Automating the hunt for illegal dumpsites in Turkey with satellite imagery
By: Guilherme M. Iablonovski
Tags: Google Earth Engine, Humanitarian, Data Science, Geospatial, Remote Sensing

Automating the hunt for illegal dumpsites in Turkey with satellite imagery

For many years now, my experience as a GIS and remote sensing specialist has been marked by projects in the humanitarian sector: the UN, World Bank, Red Cross, WRI, Translators without Borders; you name it, I probably did some work for them. Back in 2018, when I worked for Urban Resilience Platform — a French startup dedicated to developing software for solid waste management in humanitarian crises — , we were given a very simple but hard to accomplish task: help UNDP and the Turkish government identify every illegal dumpsite in Turkey.

But why, though?

Turkey’s unique geographic position with a 911 Km border with Syria, and its standing as a land migration route to Europe has resulted in the country receiving a large influx of Syrian refugees. Since the beginning of the Syrian crisis in 2016, millions have fled across the border to the Southeastern Anatolia region in Turkey. In July 2018, the number of registered Syrians in Southeastern Anatolia was over 3,5 million.

Migration flows into provinces with highest number of Syrians in Turkey by 2016. Provinces in the southeast are the ones with the most refugee camps, like Sanliurfa, Gaziantep and Osmaniye.

This has led to rapid change in the demographic structure in several Turkish border cities. In Kilis, for example, the number of Syrian refugees is greater than that of the local population.

As populations move, so does the waste they generate. With the doubling of domestic solid waste quantities, Southeastern Analotia's provinces began to face ever-increasing issues with inadequate disposal and insufficient capacity to collect and treat all the waste being generated by its new populations. That led to an imbalance in the solid waste value chain, resulting in the apparition of many new illegal dumpsites to receive the waste that could not be processed by the municipalities.

An illegal dumpsite mostly used for domestic waste in outskirts of Gaziantep.

UNDP, with its clear mandate to support municipal waste management, responded with the Effective Urban Waste Management for Host Communities Programme, which incuded key infrastructure investments, like waste transfer stations and recycling facilities.

But the diversion of waste away from uncontrolled dumpsites is only the first step in the upgrading of the whole waste collection system. UNDP also wanted to ensure those dumpsites were on course for rehabilitation. Hence the need for a methodology to identify dumpsites.

How do you process imagery for a whole country?

Since we didn’t have the means to travel the country in the hopes of bumping into well hidden dumpsites, we resorted to the use of (drum rolls…) satellite imagery!

Folks in the remote sensing field have been working with machine learning techniques way before it became the hot stuff it is now. The main difference is that they’ve been doing it in a nearly analogic way, with a lot of button clicking involved. The process usually looks something like this:

Decide which satellite imagery provider you'll use, usually with the help of a system like Maxar's Discover;

Acquire each image you'll be needing, by downloading one by one from the server or buying it (which usually takes two or three e-mail exchanges);

Train desktop softwares like ERDAS Imagine, eCognition, and ArcMap to recognize whatever land feature you're interested in.

This is what Maxar’s Discover looks like. It’s good for picking one or two images, but not great if you need to assess an entire country.

Well, it's all fun and games until you have to download and analyse images for something as large as a country.

That would require you to download a lot of images, build a mosaic with them, set the right parameters and count on your machine to be able to process it in a reasonable timeframe.

Thankfully we can now run all of these processes with fairly simple scripts, and better yet, on the cloud. That’s what Google provides with its free-to-use Earth Engine tool.

GEE user interface, where documentation, code, console and map are all in the same page.

The right spectral bands for the right objects

If you’ve ever worked with image classification before, you know that one should always have a good understanding of the composition, form and texture of whatever you’re looking at. The components of waste however, have different densities, overall sizes, chemical composition, and therefore spectral signatures. Simply put, that is to say, our targets reflect different colors (or light wave lengths) in different intensities, making it difficult to detect dumpsites as one single kind of object.

Construction waste dumpsite photographed during a field visit to Osmaniye.

So we decided to make use of an Assisted Classification Model. This — very classic — model consists of “teaching” our code what dumpsites look like with training samples in a collection of satellite imagery, and telling it to search for more. That means in order to get started, we must first decide which image collection we'll be using. Since we don't have any budget for imagery acquisition, we'll use the best free imagery we can get: Copernicus Programme's Sentinel-2, which provides for a decent spatial resolution (20m pixel, 10m if you use pan sharpening) and an even better spectral resolution (13 bands ranging from 443 to 2190 nm).

In order to do that with GEE, we declare a variable that we'll call SENTINEL and then use the ee.ImageCollection method stating we're looking for the COPERNICUS/S2 collection. We can then filter this collection to find images that actually can be of use, by setting thresholds to the percentage of clouds (<10%) and date of acquisition (Jan-2018 to Aug-2018). We also used GEE's Assets tab to upload a zipped shapefile containing our area of interest which we called southeasternturkey and then applied it as a bounds filter.