  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.
