Sinking city: A multidimensional dataset for urban and land subsidence modelling based on satellite imagery in...

Jakarta, the world's fastest-sinking city, faces complex urban challenges from the complexity of its urban morphology, infrastructure, and environmental conditions. This study presents a descriptor for a multidimensional database of Jakarta, Indonesia, that can be used to analyse the city's subsidence and understand the frequent flooding events throughout the city. The data comprise four different dataset modelled using satellite imagery: (1) land subsidence modelling to quantify subsidence rates in Jakarta based on Sentinel-1 SAR data processed using the open-source package LiCSBAS (2) spectral indices to highlight vegetation and built-up areas, namely the Normalised Difference Vegetation Index (NDVI) and the Normalised Difference Building Index (NDBI) using Landsat 5, Landsat 7, and Landsat 9 imagery, and urban morphology datasets, including (3) impervious surface areas using Sentinel-2 based on adaptation of the Enhanced Normalised Difference Impervious Surface Index (ENDISI) formulation and (4) the proportion of residential areas modelled using K-means clustering. Data processing was performed using Google Earth Engine (GEE) and Python to generate a critical dataset to support analysis and modelling of urban resilience, climate adaptation, and mitigation towards flooding in the context of sinking city. Robust technical validation techniques were performed to ensure data accuracy and validity, with detailed limitations presented. The result presents a series of modelled datasets that can inform further urban analysis and modelling related to flooding events in Jakarta, with a methodology that can be replicated globally, especially in other Global South cities.