  Garbage disposal is a challenging problem throughout the developed world. In
Cyprus, as elsewhere, illegal ``fly-tipping" is a significant issue, especially
in rural areas where few legal garbage disposal options exist. However, there
is a lack of studies that attempt to measure the scale of this problem, and few
resources available to address it. A method of automating the process of
identifying garbage dumps would help counter this and provide information to
the relevant authorities. The aim of this study was to investigate the degree
to which artificial intelligence techniques, together with satellite imagery,
can be used to identify illegal garbage dumps in the rural areas of Cyprus.
This involved collecting a novel dataset of images that could be categorised as
either containing, or not containing, garbage. The collection of such datasets
in sufficient raw quantities is time consuming and costly. Therefore a
relatively modest baseline set of images was collected, then data augmentation
techniques used to increase the size of this dataset to a point where useful
machine learning could occur. From this set of images an artificial neural
network was trained to recognise the presence or absence of garbage in new
images. A type of neural network especially suited to this task known as
``convolutional neural networks" was used. The efficacy of the resulting model
was evaluated using an independently collected dataset of test images. The
result was a deep learning model that could correctly identify images
containing garbage in approximately 90\% of cases. It is envisaged that this
model could form the basis of a future system that could systematically analyse
the entire landscape of Cyprus to build a comprehensive ``garbage" map of the
island.
