This involved reviewing three systems – the specimen management system HOLTZE, the Vegetation Site Database and the Flora NT external website – to develop an understanding of the current state.  Through workshops and interviews a baseline of the system capacity was established, as well as the strategic imperatives for the future of the systems.  This project also included considerable review of data types and data standards within these systems.

Once this was determined, an evaluation of the options for moving these systems forward was developed, that was undertaken against a robust evaluation framework, developed from the baseline.

Through an interactive process, the roadmap was then developed in conjunction with the key NT government decision making staff, so that a clear path towards the future state could be developed collaboratively.

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• Developing interactive web maps to visualise project locations and key data points for New Energy initiatives.
• Creating customised GIS applications to assist in project planning, site selection, and resource optimization.
• Conducting geospatial analysis to identify suitable sites for renewable energy installations.
• Managing and maintaining a geospatial database, ensuring data accuracy and accessibility for project teams.

Through these projects, we’ve gained valuable insight into the new energy industries. Our work has encompassed ventures like hydrogen, ammonia and solar energy generation and expanded on our efforts in the environmental data standards arena. By working with Woodside Energy, we’ve established our expertise across multiple facets of GIS in the energy domain.

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A number of these scanned forms were run through Clio in two runs – the first run saw the system return a lower accuracy of 73%, but after working through the forms and tuning the models in the AI system, we were able to get it to between 93% and 99% in the second run.

This approach of using a targeted and specific AI tool helped the Department of Environment, Parks and Water Security to capture information from field sheets in a much faster, less error-prone method than the current manual system that they previously employed.

You can read more about the project on our blog.

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