Open-Source GIS Applications for Flood and Landslide Assessment: Comparative Cases from Poland and Malaysia

When a river swells beyond its banks or a hillside gives way under the weight of rain, the immediate human response is often one of shock and urgency. Yet, before rescue teams can reach the most vulnerable, or planners can decide where to build the next safe shelter, someone must first understand the scale of the damage. This is the domain of disaster assessment, a field that has long relied on expensive satellite images and specialized software that only large governments or wealthy institutions could afford. But the tools of the trade are changing. Scientists are increasingly turning to a different kind of toolkit: free, open-source software and satellite data that anyone can access. The core idea is simple. By comparing pictures of the Earth taken before a disaster with those taken after, researchers can spot the changes that signal danger. They look for the sudden appearance of water where there was none, or the disappearance of green forests where a landslide has stripped the land bare. These digital clues allow experts to map out the extent of a catastrophe quickly, without needing to send people into harm's way immediately.

A recent study by researchers from Poland and Malaysia puts this approach to the test in two very different corners of the world. The team wanted to see if these free, open tools could handle the messy reality of real-world disasters. They chose two distinct events: a massive flood that hit the city of Wrocław in Poland in September 2024, and a devastating landslide in the tropical hills of Batang Kali, Malaysia, which occurred in December 2022. These two places could not be more different. Wrocław sits in a flat river valley, where the danger comes from water spreading across the ground. Batang Kali is a steep, forested slope in a humid climate, where the danger comes from the earth itself sliding downhill. Despite these differences, the researchers applied the same basic method to both. They used free satellite images from the European Union's Copernicus program and processed them using free software called QGIS. Their goal was not to replace the detailed engineering studies that come later, but to see if they could create a clear, reliable map of the damage right away.

For the flood in Wrocław, the researchers focused on finding water. They took satellite images of the city before the flood and compared them to images taken while the water was still high. By analyzing the colors reflected by the ground, they could distinguish between dry land and wet surfaces. Water reflects light differently than soil or concrete, a difference that the software could highlight. The researchers created a map that showed exactly where the water had spread, identifying not just the river itself, but the low-lying streets and neighborhoods that had become submerged. They then overlaid this water map with a digital map of the city's buildings. This allowed them to pinpoint which structures were sitting directly in the flood zone. In the city of Wrocław, they identified over 1,300 buildings that were potentially at risk. The map showed that the most vulnerable areas were those closest to the river and in the lowest parts of the valley. This process did not require a single dollar in software fees, yet it produced a clear picture of who was in danger.

The challenge in Malaysia was entirely different. In Batang Kali, the disaster was not a spreading sheet of water, but a violent slide of earth and trees. Here, the researchers could not simply look for water. Instead, they looked for the absence of green. Healthy forests reflect a specific type of light that indicates thick, living vegetation. When a landslide tears through a hillside, it strips away the trees and exposes the bare soil underneath. This change is visible in satellite images as a sudden drop in the "greenness" of the landscape. The team compared images from before the landslide with those taken months later. They found a distinct scar on the hillside where the vegetation had vanished. By measuring the difference in the green signal, they could trace the path of the landslide from the top of the slope down to the campsite at the bottom. They estimated the area of the disturbance to be about 1.24 square kilometers. This map showed not just where the slide started, but where the debris had traveled and where it had finally come to rest, burying parts of the Father's Organic Farm campground.

The study confirmed that these open-source methods work well for both types of disasters, but they also highlighted important limits. The approach is powerful because it is fast, cheap, and repeatable. Anyone with a computer and an internet connection can run the same analysis. This makes it an invaluable tool for universities, local governments, and emergency planners who need to understand a situation quickly. However, the researchers were careful to note that these maps are not perfect. In the tropical climate of Malaysia, clouds often hide the ground, and the forest grows back so quickly that the scar of the landslide can fade from the satellite view within months. In the city of Wrocław, shadows from tall buildings and the complex mix of roofs and streets can sometimes confuse the computer, making it hard to tell exactly where the water ends and the land begins. The maps produced are best seen as a first look, a preliminary assessment that tells officials where to focus their attention. They are not a substitute for the detailed, on-the-ground engineering studies that determine exactly how deep the water is or how stable a slope might be.

Ultimately, the work demonstrates that understanding natural disasters no longer requires a monopoly on expensive technology. By combining free satellite data with open software, researchers in Poland and Malaysia were able to create clear, actionable maps of two very different catastrophes. The flood in Wrocław and the landslide in Batang Kali were analyzed using the same digital logic, proving that the method is flexible enough to adapt to different environments. The results offer a practical way to support disaster risk reduction, helping to identify vulnerable buildings and dangerous slopes before the next crisis strikes. While these tools cannot predict the future or replace the need for physical inspections, they provide a transparent and accessible way to see the damage as it happens, turning raw data into a guide for saving lives and planning for a safer future.