This point dataset contains the major airport control towers in Australia.

Governments at the Commonwealth, State, Territory and Local level are committed to minimising the impact of natural disasters through a variety of Disaster Risk Reduction (DRR) programs. Risk analysis is one of the processes undertaken to inform DRR decision making and policy development. It involves estimating the extent and severity of one or more natural hazards, understanding the location and characteristics of the 'elements at risk' from those hazards (also known as exposure) and modelling the vulnerability and response of those elements exposed to the subject hazards. Understanding the vulnerability of buildings is crucial in risk analysis activities, as damage to buildings can have significant direct and indirect impacts on individuals, communities, economies and the functioning of society at large. The development of quality spatially-enabled information is a key activity in the risk analysis process. After demonstrating a proof of concept in 2005-2006, Geoscience Australia has led the development of exposure information for Australia via the National Exposure Information System (NEXIS). Within NEXIS, currently available spatial and non-spatial data from various sources is routinely combined, reorganised for consistency, managed and supplied to stakeholders. The products derived from NEXIS enable risk analysis specialists and policy makers to access recent exposure information they require to analyse and assess the risk posed by the hazards in Australia. At the core of NEXIS is information about buildings. There are many challenges to developing and providing reliable information about buildings across the country. Through an offer of assistance from the ACT Government, Geoscience Australia has developed an innovative and rapid method to analyse and interpret cadastral data to estimate an important exposure attribute. This presentation describes the development of the method, the resulting benefits for exposure information in the ACT and outlines how cadastral data can improve DRR outcomes across Australia.

Note: A more recent version of this product is available. This dataset contains spatial locations in point format as a representation of Electricity Transmission Substations in Australia. For government use only. Access through negotiation with Geoscience Australia

This point dataset contains the Aviation Fire Fighting and Recue Facilities in Australia.

The National Telephone Exchanges Dataset presents the spatial locations of Australia's known Telephone Exchanges. The purpose of the National Telephone Exchanges Dataset is to support decision makers from and all levels of National, State and Local Government, Industry and other interested parties to make better, and more informed decisions based on evidence based information to underpin future emergency management planning and communications management decisions.

This point dataset contains offshore Oil and Gas Platforms located in Australian waters that include infrastructure facilities for the extraction, processing and/or storage of oil and natural gas.

The Bushfire and Natural Hazards CRC are funding the preparation of an exposure information modelling framework to identify fundamental information requirements for better evidence based disaster management. The framework will underpin the development of a comprehensive natural hazards exposure information base to assist in reducing the natural hazards risk to communities. Implementation of the framework will help to understand the exposure of people, buildings, businesses and infrastructure to natural hazards. Current exposure information provision capabilities have been reviewed to identify key issues, needs, gaps, overlaps and deficiencies. A stakeholder engagement workshop was convened to identify future information needs and to understand the trends by aligning with broader framework objectives of the National Emergency Risk Assessment Guidelines. The following are four different components of built environment exposure identified through extensive reviews and stakeholder consultation for endorsement. Information on population o remoteness, density, mobility, socio-economic status, age profile, disability status, indigenous and ethnic composition and proportion of floating and tourist population o time dependence of population, population evacuation speed, information on factors that influence human risk perception and information on existing network organisations and community groups are important for exposure information modelling. Information on Buildings o building type, code/standard, age, location, location by the exposure categories o number of storeys, floor height, elevation, orientation, façade coverage and material use o ancillary buildings Information on infrastructure o roads, bridges, airports and seaports; power distribution networks; underground water supply networks o lifeline and connected infrastructure. Information on business and land use o location of operations, type, employment, turnover, size, value and costs o primary industry agriculture, horticulture, cattle o ecological inventory, land use The collective views of data managers, researchers and end users have informed the basis for exposure information requirements to develop a consistent, standardised exposure information framework that will support vulnerability assessments for disaster risk reduction and socio-economic impact analysis.

PLEASE NOTE: There is a more recent version of this product which can be accessed via the link on the right hand pane. It has been widely recognised that Light Detection And Ranging (LiDAR) data is a valuable resource for estimating the geometry of natural and artificial features. While the LiDAR point cloud data can be extremely detailed and difficult to use for the recognition and extraction of three dimensional objects, the Digital Elevation Model and Digital Surface Model are useful for rapidly estimating the horizontal extent of features and the height variations across those features. This has utility in describing the characteristics of buildings or other artificial structures. LiDAR is an optical remote sensing technology that can measure the distance from the sensor to a target area by illuminating the target area with light, often using pulses from a laser scanner. LiDAR has many applications in a broad range of fields, including aiding in mapping features beneath forest canopies, creating high resolution digital elevation and surface models. A Digital Surface Model (DSM) represents the earth's surface and includes all objects on it, while the Digital Elevation Model (DEM) represents the bare ground surface without any natural or artificial objects such as vegetation, structures and buildings. The Building Geometry Model (BGM) application is a Python-based software system, used to execute ArcGIS geoprocessing routines developed by Geoscience Australia, which can derive the horizontal and vertical extents and geometry information of building and other elevated features from LiDAR data. The Building Geometry Model algorithms were developed in response to the availability of LiDAR data for the development of exposure information for natural hazard risk analysis. The LiDAR derivatives were used to estimate building footprint areas, inter-storey heights across areas occupied by buildings, and eventually an estimate of gross floor area of different types of buildings. The design and development of the BGM application started in February 2012 as part of a natural hazard risk analysis project in the Philippines. Many of the examples of interface usage in this document contain references to locations and terms used in the Philippines. However, the BGM application has been designed to process data regardless of its geographic location. The object-oriented programming techniques and design patterns were used in the software design and development. In order to provide users with a convenient interface to run the application on Microsoft® Windows, a Python-based Graphical User Interface (GUI) was implemented in March 2012 and significantly improved in the subsequent months. The application can be either run as a command-line program or start via the GUI. The BGM application is currently benchmarked as Version 1.0 as it is still under development. This document is a user guide to the BGM GUI. It describes the main User Interface (UI) components, functionality and procedures for running the BGM processes via GUI.

The Greater Metro Manila Area is one of the world's megacities and is home to about 12 million people. It is located in a region at risk from earthquakes, volcanic eruptions, tropical cyclones, riverine flooding, landslides and other natural hazards. Major flooding affected the Greater Metro Manila Area in September 2009 following the passage of Typhoon Ketsana (known locally as Typhoon Ondoy). Following this event, the Australian Aid Program supported Geoscience Australia to undertake a capacity building project with its partner agencies in the Government of the Philippines. The output of this project has been a series of risk information products developed by agencies in the Collective Strengthening of Community Awareness for Natural Disasters (CSCAND) group. These products quantify the expected physical damage and economic loss to buildings caused by earthquakes, tropical cyclone severe wind and riverine flooding across the Greater Metro Manila Area. Spatial data is a key input to the development of hazard models and information on exposure, or the 'elements at risk'. The development of a spatially enabled exposure database was a crucial element in the construction of risk information products for the Greater Metro Manila Area. The database provides one central repository to host consistent information about the location, size, type, age, residential population and structural characteristics of buildings within the area of interest. Unique spatial analysis techniques were employed to quantify and record important aspects of the built environment, for inclusion in the database. The process of exposure data development within the Greater Metro Manila Area, including a new application developed by Geoscience Australia for estimating the geometric characteristics of buildings from high resolution elevation data and multi-spectral imagery, will be presented.

The dataset contains spatial locations, in point format, of the Australian High Court, Australian Federal Courts and the Australian Magistrates Courts.