Spatial sciences

Research leaders in spatial intelligence

The Spatial Sciences Group conducts research to improve our understanding, management and monitoring of the environment at landscape scales.

We use remote sensing, geographic information systems, ecological modelling and multi-objective decision support systems to understand spatial variability, temporal dynamics, change and interrelationships in the environment.

We work in a diversity of natural and managed ecosystems, terrestrial, aquatic and marine environments. 

Our capability is nationally and internationally unique because of a co-location of expertise in all scales of spatial imaging from unmanned aircraft to satellites, spatial big-data analysis, and environmental management. 

Major themes of our research are:

  • Spatial variation in landscape – what occurs where and why?
  • Biodiversity and landscape composition
  • Improving environmental and resource mapping
  • Assessing land, habitat, vegetation, soil and water condition
  • Monitoring environmental change over time
  • Natural resource and wildlife management planning and decision support


Research strengths


Our people



Spatial Points blog






Seagrass forms an incredibly important ecosystem worldwide, providing a wide range of functions. However, as with many other lifeforms, their health has been threatened by human advancements. Significant declines of seagrass meadows extent (up to a third of meadows along the Adelaide Metropolitan Coastline) leading up to the early 2000s, has been due to poor …



Conference Poster – Using Deep Learning to Detect an Arid Shrub Species in UAV Imagery

This poster was presented at the Advancing Earth Observation Forum in Brisbane in August 2022, and shows the results of the second chapter of my thesis. The full resolution PDF of the poster can be viewed here. Additional information about the research can be seen in this handout. The research paper detailing the research presented …



Deep Learning in ArcGIS Pro using your GPU

ArcGIS Pro includes built in tools that allow end-to-end deep learning, all within the Arc interface – from training sample labelling, through model training and final image classification / object detection. This removes the need for coding and installing the correct versions of the required libraries for machine / deep learning in Python. However, training …



Some Useful Biodiversity Platforms for Spatial Research

The following are a few useful sites for those undertaking spatial research involving species observational data in Australia primarily. These sites may contain both the observational data as well as tools for analysing this data, with direct access to curated environmental and other data layers. 1. ALA – Atlas of Living Australia ( Provides open …