"""
Building a Community-Curated, Open-Access Platform of
Global Landslide Datasets to Support Reliable and Scalable AI Models

Recent advances in machine learning (ML) and deep learning (DL) have
significantly advanced landslide-related applications, including detection,
early warning, and susceptibility mapping. Generative AI technologies further
offer new opportunities to accelerate landslide research, particularly by
supporting rapid prototyping and iteration of ML/DL workflows.

However, most existing models are trained on datasets specific to certain
regions or landslide types, resulting in poor or untested generalization
across different geographic and environmental settings. While there have been
growing efforts to publish open-access landslide datasets, these resources
remain fragmented across individual publications, institutional repositories,
and project-specific websites. researchers often spend substantial time locating, retrieving,
and preparing data when building models for new regions. Progress remains
constrained by the lack of high-quality, high-volume, standardized, and
accessible datasets.

To address this gap, we present a community-curated, open-access, evolving
platform that aggregates global landslide inventories and related geospatial
data, and provides detailed metadata for each dataset. Metadata fields include
inventory type (e.g., point, polygon), record count, spatial resolution,
geographic coverage, input features (e.g., optical imagery, elevation, land use),
ML/DL models used, evaluation settings, and whether cross-regional generalization
was tested. Users can search, filter, and download datasets through an interactive,
map-based interface. The platform also encourages community contributions via
an easy-to-use upload interface. It serves as a central hub for high-quality,
globally sourced landslide and geospatial data, supporting the development and
benchmarking of reliable, scalable, and generalizable AI models for both
fundamental research and real-world applications.
"""