Detecting Urban Deforestation: A Semantic Segmentation Approach

Abstract. The increase in built-up areas within metropolitan perimeters has far-reaching negative effects. The increase in buildings, suburbs, and roads leads to reductions in native environment areas, contributing to the phenomenon of urban heat-islands (UHIs). According to the World Bank (World Bank Group, 2023), the temperature of south-eastern Asian cities is on average 1.6 to 2.0 degrees warmer than their surroundings, with some cities reaching 5.9 degrees warmer than the surrounding areas. Extreme urban heat has negative consequences including reducing city Gross Domestic Product (GDP) and increasing the demand for electric energy. Furthermore, it can lead to the accumulation of greenhouse gases in the local area and can cause heat-related health issues, which can even result in death (Environmental Protection Agency, 2008). Measuring the decrease of natural areas within cities is a complicated task which involves government monitoring and accurate mapping. This work aims to accurately detect the reduction in natural areas within the city of Rio de Janeiro using semantic segmentation machine learning techniques. The models used were able to detect vegetation and urban areas with over 90% accuracy.