# The identification of garbage dumps in the rural areas of Cyprus through the application of deep learning to satellite imagery

arXiv: https://arxiv.org/abs/2308.02502 · HTML: https://mlsanity.com/paper/2308.02502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches](https://mlsanity.com/paper/1803.01164/) (2018)
- [DeepSat - A Learning framework for Satellite Imagery](https://mlsanity.com/paper/1509.03602/) (2015)
- [Zoo-Tuning: Adaptive Transfer from a Zoo of Models](https://mlsanity.com/paper/2106.15434/) (2021)
- [Land Use Classification in Remote Sensing Images by Convolutional Neural Networks](https://mlsanity.com/paper/1508.00092/) (2015)
- [Mapping illegal waste dumping sites with neural-network classification of satellite imagery](https://mlsanity.com/paper/2110.08599/) (2021)

## Abstract (arXiv)

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.