A Collection of Flood Hazard Layers for New York City.
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Updated
Nov 21, 2023 - Jupyter Notebook
A Collection of Flood Hazard Layers for New York City.
Turn Japan's PLATEAU 3D city models into a queryable, hazard-aware buildings.parquet. Pre-built bundles for 29 cities; SQL via DuckDB; 3D Tiles + PMTiles + FlatGeobuf out of the box.
A presentation on leveraging Jupyter Notebooks for automating large scale flood risk studies
Flood risk prediction using machine learning and SHAP explainability
Mangrove Flood Risk Assessment 🌳🌊
NASA ACRES fellowship project mapping wildfire and flood risk in Kula and South Maui using satellite remote sensing.
Exploring the Building Elevation and Subgrade (BES) Dataset for New York City in Python
Malaysia-focused flood risk prediction and explainability research project using FastAPI, Streamlit, geospatial data, and machine learning.
Ready-to-use MCP extensions for Gemini CLI and Claude Desktop - neighborhood intelligence, school ratings, flood risk, air quality, and more.
Municipality-Scale Flood Risk Mapping across Colombia — 7 departments (Antioquia, Bolívar, Cauca, Chocó, Guajira, Magdalena, Nariño) — Sentinel-1 SAR + RF/XGBoost/LightGBM ensemble + JRC water + WorldPop — GEE (2015–2025)
End-to-end spatial ML pipeline predicting flood vulnerability at the census tract level using FEMA flood zones, OSM waterways, and Census demographics — Harris County, TX.
Flood susceptibility mapping workflow using geospatial data engineering and a decision making algorithm to identify and visualize flood-prone areas.
Flood-retention investment MVP that ranks where extra capacity can reduce downstream risk first.
Flood score integration for parcel-level flood risk assessment.
Experiments with LiDAR, IMU and machine learning for water level measurement.
Free tool to download EA surface water flood risk data as shapefiles for any UK postcode input
Real-time flood risk intelligence dashboard with live Open-Meteo data and responsive ML predictions.
[PJDSC 2025] Explainable, street-level flood-risk mapping for Philippine cities.
Citation-grounded NYC flood-exposure briefings: multi-agent civic AI on Granite 4.1 + Prithvi-EO + TerraMind, every claim cited to a public-record source.
GIS analysis of flood risk impact on road infrastructure in Lagos using OpenStreetMap and spatial modelling.
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