest Point Greedy algorithm. Experimentation shows that images covering a few city blocks with as few as 6 to 17 extracted road intersection points can be efficiently and correctly located using the road network of Dallas County, Texas, with over 80,000 intersections.     

Non-destructive sampling of juvenile abalone using epipodial tentacles and mucus: method and application

Good-quality biological material is needed to obtain intact deoxyribonucleic acid (DNA) for use in molecular techniques such as the polymerase chain reaction (PCR). Non-destructive sampling protocols of juvenile abalone Haliotis midae (7–15 months old) were tested in order to collect material for DNA extraction. DNA was successfully extracted from epipodial tentacles and mucus samples. PCR results confirmed the good quality of the DNA and the reliability of the method.     

Introduction

We introduce and test an algorithm for extracting high-point locations from statistical surface data. The algorithm uses map algebra and local neighborhood analysis via three key parameters: minimum vertical gain, vertical gain neighborhood, and horizontal separation neighborhood. Though the method is applicable to any x,y,z data set, we tested it on 1:250,000 digital elevation models (DEMs) for Arizona. The resulting high points were compared quantitatively with an independent data set of named summits from the USGS Geographic Names Information System (GNIS). The comparison showed that, on an aggregate basis, the extraction method can approximate the number and spatial pattern of high points when compared to the GNIS points. However, extraction by neighborhood analysis may consistently misdiagnose certain features, such as the edges of troughs (e.g., canyon rims).