Humanitarian · Crisis Response Tech
Faraja: Crowdsourced Crisis Damage Assessment
Client
Independent build, inspired by UNDP's RAPIDA methodology
Location
Global
Year
2026
Services
Custom Development
UNDP's RAPIDA methodology delivers early recovery insight from satellite imagery, night-light data, and social media — but ground-level, structured field data still takes days to arrive. Kaistrum built Faraja to close that gap: an open-source, end-to-end platform that lets communities submit geolocated, AI-triaged damage reports over a mobile app or WhatsApp within 72 hours of a sudden-onset disaster, in all six official UN languages, fully offline-capable.
90.2%
AI damage-severity accuracy
Fine-tuned ResNet18 on the MEDIC benchmark (71,198 real disaster images), with 94% recall on severe damage.
500k+
Report capacity per crisis
Architecture scales from a single VPS to a 4+ replica, read-replica deployment for national-scale crises.
6
UN languages
All six official UN languages, including full RTL Arabic support, across both the web app and WhatsApp bot.
100%
Offline capable
Reports and photos queue on-device and sync automatically the moment connectivity returns.
The Challenge
Satellites see the damage. They can't see the ground.
RAPIDA fuses satellite imagery, night-light data, and social signals into a 72-hour early recovery picture — but that picture is built from orbit, and structured, ground-level confirmation remains days away due to logistical constraints. Humanitarian teams needed a way to pull community-sourced, geolocated field reports into that same 72-hour window, at a scale and reliability no existing tool offered.
- 0172-hour assessment window — but field survey tools took days to configure and deploy
- 02Communities span Arabic, Chinese, French, Russian, Spanish, and English — no single tool supported all six
- 03Most-affected areas are also the least connected — low and zero connectivity by default
- 04Existing tools broke well before 50,000 submissions — Faraja needed to hold 500,000+ per crisis, across hundreds of crises a year
- 05Photo evidence arrived faster than any team could manually review it
The Solution
An open-source ground-truth layer for RAPIDA.
Kaistrum built Faraja as a fully open-source, seven-service platform: a mobile-first PWA and an automated WhatsApp bot for zero-barrier community intake; a Redis-backed queue that triages every photo through a purpose-trained AI model; three-tier geospatial deduplication with building-level versioning; and a live coordinator dashboard with responder dispatch — producing structured, RAPIDA-ready data within seconds of submission.
01
Reporters app + WhatsApp bot
A guided, multi-step survey delivered as a mobile PWA carousel, mirrored step-for-step by a WhatsApp bot with native location and photo sharing — no app install required, fully localised into all 6 UN languages with RTL Arabic support.
02
AI damage triage
A multi-task ResNet18 model, fine-tuned on 71,198 real disaster images from the MEDIC benchmark, classifies every submitted photo for disaster type, informativeness, humanitarian category, and damage severity — 90.2% accuracy, 20–30ms per image.
03
Three-tier deduplication & versioning
Cascading dedup — exact client-ID match, photo+coordinate proximity within 60 seconds, and 50-metre spatial matching within a 48-hour window — paired with building-level versioning so coordinators always see the latest report without losing submission history.
04
Burst-resilient queue
A BullMQ + Redis queue with append-only-file persistence absorbs submission bursts and processes reports across concurrent workers, retrying failed jobs automatically — no data lost, even through a service restart.
05
Coordinator dashboard & dispatch
A live, clustered incident map with AI-weighted zone scoring, a conversational assistant for situation summaries, and Haversine-ranked responder dispatch with optimised multi-stop routing to the field.
06
RAPIDA-ready export
PostGIS-backed spatial storage with GIST indexing, exportable on demand as GeoJSON, CSV, Shapefile, or via REST API — structured to feed directly into RAPIDA's geospatial workflows.
Outcomes
AI damage-severity accuracy
Fine-tuned ResNet18 on the MEDIC benchmark (71,198 real disaster images), with 94% recall on severe damage.
Report capacity per crisis
Architecture scales from a single VPS to a 4+ replica, read-replica deployment for national-scale crises.
UN languages
All six official UN languages, including full RTL Arabic support, across both the web app and WhatsApp bot.
Offline capable
Reports and photos queue on-device and sync automatically the moment connectivity returns.
Work with Kaistrum