This repository contains the full analytical workflow and dataset supporting the study “Attributing a deadly landslide disaster in Southeastern Brazil to human-induced climate change”. The analysis focuses on land-use and land-cover (LULC) dynamics in landslide-prone areas of Petrópolis, Rio de Janeiro, Brazil, from 1985 to 2024. The workflow integrates Google Earth Engine (GEE) data processing, Bayesian time-series modeling, and visualization routines to quantify LULC changes and their association with landslide risk. Using MapBiomas Collection 10, a custom landslide inventory, and topographic data (SRTM DEM), the pipeline reclassifies over 60 land-cover categories into 10 consolidated classes, calculates annual class areas, generates transition matrices, and fits hierarchical Bayesian models to detect long-term trends. The outputs include reproducible datasets, statistical summaries, and graphics illustrating forest loss, urban expansion, and their links to landslide concentration in steep terrain. Contents Python script (codigo_petropolis.py) compatible with Colab/Jupyter. Annual LULC area CSVs (1985–2024) for the municipality and mapped landslide points. Aggregated transition tables and Sankey diagrams (1985→2012→2022). Bayesian model outputs: posterior means, HDI intervals, slope probabilities, and performance metrics. Figures used in the article and supplementary material (bar plots, risk charts, HDI trend curves, heatmaps). Key features Reproducible and openly reusable workflow. Harmonized land-cover dataset for a critical hotspot of climate-related disasters in Brazil. Ready-to-use CSVs for further analysis in R, Python, or GIS. Bayesian hierarchical modeling framework to assess long-term LULC trends. Requirements Python ≥ 3.9 Libraries: earthengine-api, geemap, pandas, numpy, matplotlib, plotly, pymc, arviz, scikit-learn Google Earth Engine account with access to specified assets. This dataset and code provide a valuable resource for understanding the role of land-use change in amplifying landslide risk under climate change in Southeastern Brazil.