Traditional remote sensing classification approaches use the colours (spectral bands) of individual image pixels or dots to determine what the feature is. The problem is that different features can have similar colours such as banana plantations and rain forests or even grasslands as they are all green. In interpreting land use, humans also take into account the context of the whole image so we can see that the banana plants are planted in rows, near roads and packing sheds and have a distinct leaf texture. This is why using computer vision has been so successful; the computer learns about all the components which make up a banana plantation, not just the colour.
Traditional approaches are also resource intensive, the benefits of computer vision are their capacity to process big data, which is increasingly being acquired at ever greater spatial and temporal resolutions, to output extremely timely land use information.

As of October 2018, we are writing a paper on the proof of concept with the hope it will be peer reviewed and published to aid the scientific community. We are also seeking to operationalise the method into the land use mapping program and extend its capability to inform other land use classes. Initial work has commenced to automate sugar cane crop mapping with promising results.

We are looking for opportunities to work with other states and territories of Australia to assist with their mapping programs and international collaborations to use these technologies to assist other countries to map their features of interest.

The Queensland Government initially collaborated with a private company, Envista, in the initial proof of concept stage. Envista assisted with their computer vision experience to accelerate our learning in this area and introduced us to a new state-of-the art algorithm.
We have also started initial collaborations with the New South Wales Government and the Australian Federal Government to apply our methods to other parts of Australia.

Other Queensland Government agencies, industry groups and catchment managers can now can access an updated banana plantation map. This is of great benefit to the current biosecurity response and containment incident affecting the banana industry in Queensland.
The Queensland Government is part of the Australian Collaborative Land Use and Management Program so other states and territories will be able to access this technology and apply it to their own land use mapping programs.

Using a stratified random sample of points throughout the catchment, the accuracy assessment of banana and non-banana plantations was 97% accurate. Assessing the banana plantations separately from other land use features we found the model was 80% accurate. The main confusion the model had was separating banana plantation from other tree fruit crops such as paw paws. Further training of the model using additional data will likely resolve some of these issues and increase the accuracy. We anticipate expanding the methodology to other land use classes to create a fully or semi automated land use map of Queensland. This could potentially reduce the time it takes one person to map the state of Queensland from 30 years to less than a year.

The biggest challenge was the lack of literature in this application. This is a new areas science and limited research has been conducted.
Managing expectations of colleagues and other interested parties was also a challenge. These methods do not work without training and training data. I had to manage the expectation held by some colleagues that this technology can map anything and everything automatically.
Securing on-going funding to continue this research and to operationalise and integrate this method into the existing land use mapping program has been a challenge. Although this is cutting edge science producing encouraging results, the current economic environment has restricted the available resources to continue this work.

This work is computationally intensive and high end Graphics Processing Units (GPUs) are required to process the data. We found using a GPU (Nvidia Tesla P100) was 70 times faster than using the computers Central Processing Unit (CPU) (Intel Xeon).
Support from management and securing resourcing to conduct research into new methods is imperative.
The results have been encouraging to further pursue the work, the potential efficiencies to expand the computer vision into other land use classes is real.

These methods have started to be replicated to other applications such as mapping sugar cane plantations and woody vegetation. It is anticipated these methods could be used to map other features in satellite imagery and aerial photography such as fire scar mapping, woody vegetation change detection and monitoring of mining and coal seam gas well infrastructure.
Computer vision has the capability to greatly assist the efficiency of compiling mapping to support large-area landscape management and monitoring programs, in support of natural resource management and monitoring by government and non-government and aid organisations.
As Queensland is part of the Australian Collaborative Land Use and Management Program (ACLUMP), this work was presented to all other states and territories at the 2018 ACLUMP workshop. This research has generated interest across the country, particularly in states who do not have enough funding to conduct a state wide land use program.

Data preparation and quality training data are required for an accurate model. Although we already had quality land use mapping data, some errors were identified and fixed to produce quality outputs.
Communication to promote the science, including tailored content suited to both technical and non-technical audiences was vital to promote the work and for support to continue.
Comprehensive field survey data has strengthened the validation of our results. Our research team is fortunate to have access to high performance computing resources and substantial archives of satellite imagery. The choice of machine learning algorithm has been critical for our project.