| dc.description.abstract | This research aims to identify and map slum settlements in the coastal area
of Medan Belawan, Medan City, utilizing a combination of remote sensing
technologies. The methodology involved Unmanned Aerial Vehicle (UAV) aerial
photo acquisition, which generated an ultra-high-resolution orthomosaic map
(2.90 cm/pixel) covering an area of 62.60 Ha. This data was subsequently analyzed
using Object-Based Image Analysis (OBIA), an advanced image analysis technique
that classifies objects based on spectral, spatial, and elevation attributes. The
identification and classification processes were directly validated through field
observations to ensure accuracy. The results of land cover classification using
OBIA identified slum settlements covering 9.51 Ha, approximately 18.6% of the
total analyzed settlement area. Further building height analysis revealed that the
majority of structures were low-rise buildings (<6 meters), constituting 92.7% of
all buildings, reflecting the characteristics of dense settlements in coastal areas
prone to tidal flooding and experiencing poor drainage. The physical
characteristics of slum settlements identified through image analysis—such as high
building density, irregular patterns, low heights, and frequently inundated
locations—were highly consistent with actual field conditions. This study
demonstrates the effectiveness of integrating UAV and OBIA technologies in
accurately identifying and mapping slum settlements, even in difficult-to-access
coastal regions. Although this method has limitations in directly identifying socioeconomic
aspects of slum conditions, the resulting physical spatial data are highly
precise. These findings are expected to provide a strong scientific basis for spatial
planning, prioritizing interventions, and implementing more targeted settlement
quality improvement programs in Medan Belawan. | en_US |
