2 Feature Extraction

Automation of the data-to-information generation process gives productivity gains, which are necessary to handle the ever-increasing volume of spatial data

Over the past decade new automated data acquisition technologies have emerged.

Under the classification of “imaging sensors”, such technologies include digital aerial photography, high-resolution satellite imaging systems, space-borne and airborne interferometric synthetic aperture radar (InSAR) and airborne laser scanning (LiDAR).

Moreover, such new technologies have not been restricted to aerial and space platforms, as we are seeing broader application of terrestrial imaging and laser scanning within mainstream surveying, mapping and GIS.

These data acquisition systems produce a wealth of primary data that require extensive processing in order to generate information products.

Feature Extraction Intensive Seminar

crcsi p2 seminar

The program has three themes

  1. Support for the application of new data acquisition systems for feature extraction in spatial information. This includes metric sensor modelling, calibration, sensor orientation and geopositioning and development of the necessary mathematical models and computational processes needed to generate the data products from which cartographic features are extracted.
  2. Automated feature extraction from fused data obtained with multisensor systems, the focus being upon imaging and ranging data from digital aerial and satellite imagery, multi- and hyperspectral imagers, LiDAR and space- and airborne InSAR.
  3. Developing feature extraction algorithms, tools and processes for accurate and reliable feature database building in end-user specified application domains in the areas of defence mapping, utility asset management, urban planning and modelling, natural resource management and GIS in general.


Cliver Fraser 2013 sqProf Clive Fraser
Science Director P2

Geoff West 2013 sqProf Geoff West

Science Director P3, Project Leader P2.01 & P3.01

chunsun zhangDr Chunsun Zhang
Project Leader P2.02

2.01 Mobile and Terrestrial Mapping

Mobile Mapping people sml This Project researches techniques and algorithms for the detection, recognition and analysis of low-level and high-level features from mobile mapping data along urban and rural transport corridors.

2.02 Urban Feature Extraction

Building detection Aitkenvale Qld This research uses both airborne imagery and LiDAR point clouds for feature extraction. The first significant deliverable will be a process to metrically combine the imagery and LiDAR point clouds to form accurate...

2.07 Woody Vegetation

This Project will produce tools and procedures to auto-generate landscape level woody vegetation features (ie spatial layers) from field and remote sensing woody vegetation data.

2.09 LiDAR Quality Assurance Tool

pretty remote sensing scans graphic The Inter-governmental Committee on Survey and Mapping (ICSM) recognised the need for standardisation of the acquisition of elevation data, particularly LiDAR, and produced the ICSM LiDAR Acquisition Specifications and Tender Template (ICSM...

2.12 Economic Value of Earth Observation From Space

gps satellite nasa 72 Earth Observations from Space (EOS) are the richest source of information about the Earth system. EOS informs activities across environment, agriculture, mining, community safety and healthcare.

2.14 Victorian River Feature Extraction

State-wide assessments of river condition have been undertaken in Victoria since 1999 using the Index of Stream Condition (ISC). The third ISC report provided a snapshot of river health for 29,000km of major rivers and streams...

Kokoda Trail

Kokoda trail 1 In 2008, Australia and Papua New Guinea committed to work together for the protection and sustainable use of the natural and cultural resources of the Owen Stanley Ranges region including the Kokoda...