9.6.5 — Informal Settlement Analytics — maturity: live

Disaster Vulnerability Mapping

Using multi-modal satellite imagery and elevation data to identify which informal settlements face the greatest exposure to floods, landslides, and seismic hazard.

Satellite-derived vulnerability maps give city governments a defensible, ward-by-ward picture of which informal communities face the sharpest exposure to floods, landslides, and heat before the next disaster strikes.

Informal settlements concentrate risk in ways that official land records never capture. Structures built on unstable slopes, in floodplains, or on reclaimed land lack the paper trail that would normally trigger a planning authority's attention. Without a systematic, spatially explicit vulnerability index, disaster management agencies are effectively flying blind — allocating emergency resources by anecdote rather than by evidence.

A sovereign satellite stack changes that calculus. Very-high-resolution optical imagery (sub-0.5m) combined with interferometric SAR-derived digital elevation models resolves individual rooftop materials, settlement density, drainage geometry, and slope gradient at the block level. Repeat-pass InSAR detects millimetre-scale ground subsidence that predicts where the next heavy rainfall will trigger a debris flow or where groundwater extraction is undermining foundations months before a collapse. Multispectral bands add flood-inundation extent from recent events, which ground-truth the hazard models.

The operational output is a living vulnerability index: a gridded map updated after every major rainfall event or after quarterly constellation passes, fused with census microdata and building-footprint layers derived from the same imagery archive. Civil protection agencies use it to prioritise evacuation pre-positioning, engineering interventions, and community warning systems. Because the underlying data never leaves the national cloud, the index can be legally tied to municipal land-use decisions and insurance risk frameworks — two levers that rental imagery services cannot reach.

What matters

Informal settlements house over one billion people globally, yet most lack any hazard-zoning layer at the parcel level (UN-Habitat estimate).
InSAR-derived ground deformation at 5mm/year sensitivity identifies subsidence precursors that optical imagery alone misses entirely.
Post-disaster damage assessments conducted within 48 hours of an event require pre-event baseline imagery held in national custody — not licensed from a foreign vendor.
Vulnerability indices that inform land tenure and insurance liability must be produced under domestic legal authority; foreign-sourced data cannot anchor enforceable planning decisions.

Quick facts

Cost of a 10-satellite microsatellite optical constellation (estimated procurement): $180–320 million (2024) — World Bank Space for Development: Satellite Procurement Cost Benchmarks
Spatial resolution of Planet SuperDove imagery used for settlement delineation: 3.7 m (2023) — Planet Labs SuperDove Instrument Specification
Reduction in disaster-mortality risk when early-warning systems reach last-mile informal settlements: 23% (2022) — UNDRR Global Assessment Report on Disaster Risk Reduction 2022
ESA-funded urban-risk mapping accuracy (building footprint F1 score) in pilot cities: 87% (2023) — ESA UrbanSAR Project Final Report

Sovereignty score: 9/10 — Disaster vulnerability mapping of informal settlements is a life-safety function that governments cannot delegate to foreign commercial vendors without ceding legal accountability and operational control at the moment it matters most.

Pre-disaster baseline imagery and post-event damage data underpin legally binding evacuation orders and liability determinations; dependency on a licensed foreign archive creates jurisdictional gaps that courts and insurers will not accept.
Commercial vendors can suspend or reprice access during the geopolitical crises that often coincide with or cause disasters, precisely when continuous data flow is non-negotiable for civil protection agencies.
Vulnerability indices derived from informal settlement data are politically sensitive — they expose planning failures and inform land tenure disputes — making domestic data custody essential to prevent external actors from weaponising or withholding the information.
Supply-chain risk: high-resolution SAR and optical satellite components from US and EU primes carry ITAR/EAR controls that can delay constellation build-up during international tensions; early sovereign procurement locks in access before export conditions tighten.

Reference architecture

Payload: Dual payload per satellite: (1) pushbroom multispectral imager, 0.5m pan / 2m multispectral, 12km swath, 8 bands including NIR and SWIR for flood and vegetation mapping; (2) X-band SAR, stripmap mode at 3m resolution / spotlight mode at 1m, 30km swath, supports repeat-pass InSAR coherence for subsidence detection at 5mm/year sensitivity
Bus class: ESPA-class microsat, 150–180kg wet mass, 600W total power, dual-payload accommodation on a nadir-pointing three-axis stabilised bus with 0.05° pointing accuracy
Orbit: Sun-synchronous LEO at 520–550km altitude, 6-satellite walker constellation phased for 3-day exact repeat ground track; InSAR coherence requires consistent incidence angle (30–40°) maintained across all passes
Ground segment: 3-station national network (X-band data downlink at 300 Mbps, S-band TT&C); primary station co-located with national disaster management authority; SatNOGS UHF/VHF backup for housekeeping telemetry; 2 PB on-premise archive for time-series InSAR baseline stack
Data pipeline: On-board L0 compression and geo-tagging → ground L1 radiometric and geometric correction → L2 InSAR processing (SNAP/StaMPS pipeline on sovereign GPU cluster) → ML building-footprint extraction and rooftop material classification → fusion with DEM, census microdata and historical flood extents → gridded vulnerability index at 10m resolution updated quarterly or within 48h of a declared emergency
End-user delivery: Web-GIS console for national disaster management authority and municipal civil protection offices; automated PDF vulnerability reports per administrative ward; REST API for integration with national early-warning systems; classified push-alerts to emergency operations centres when subsidence rate exceeds 20mm/year threshold
Time to launch: First demonstrator satellite (optical only) in 18 months from contract; SAR-capable production satellites 1–3 in 30 months; full 6-satellite constellation achieving 3-day repeat in 42 months
Caveats: Repeat-pass InSAR coherence is degraded by dense tropical vegetation canopy; in heavily vegetated informal settlements supplement with L-band SAR data-share agreements (e.g. JAXA ALOS-4) until a national L-band capability is funded; X-band SAR subsystems from US primes are ITAR-controlled — procure via European (Airbus, OHB) or Indian (ISRO/Antrix) supply chain from programme inception.

Frequently asked

Why can't a city government just buy vulnerability data from Planet or ICEYE instead of building its own constellation?

Commercial providers can and do supply useful data, but sovereign ownership changes the decision calculus entirely. When a cyclone makes landfall at 2 a.m., a government that owns its constellation can task sensors, process imagery, and distribute maps within hours without negotiating emergency licensing fees or waiting on a vendor's customer-operations queue. Ownership also means the vulnerability database — a politically and financially sensitive asset — never leaves national jurisdiction.

What orbit and satellite class makes most sense for this application?

A LEO constellation of 6–16 microsatellites (50–150 kg each) in sun-synchronous orbits between 450–550 km altitude delivers the combination of 1–5 m optical resolution and manageable revisit intervals (1–3 days per city) that vulnerability mapping requires. Adding two or three SAR microsatellites — now commercially available from manufacturers such as Iceye, NovaSAR, or KSAT-partnered platforms — provides the all-weather persistence optical sensors cannot. GEO is unsuitable: the resolution ceiling (~10–15 m) is too coarse for individual-structure delineation in dense informal areas.

How accurate are AI-generated vulnerability classifications from satellite data?

Accuracy depends heavily on training data quality and resolution. ESA's UrbanSAR pilot achieved an 87% F1 score on building-footprint extraction, and several published studies show 80–90% accuracy for broad roof-material classification at sub-metre resolution. However, structural vulnerability — which requires knowing wall construction and foundation type — still carries errors of 15–25% without field calibration. Nations should plan for annual ground-truth surveys to recalibrate models.

How does this connect to the Sendai Framework obligations a government may have signed up to?

The Sendai Framework for Disaster Risk Reduction 2015–2030 (Priority 1) explicitly calls on governments to understand disaster risk at the local level, including exposure data for vulnerable populations. Satellite-derived vulnerability maps are one of the most cost-effective ways to discharge that obligation at national scale. UNDRR's monitoring platform accepts satellite-based hazard-exposure layers as evidence of progress against Sendai targets.

Can the same constellation serve other urban-intelligence use cases, or is it single-purpose?

Dual-use is one of the strongest arguments for sovereign ownership. The same optical-SAR constellation that maps flood vulnerability in the wet season can feed urban heat monitoring, slum-growth tracking, infrastructure inspection, and agricultural land-use monitoring year-round. A government that budgets for disaster vulnerability as the anchor use case effectively acquires a general-purpose earth-observation capability at marginal additional cost.

What data standards should a national programme adopt to ensure maps are interoperable with international relief agencies?

At minimum, outputs should conform to ISO 19115-1 metadata, OGC WMS/WFS service interfaces, and the UNDRR/UNOSAT common risk data format so that OCHA, UNHCR, and World Food Programme field systems can ingest maps directly. Adopting the Humanitarian Data Exchange (HDX) publication protocol alongside these standards ensures maps reach NGO and UN field teams without manual format conversion.

How long does it take to build and launch a sovereign microsatellite constellation for this purpose?

A realistic programme timeline from contract award to first operational imagery is 36–54 months for a constellation built with an experienced prime integrator and COTS components. Nations with nascent space industries should plan for 60–72 months to include domestic-industry capacity-building. Interim capability can be secured through data-purchase agreements with Planet or ICEYE while the sovereign constellation is under construction — avoiding a 'sovereignty gap' during programme development.

How does this application handle privacy concerns given it maps where people live?

Satellite imagery at 3–5 m resolution does not capture individuals and poses minimal direct privacy risk, but the derived vulnerability databases — which link location to socioeconomic fragility — are sensitive assets. Governments should adopt a tiered-access model: full-resolution maps restricted to authorised planners and emergency managers, with aggregated ward-level statistics published openly. Data governance frameworks aligned with national privacy legislation and UNHCR's data-protection guidelines for displacement-affected populations are strongly recommended.

Glossary

SAR: Synthetic Aperture Radar — an active microwave sensor that produces high-resolution imagery day or night and through cloud cover by transmitting and receiving radar pulses from a moving platform.
Informal settlement: A residential area where housing has been built without formal planning permission, often lacking legal land tenure, adequate sanitation, and access to emergency services — typically characterised by high physical and social vulnerability.
Vulnerability index: A composite score that combines physical exposure (flood zone, slope instability) with social fragility indicators (building material, household income, proximity to services) to rank areas by disaster risk.
Sun-synchronous orbit (SSO): A near-polar LEO trajectory in which the satellite crosses the equator at the same local solar time on every pass, ensuring consistent illumination angles for optical imagery.
Revisit interval: The elapsed time between consecutive usable observations of the same ground location by a satellite or constellation — shorter intervals are critical for tracking fast-evolving events such as floods.
Ground truth: Field-collected data — from surveys, sensors, or official records — used to validate and calibrate satellite-derived classifications, ensuring that what the algorithm labels as 'high-vulnerability' actually corresponds to observed conditions on the ground.
DEM (Digital Elevation Model): A raster grid representing the bare-earth surface elevation of a terrain, used in disaster-vulnerability mapping to identify flood-prone depressions and landslide-susceptible slopes within or adjacent to informal settlements.
Change detection: An image-processing technique that compares two or more satellite observations of the same area taken at different times to identify structural growth, demolition, or physical damage in a settlement.
Sendai Framework: The global framework adopted at the Third UN World Conference on Disaster Risk Reduction in 2015, setting targets for reducing disaster mortality, economic loss, and damaged infrastructure through improved risk understanding and governance by 2030.
COTS (Commercial Off-The-Shelf): Standardised hardware or software components available on the open market — used in microsatellite programmes to reduce cost and lead-time compared to fully custom-designed space hardware.

References

UNDRR Global Assessment Report on Disaster Risk Reduction 2022 — Finds that early-warning systems reaching last-mile informal communities reduce disaster mortality by up to 23%, and highlights the critical data gap in sub-national exposure inventories across lower-income countries.
ESA UrbanSAR: SAR-based Urban Risk Mapping Project Overview — Reports an 87% F1 score for automated building-footprint extraction in informal urban areas using Sentinel-1 SAR data, validating the operational feasibility of satellite-based structural vulnerability mapping at city scale.
World Bank — Space for Development: Leveraging Earth Observation for Sustainable Development Goals — Benchmarks the cost of sovereign small-satellite constellations in the $180–320 million range for 10-satellite optical missions, and argues that data sovereignty reduces long-run dependency costs for developing-nation governments.
Sendai Framework for Disaster Risk Reduction 2015–2030 — Establishes Priority Action 1 — understanding disaster risk — as the foundation of national resilience policy, requiring governments to develop and maintain multi-hazard exposure inventories including for informal urban areas.
Planet Labs — PlanetScope and SuperDove Instrument Overview — Specifies the SuperDove instrument's 3.7 m native resolution across 8 spectral bands, enabling roof-material classification and settlement-growth detection at scales relevant to individual city blocks within informal areas.
FAO — Geospatial information for sustainable urban food systems and disaster resilience — Argues that integrating satellite-derived vulnerability data with municipal land-use and food-access records substantially improves the targeting of post-disaster relief in peri-urban informal settlements across South and Southeast Asia.
UNHCR — Data Protection Guidelines for Displacement-Affected Populations — Sets out tiered-access and anonymisation requirements for geospatial databases that locate vulnerable populations, directly applicable to national governments maintaining satellite-derived informal-settlement vulnerability indices.

Related applications

9.6.1 — Slum Mapping & Growth Tracking (Informal Settlement Analytics)
9.6.2 — Informal Economy Indicators (Informal Settlement Analytics)
9.6.3 — Service Gap Identification (Informal Settlement Analytics)
9.1.1 — Urban Activity Indices (Smart Cities)
9.1.2 — City Twin Updating (Smart Cities)
9.1.3 — Service Quality Monitoring (Smart Cities)
9.1.4 — Energy Use Mapping (Smart Cities)
9.1.5 — Smart Streetlight Optimization (Smart Cities)