WFP-VAM

WFP-VAM's repositories

prism-app

PRISM is an interactive map-based dashboard that simplifies the integration of geospatial data on hazards, along with information on socioeconomic vulnerability

Language:TypeScriptMIT42 8 484

modape

MODIS Assimilation and Processing Engine

Language:PythonMIT29 5 86

RAMResourcesScripts

A public space for contributing to WFP's corporate Needs Assessment and Monitoring analysis of micro-data.

Language:SchemeApache-2.025 12 56

vam.whittaker

State-of-the art whittaker smoother for EO data

Language:PythonMIT16 4 4

wfpthemes

WFP branded theme for ggplot2 and data visualization colour palettes

Language:R3 4 2

DataBridgesAPI

Scripts and applications for querying DataBridges API from the API Gateway

Language:PythonMIT1 6 7

dmp

Disaster management portal

Language:TypeScript1 2 34

Rdatavizgallery

Gallery of key visualizations using the ggplot and wfpthemes package

Language:R1 4 1

Voila-smoothing-inspector

Voila smoothing inspector with NDVI and LST data

Language:Jupyter Notebook1 30

hdc-algo

Algorithmic code used in the HumanitarianDataCube

Language:PythonMIT04 13

Smoothing_Inspector

Language:Python000

AFG_Dashboard

Language:Python090

AWS-CICD-test

Testing deployment using AWS CI/CD

Language:JavaScript000

blended-product-dashboard

MapLibre dashboard for viewing blended product data and running custom functions on a time series

Language:JavaScript000

ClimaX

Foundation model for weather & climate

Language:PythonMIT010

cost_of_inaction_RBD2023

040

DataBridgesAPIr

Language:R000

DataLibraryAPI

Script to query the Data Library API

Language:Python020

economic-explorer-assets

AGPL-3.0000

FamPredAI

Forecasting time series of food consumption levels with Reservoir Computing

Language:PythonMIT000

FbF_streamlit

Language:Jupyter Notebook090

FoodSecRammer

R package

Language:R020

hdc-colors

Color definitions for HDC products

Language:PythonMIT03 6

PatchTST

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

Language:Jupyter NotebookApache-2.0000