Deep-learning coupled with novel classification method to classify the urban environment of the developing world
This work addresses the problem of classifying diverse urban environments in developing countries, which are often overlooked by existing methods focused on developed nations, providing a more nuanced understanding for urban planners.
This paper proposes a novel classification method for urban environments, specifically for developing countries, by categorizing urban areas into four classes based on formality and architectural form, with 16 sub-classes. Using Google's DeeplabV3+ model on a 50 km x 50 km Google Earth image of Dhaka, Bangladesh, the method achieved 75% accuracy and 60% Mean IoU for semantic segmentation.
Rapid globalization and the interdependence of humanity that engender tremendous in-flow of human migration towards the urban spaces. With advent of high definition satellite images, high resolution data, computational methods such as deep neural network, capable hardware; urban planning is seeing a paradigm shift. Legacy data on urban environments are now being complemented with high-volume, high-frequency data. In this paper we propose a novel classification method that is readily usable for machine analysis and show applicability of the methodology on a developing world setting. The state-of-the-art is mostly dominated by classification of building structures, building types etc. and largely represents the developed world which are insufficient for developing countries such as Bangladesh where the surrounding is crucial for the classification. Moreover, the traditional methods propose small-scale classifications, which give limited information with poor scalability and are slow to compute. We categorize the urban area in terms of informal and formal spaces taking the surroundings into account. 50 km x 50 km Google Earth image of Dhaka, Bangladesh was visually annotated and categorized by an expert. The classification is based broadly on two dimensions: urbanization and the architectural form of urban environment. Consequently, the urban space is divided into four classes: 1) highly informal; 2) moderately informal; 3) moderately formal; and 4) highly formal areas. In total 16 sub-classes were identified. For semantic segmentation, Google's DeeplabV3+ model was used which increases the field of view of the filters to incorporate larger context. Image encompassing 70% of the urban space was used for training and the remaining 30% was used for testing and validation. The model is able to segment with 75% accuracy and 60% Mean IoU.