The Surface Hydrology Points (Regional) dataset provides a set of related features classes to be used as the basis of the production of consistent hydrological information. This dataset contains a geometric representation of major hydrographic point elements - both natural and artificial. This dataset is the best available data supplied by Jurisdictions and aggregated by Geoscience Australia it is intended for defining hydrological features.

This project consists of data that has been reprocessed by RPS and AAM for the purpose of creating an improved Victorian coastal DEM including contours based on the original data acquired in 2007. The purpose of this project is to reclassify the original level 2 classification LiDAR data into level 3 for input to a higher accuracy ICSM Level 3 classification (Level 3 DEM). LiDAR (Light Detection and Ranging) is an airborne remote sensing technique for rapid collection of terrain data. The sensor used for this LiDAR project collected XYZ and Intensity data for first and last return by bouncing a pulse from the aircraft to the surface that enables the height and intensity values to be calculated. From the raw LiDAR data, a suite of elevation products was generated including DEM and Contours. Project Products: DEM, Contours, raw LiDAR.

ASEG 2015 digital data for conference delegates stored on 8GB USB stick. Two directories - GIS_data & PDF_maps. The GIS_data diectory contains AEM data from the three OESP AEM surveys; 6th edition magnetic grid, 3rdd edition radiometric grids and bandpass filtered and isostatic gravity grids at a national scale; locations of onshore seismic lines (Geocat 32407). A sub-directory containing survey metadata on open-file airborne geophysical surveys in MapInfo/Shape formats. A sub-directory containing mineral occurrence data in MapInfo/Shape formats. A sub-directory containing surface geology datasets a 1:1M and 1:2.5M scales. A sub-directory containing the Global Map 1:1M scale (Geocat 48006) dataset of administrative boundaries, drainage, transportation and population centres. The PDF_Maps directory contains A0 & A3 scale maps of the national magnetic, radiometric and gravity datasets. Maps of mines and mineral occurrences at a national scale and index maps of the airborne geophysical and gravity surveys coverage of the continent.

This product is a promotional flyer to showcase some of the cpaability and capacity of the regional development section, with a focus on the PNG work.

The Geoscience Australia Strategic Plan outlines the agency's strategic priorities, value streams, and statement of intent over the next 3 years.

Poster display for the 2015 AMEC convention

This booklet focuses on volcanoes, their formation process, types, shapes, locations and effects on their surroundings. Significant volcano events around the world are covered, and special emphasis is placed on the Australian volcanic landscape. Student activities included. Recommended for Years 6 and 9.

The accuracy of spatially continuous environmental data, usually generated from point samples using spatial prediction methods (SPMs), is crucial for evidence-informed environmental management and conservation. Improving the accuracy by identifying the most accurate methods is essential, but also challenging since the accuracy is often data specific and affected by multiple factors. Because of the high predictive accuracy of machine learning methods, especially random forest (RF), they were introduced into spatial statistics by combining them with existing SPMs, which resulted in new hybrid methods with improved accuracy. This development opened an alternative source of methods for spatial prediction. In this study, we introduced these hybrid methods, along with the modelling procedure adopted to develop the final predictive models. These methods were compared with the commonly used SPMs in R using cross-validation techniques based on both marine and terrestrial environmental data. We also addressed the following questions: 1) whether they are data-specific for marine environmental data, 2) whether input predictors affect their performance, and 3) whether they are equally applicable to terrestrial environmental data? This study provides suggestions and guidelines for the application of these hybrid methods to spatial predictive modelling not only in environmental sciences, but also in other relevant disciplines.

Digital elevation models (DEMs) provide basic information about the shape of the earth. This information is fundamental for understanding and modelling natural hazards and their consequences. The low resolution and accuracy of the current generation of freely available global DEMs means inappropriate use can lead to dangerously misinformed disaster management decisions being made, particularly at the local level. Investing in high resolution, high accuracy global DEMs and making them freely available to the global community would have many benefits to society, one of which would be better informed disaster management decision making.

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