Abstract
Abstract
This study evaluates Google Earth Engine (GEE) as the central computational environment for a unified, repeatable workflow for post-event geospatial diagnostics of two contrasting natural hazards: the September 2024 flood in Lower Silesia, Poland, and the December 2022 Batang Kali landslide in Selangor, Malaysia. Both cases followed the same analytical sequence: pre-event and post-event image selection, generation of a continuous change layer, threshold-based binary mask extraction, z-score standardization of change magnitude, point-based validation, and cartographic export. The flood branch used Sentinel-2 Level-2A imagery and ΔNDWI, whereas the landslide branch used Sentinel-1 GRD SAR imagery and Δ
σ
⁰. For the Lower Silesia AOI, the workflow delineated 70.142 km² of flood-class pixels and produced complete agreement for the 240-point internal reference sample. Because the reference points were selected within the classified domain and interpreted from satellite imagery, this 100% agreement is treated as case-specific and potentially optimistic rather than as independent proof of error-free classification. For Batang Kali, all 11 reference landslide points were detected, giving 100% producer's accuracy, but 109 stable reference points were also classified as landslide-like; overall accuracy and user's accuracy were 9.17%. The landslide output is therefore interpreted as a high-sensitivity disturbance-screening product rather than a precise landslide inventory. The results show that GEE provides a transferable computational architecture, while diagnostic performance remains hazard- and indicator-specific.