Review of Encyclopedia of Exploration: Invented and Apocryphal Narratives of Travel Raymond J. Howgego.

Review of Encyclopedia of Exploration: Invented and Apocryphal Narratives of Travel Raymond J. Howgego

Nature's conveyor belt - the matrix mediated biomineralization of magnetite in chitons (Mollusca)

Chitons are marine molluscs that use a variety of iron and calcium based minerals to harden their teeth, which they use to scrape algae growing upon, and within, rocks. The teeth are mounted on a long ribbon-like organ termed the radula, with immature, unmineralized teeth at the posterior end and the hardened iron-mineralized teeth at the anterior end (Fig. 1). At any one time, up to 80 individual tooth rows can be observed, with each row becoming progressively mineralized as it moves forward in a conveyor belt-like manner. The ability to study the entire mineralization process in a single animal makes these creatures ideal for the study of matrix mediated biomineralization. The chiton’s ability to mineralize iron has inspired researchers who believe that new biomimetic materials and technologies can be developed based on the principles of biomineral formation

Fine-scale analysis of biomineralized mollusc teeth using FIB and TEM

When it comes to mineral synthesis, there is a lot we can learn from nature. Although we can synthesize a range of materials in the laboratory, the experimental conditions are often constrained to particular ranges of temperature, pH, etc. Biological systems, on the other hand, seem to be able to produce individual minerals and complex composite mineral structures under a variety of conditions, many of which are far from those applied to create their synthetic counterparts. Understanding how nature does this could provide a means to produce novel biomimetic materials with potential applications in a diverse range of fields from medicine to materials engineering

The Last Whale

Stor

The Automatic Extraction of Roads from LIDAR data

A method for the automatic detection of roads from airborne laser scanner data is presented. Traditionally, intensity information has not been used in feature extraction from LIDAR data because the data is too noisy. This article deals with using as much of the recorded laser information as possible thus both height and intensity are used. To extract roads from a LIDAR point cloud, a hierarchical classification technique is used to classify the LIDAR points progressively into road or non-road. Initially, an accurate digital terrain model (DTM) model is created by using successive morphological openings with different structural element sizes. Individual laser points are checked for both a valid intensity range and height difference from the subsequent DTM. A series of filters are then passed over the road candidate image to improve the accuracy of the classification. The success rate of road detection and the level of detail of the resulting road image both depend on the resolution of the laser scanner data and the types of roads expected to be found. The presence of road-like features within the survey area such as private roads and car parks is discussed and methods to remove this information are entertained. All algorithms used are described and applied to an example urban test site

Detecting Buildings and Roof Segments by Combining LIDAR Data and Multispectral Images

A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the seed regions detected by a grey-level segmentation of the multispectral images. We describe the algorithms involved, giving examples for a test site in Fairfield (Sydney)