Comparing WorldView-2 and PlanetScope Imagery to Mapping Housing Types Using GEOBIA

Abstract

Abstract The mapping accuracy of housing types plays a vital role in urban planning and development. Choosing the right imagery for urban geospatial analysis matters in terms of spatial or textural resolution. Here we compare the effectiveness of different satellite imagery, namely WorldView-2 (2m resolution) and PlanetScope (3m resolution) to map housing types. The segmentation algorithm employed is SNIC (Simple Non-Iterative Clustering) while SVM (Support Vector Machine) algorithm is for classification. This study assessed the performance of these satellite platforms in capturing to extract spatial and spectral elements of each housing class and differentiating between urban villages (Kampung Kota), government-based housing, and private-based gated housing classes in the Tangerang area. WorldView-2, with its high spatial resolution, provides detailed information, allowing for precise delineation of housing boundaries and distinctive features, whereas Planetscope imagery offers better textural information for the segmentation stage. Despite the coarser details, the SVM classification algorithm achieved an overall accuracy of 65.00% using PlanetScope imagery. Comparative analysis revealed that WorldView-2 imagery outperformed PlanetScope imagery in terms of overall accuracy, with an overall accuracy of 65.52%. The higher spatial resolution of WorldView-2 enables better discrimination of housing types, resulting in more accurate classification. However, PlanetScope imagery provides valuable information, particularly for large-scale urban planning applications. The findings of this study contribute to the field of remote sensing and assist urban planners in making informed decisions regarding housing development and infrastructure planning based on available satellite imagery resources, both of which have their own advantages and disadvantages.