Ticket Contents

Description

Landslide susceptibility mapping at field level (~100m resolution) helps identify areas prone to slope failure, enabling disaster risk management and land-use planning. Using GEE, susceptibility can be computed based on established methodologies and datasets, vectorized, and later refined for finer spatial scales.

Goals

Goals

• Implement landslide susceptibility methodology from existing https://www.sciencedirect.com/science/article/pii/S0341816223007440 using GEE.
• Generate raster outputs at ~100m resolution representing landslide susceptibility.
• Explore methods to compute susceptibility at finer spatial scales (<100m) for high-resolution mapping.
• Vectorize raster outputs into polygons for field-level analysis.
• Publish raster and vector outputs as Earth Engine assets with metadata.
• Enable visualization and spatial analysis of landslide-prone areas.

Expected Outcome

Expected Output

• Raster dataset (~100m resolution) showing landslide susceptibility.
• Vectorized polygons with attributes:
  • Susceptibility class (low, moderate, high)
  • Area (ha)
  • Relevant metrics (slope, curvature, land cover)
• Published Earth Engine assets (raster + vector) with metadata.
• GEE visualization highlighting landslide-prone zones.
• Validation report confirming coverage, accuracy, and classification.

Acceptance Criteria

Acceptance Criteria

Data Acquisition

• Input datasets (DEM, slope, curvature, LULC, rainfall, soil) preprocessed and clipped to AoI/MWS boundaries.
• Resolution standardized to ~100m.
• Derived topographic indices (slope, curvature, flow accumulation) computed.

Raster Computation

• Raster outputs computed using established landslide susceptibility methodology.
• Entire AoI/MWS covered without gaps.
• Classification thresholds documented (low, moderate, high susceptibility).

Vectorization

• Raster outputs converted to field-level polygons using reduceToVectors() in GEE.
• Each polygon includes:
  • Susceptibility class
  • Area (ha)
  • Relevant metrics
• Polygons aligned with AoI/MWS boundaries.

Asset Publishing

• Raster and vector datasets published as Earth Engine assets.
• Metadata includes source datasets, resolution, processing date, and methodology.

Quality & Validation

• Coverage check: all study areas included.
• Accuracy check: susceptibility classes validated against known landslide locations or historical records.
• Attribute check: all polygons include class, area, and metrics.
• GEE visualization confirms correct spatial distribution.

Implementation Details

Implementation Details

Data Sources

• DEM (e.g., SRTM 30m)
• LULC datasets
• Rainfall and soil data
• Historical landslide inventory
• AoI/MWS boundaries

Processing

• Compute topographic and hydrological indices (slope, curvature, flow accumulation).
• Apply weighted susceptibility model from literature.
• Generate raster outputs at 100m resolution.
• Explore methods for higher-resolution susceptibility computation (<100m).

Vectorization & Publishing

• Convert raster outputs to polygons using reduceToVectors().
• Include attributes: class, area, slope, curvature, land cover.
• Upload raster and vector layers as EE assets with metadata.

Visualization

• Color-coded raster and vector layers in GEE (low = green, moderate = yellow, high = red).
• Overlay with AoI/MWS boundaries for field-level inspection.

Validation

• Compare outputs with historical landslide events.
• Spot-check vector polygons for correct classification.
• Generate validation report documenting coverage, accuracy, and completeness.

Mockups/Wireframes

No response

Product Name

KYL

Organisation Name

C4GT

Domain

No response

Tech Skills Needed

Python

Organizational Mentor

@amanodt @ankit-work7 @kapildadheech

Angel Mentor

No response

Complexity

Medium

Category

Backend