A Dynamic LUCIS Framework for Identifying Urban-Hazard Conflicts in the Kelani River Basin Using Near Real-Time Earth Observation Data

Abstract

Rapid urbanisation in Sri Lanka frequently bypasses formal planning, resulting in concealed land-use conflicts where infrastructure encroaches on disaster-prone areas. Traditional Land-Use Conflict Identification Strategy (LUCIS) models are based on static, outdated survey data and fail to capture realtime risks. This temporal lag leads to undetected unauthorized settlements in hazard zones increasing disaster vulnerability. This study introduces a Dynamic LUCIS framework utilizing near real-time Earth Observation (EO) data from Google Earth Engine, specifically the Dynamic World and Open Buildings datasets. Focused on the Kelani River Basin, the model identifies critical risk conflicts between urban development and flood/landslide safety zones. Validation using high-resolution drone imagery yielded an overall accuracy of 82.84%, F1-score of 0.83 and a Kappa coefficient of 0.66. The results demonstrate that EO-driven models can identify unauthorized settlements in hazard-prone areas with high precision, providing a scalable tool for resilient urban governance.