Potential for mapping the world’s land resources using satellites and artificial intelligence: an Australian case study in land use.

The Queensland Government is using machine learning and computer vision to automatically map and classify land use features in satellite imagery. Successfully applied to the mapping of banana plantations, the method is extremely efficient compared to current methods of mapping compilation. Using this technology the Queensland Government can accurately map and classify the land use in a timely manner, aiding response to biosecurity and natural disaster events.

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The Queensland Land Use Mapping Program (QLUMP) has been mapping and assessing land use patterns and changes throughout Queensland, Australia for twenty years. Although many advances have been made automating processes, the main methodology of QLUMP requires manual interpretation of imagery and ancillary data at the desktop, hand digitising features to map on-screen and classification of land use features by a team of skilled staff. Queensland has an area of 1.7 million square kilometres (seven times the size of Great Britain and two and a half the size of Texas) and as a result, this process is time and resource intensive. To map land use for the whole state would take one person approximately 30 years to complete.

Machine learning as a sub-discipline of artificial intelligence has in recent years progressed with enhanced computing power so that methods of computer vision and deep learning in image analysis and classification approaches are now viable. Machine learning algorithms allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range. For these reasons, machine learning facilitates the building of models from existing land use data in order to predict the land uses of a new image.

Following these principles, we used existing land use mapping of banana plantations to test the capability of computer vision to map land use features as a proof of concept. We trained the computer to map banana plantations in the Johnstone catchment in north Queensland and used the model to predict banana plantation in another area, the Tully catchment. The results were very encouraging with an overall mapping accuracy of 97%, well above the 80% requirement.

With a trained model we can now readily update banana plantation mapping and rapidly respond to critical events such as natural disasters and biosecurity incidents - Queensland is threatened episodically by tropical cyclones and recently the viability of the local banana industry has been threatened by the detection of a very damaging fungus disease, Panama Tropical Race 4.

Future work will expand this method into other land use classes in an attempt to fully or semi-automate land use mapping in Queensland. Using the manual method, it took one person approximately six weeks to map all land uses in the Tully catchment, including banana plantations. Running the trained model on the same area to map banana plantations took a matter of minutes. With a fully trained model consisting of all land uses, we estimate we could reduce the state wide mapping of land use from 30 years to less than a year.

The proof of concept using machine learning and computer vision on existing land use data was highly successful for banana plantations and it is anticipated to be just as successful for other land use classes. This is a paradigm shift for the way land use is mapped from the traditional manual hand digitising to full or semi-automation.

A rapid approach to mapping land use, land use change, land management practices and the impact of disasters such as floods, fires, tsunamis, typhoons, hurricanes and tropical cyclones would be a major benefit to the developing world and disaster zones. With machine learning in fusion with high performance supercomputing, cloud computing, big data technologies, and readily available global satellite imagery from international sources like the European Copernicus and the US Geological Survey programs combined with growing commercial providers, a new era in land resource mapping can be envisaged.

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