August 2021 Remote Sensing Innovation: Progressing .

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August 2021Remote sensing innovation: progressingsustainability goals and expanding insurability01 Executive summary02 Key takeaways04 Innovation inremote sensing11 Demand driversin insurance17 Enterprise-scaledeployment ininsurance21 Conclusion

Executive summaryRecent innovations have supported thecommercial adoption of remote sensing.Applications of geospatial and earth observation sensor data from satellites, aircraftand drones are expanding rapidly. New business models are emerging as a broadrange of solutions and services enabled by such data are unlocked across industries.The advent of start-ups taking advantage of better technology and substantialreductions in the cost of launching satellites will make such services even moreaccessible. This will democratise granular geo-physical insights beyond militaryand government to benefit also commercial enterprises, including insurers.Remote sensing can play a wider rolebeyond industrial application: it can helpbuild sustainable society.Further, intergovernmental efforts towards building sustainable societies are gainingtraction and remote sensing can play a key role in providing better data to measuresustainability indicators. High resolution (spatial and temporal) earth observation(EO) data can quickly and frequently reveal on-ground changes relevant for the UNʼsSustainable Development Goals (SDGs), and help in optimising allocation ofresources to accelerate progress. We estimate that at least 17% of the UNʼs 231SDG indicators can immediately benefit either directly or indirectly from the use ofEO data, and expect this to increase over time.Rapid damage assessment andparametric product development areearly use cases for insurance.In insurance specifically, the trends can have far reaching implications for insurersand insureds. Remote sensing will enable new markets and risk pools, and easeexisting processes such as claims assessment and, in time, underwriting and riskmonitoring. Insurers will get better at leveraging high-frequency data from sourceslike synthetic aperture radar (SAR), combined with other sources (both ground andair). We will likely see multiple use cases with varying degrees of success acrossbusiness lines. While challenges such as establishing accurate correlations betweensatellite data and actual losses have hindered widespread adoption, insurers willincreasingly address these challenges by experimenting with hybrid modellingapproaches, accessing better data and advanced data integration techniques.Vendors and services in the remotesensing arena offer a wide variety of dataand technologies.Advancing machine-learning techniques and integrating multi-sensory, multi-sourceand multi-temporal data is key to plug data gaps and enable granular risk and lossinsights for insurers. However, the diversity of data sources has raised severalquestions regarding cost, coverage, predictive power, regulation and challenges ofintegrating them across different risk pools and geographies. As a result, commercialdata and satellite vendors have started to offer customised offerings to insurers.These are vertically-integrated packaged solutions, starting from launching a satelliteto capturing the data and delivering the final analysis.Data from multiple providers via openApplication Programming Interfaces(API) may be combined to enabledifferent business models.In the future, insurers will operate in an environment where they will need continuousaccess to many different data sources, including from remote sensing. This is astrategic issue, taking insurers beyond their existing value chain. No single firm ormarketplace currently provides all these sources of data. While many data vendorsfocus on extraction and distribution of data, few concentrate on data curation andrefinement. We expect that this will give rise to specialised aggregators focused onintegration and curation. The more integrated and refined the data, the wider theservice offering to a customer. This emerging data ecosystem has many implicationsfor the insurance industry, in particular the need for modular products, and betterdistribution.Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20211

Key takeawaysRemote-sensing-enabled earth observation (EO) can support progress towards the UN Sustainable DevelopmentGoals (SDGs): EO data for vegetation cover, habitation patterns and hydrometeorology can be merged with ground surveys andother Big Data to create synthetic maps providing granular insights on progress towards different SDG indicators. We estimatethat progress towards at least 17% i.e. 39 out of 231 SDG indicators could benefit by direct or indirect use of EO data.AgricultureProductdevelopmentCrop parametricproduct fordrought risksbased on soilmoisture index.Passive(microwave)images tomeasure soilmoisture levels.Morecustomisationrequired foradjusting theindex to crop typeand sowing stage.Automatedunderwriting andpayout. Morecomprehensivethan NormalizedDifferenceVegetation Index(NDVI) and rainfallindex.PropertyProductdevelopmentProperty (Flood)parametricproduct based onexcess rainfallindex.Passive remotesensing imagescombined withdata fromground-basedweather stations.High basis risk.Not suitable forsingle location riskand retailcustomers.Affordableproduct withautomatedunderwriting andpayout. Longertime series ofweather data.Collection of UN SDG indicators with earth-observation relevance313676119Goal 1:No poverty253411Goal 2:ZerohungerGoal 3:Goodhealth andwellbeingOther SDG indicatorsGoal 4:Qualityeducation1155Goal 6:Cleanwater andsanitationGoal 7:Affordableand cleanenergy498Goal 9:Goal 11:Industry,SustainableInnovation,cities andandcommunitiesInfrastructure767Goal 13:ClimateactionGoal 14:Life belowwaterGoal 15:Life on landNote: Passive sensors observe objects illuminated by sunlight or self-illuminated objects. Active sensors emit energy (radio, sound & light waves) to illuminateobjects and they can operate in night and all-weather conditions.Source: Swiss Re InstituteSDG indicators with EO relevanceSource: United Nations, European Space Agency, Swiss Re InstituteRemote sensing can expand the bounds of insurability to new risk pools and make insurance processes more efficientacross different lines of business (LoB). Examples of insurance processes that can benefit are granular indices for parametricproducts, rapid claims assessment and early warning alerts. By expanding the scope of insurability, remote sensing will makehouseholds and business more resilient.Link to the UN SDGs2Line of businessValue chainUse caseAgricultureClaimsassessmentCrop yieldestimation toassess and rtyRemote sensingtechnologyChallengesBenefitsSmart samplingenabled by bothpassive and active(SAR) remotesensing.Establishingcorrelationbetween remotesensing vshistorical yielddata.Less manpowerrequired toestimate yields.Reliable samplingand cost saving.Rapid damageassessment afterlarge scale floodevents.Flood mapsenabled by bothpassive and active(SAR) remotesensing.Measuring peakflood height andtime for whichwater stands still.Faster decisionson claimadmissibility andsettlement. Betterreserving andlower moralhazard.Underwriting andclaimsassessmentDetecting severityof roof andbuilding structuredamage.Passive (aerial)imagery analysedwith semanticsegmentation.Aerial imagerycan be costly anddifficult to acquireat short notice.Fasterunderwriting.Better reservingand lower moralhazard.ClaimsassessmentSubsidence lossassessment andprediction forsinking structures.Active (DifferentialInterferometricSyntheticAperture Radar orD-InSAR) imagesanalysed overlong time.Hard to achievehigher temporalresolution acrossdifferentgeneration ofsatellites.Faster decision onclaim admissibilityand settlement.Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20213

Innovation trends in remote sensingSupply side and economic factors driving adoptionInnovation and commercialisation ismaking remote sensing more accessible.Remote sensing, which includes both space and earth observation (EO), is thescience of collecting and interpreting visual intelligence about objects from afarusing sensors mounted on satellites, aircraft and drones. Military and governmentshave been the main users of remote sensing but with the technology becoming moreaffordable, supply-side developments are democratising EO across the private sectoralso. Innovation and commercialisation have accelerated launches of remote sensingsatellites in recent years and expanded the range of applications in earthobservation, which is our focus in this report.Figure 1Remote sensing market dynamicsSupplySmaller satellitesMarketCheaper launchingMilitary andGovernmentFigure 3Active satellites by launch year and size(LHS); active smallsats by area ofapplication (in 2020 (RHS))DemandMilitary intelligence supported by modular assembly andimprovements in solar power technology.Disaster resilienceThe availability of commercial off-the-shelf components is lowering ground-updevelopment costs and standardising satellite build. This is in contrast to thetraditional approach of making customised mission-specific satellites. As a result,the number of smaller in-orbit satellites (weighing less than 100 kg) has increasednotably since 2017 (see Figure 3, (LHS)). Most smaller satellites rely on solar power,and recent developments in solar panel cell technology have enabled smallersatellites to operate with optimal power supply in space.1 This has partly addressedthe issue of revisit time, with commercial vendors able to launch a greater numberof smaller and cheaper satellites in a single mission. However, more satellites in theorbit increase the space waste and the risk of satellites colliding with each other.4503509%10%25027%15054%50Venture fundingApplicationsComplementary dataEnablersAsset monitoringBetter prises(incl. intergovernmental entities)2016–2020 100kg (commercial or civil) 100kg (commercial or civil)CommunicationsRemote sensing 100kg (Govt. or military)Technology development 100kg (Govt. or military)OthersSource: Swiss Re Institute, Union of Concerned ScientistsSource: Swiss Re InstituteSatellite launch costs have fallen with theavailability of private-launch services.The number of commercial remotesensing satellites in service has grownrapidly.The sector is at an inflection point, given advances in satellite (miniaturisation)design and availability of cheaper commercial launching services. Componentmakers now offer packaged manufacturing and engineering services, which haveenabled modular component design and quicker assembly of smaller satellites.The number of active commercial remote sensing satellites has outpaced growthin government satellites since 2015 due to increased affordability and equityinvestment. This has enabled application of remote-sensing technology to areaswell beyond traditional end-customers and industries (see Figure 2).Figure 2Growth in number of remote sensingsatellites by end user (LHS); growth inequity investment, USD bn (RHS)140.01.6122.51.4105. 00070.00.820 Equity investment USD bn (RHS)Number of funding rounds (LHS)Private (commercial & civil) (LHS)Govt & military (exclusive) (LHS)Launch costs for low-earth orbit (LEO) satellites have fallen substantially (see Figure4). Sector structural changes started in 2011, when NASA ended its space shuttleprogram to focus on earth science and deep-space exploration. This resulted inNASA awarding more contracts with research funding to private operators for thedevelopment of low-cost launch service capabilities, mostly for LEO deployment.Commercial launchers saved initial R&D costs by using readily-available rocketmissile design. They also took on in-house end-to-end development of launchvehicles, which has proved more efficient than previous sub-contractingarrangements.2Figure 4Payload launch costs per kilogram for LEO at constant US dollars of 2018000Equity investment US 50bn (RHS)40 00010 0000Pegasus XL Atlas IIA(1990)(1991)H-2Start(1993) (1994)AmericasAPACSource:Compiled byNASA andEuropeSwiss Re InstituteRockot(1994)12Athena 1(1995)Athena 2 Ariane 5G Delta III(1998)(1996)(1995)Dnepr(1999)Zenit 3SL Falcon 9(2010)(1999)Falcon StarshipHeavy (planned)(2021)(2018)Satellite makers have now started including multi-junction solar panel cells in smaller satellites. This hasmultiple layers of light absorbing semi-conductors, which can use wider spectrum of solar radiation tomake power. See section on power in State-of-the-Art Small Spacecraft Technology, NASA, 2020.The Recent Large Reduction in Space Launch Cost, NASA, 2018.Source: Union of Concerned Scientists, Space Capital, Swiss Re InstituteNote: LHS denotes left hand side index, vice versa for RHS4Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20215

Innovation trends in remote sensingAnnual space tech investments haveincreased 13 times since 2012.Greater accessibility to space has expanded the customer base for remote sensingand attracted more equity investment. Even with the effects of COVID-19 pandemic,in 2020 there was 28% year-on-year growth in funding to geospatial companies,taking total investment to around USD 28 billion; the US and China lead. Almost 70%of the funding in spacetech comes from venture capital firms.3 There is growinginterest from the public sector too, especially in launch services. More than half ofcumulative equity investment since 2012 is classified as late-stage funding,underpinning market readiness for space tech solutions (see Figure 5).Figure 5Cumulative spacetech equityinvestments by stage and geography(2012‒2020)Cumulative equity investment by stageCumulative equity investment by geographyMeasuring and advancing sustainability via remote sensing2%6%7%26%Over time, innovations will lead to a confluence of geospatial offerings deliveredthrough new data-driven business models. Such solutions will be generic productsfit for application across industries, later customised by adding business-specificdata and context, including in insurance. Remote sensing vendors can layer nearreal-time geophysical insights with behavioural, situational and contextual patternsto enable autonomous visual intelligence. The advance of such capabilities will alsoprogress global efforts to ensure a sustainable world for future generations.Governments and international agencies can also use the insights to monitoreconomic and planetary sustainability.Secure access to sensor and othercomplimentary data can foster newbusiness models.3%3%4%SeedSeries ASeries BSeries CLate billion5%Singapore47%There is growing recognition of remote-sensing-enabled EO as a complementarydata source to progress the UNʼs Sustainable Development Goals (SDG).4 The UNmonitors progress for development goals by collecting socio-economic andenvironmental data under 17 SDGs broken down to 169 targets and 231 indicators.UN member states report most of this data in national statistics, but it often comeswith a time lag or is patchy (eg, data on mountain biodiversity). Remote sensing canaddress many of these shortcomings (see Figure 7).Remote sensing can play a key role inbuilding sustainable society.UKIndonesiaIndiaOthers28%Source: Space Capital data as on 1Q2021Today, much-increased spatial and temporal resolutions are leading to additional andsuperior geospatial data collection methods. In the 1990s, a single pixel of a typicalimage represented about 20 square metres of land area. By 2000, a pixelrepresented around 10 meters. Today a resolution of 0.3 meters is possible fromDigital Globeʼs WorldView-3 (see Figure 6). With improvements in spatial resolution,the focus is now on enhancing temporal resolution. Companies like ICEYE andCapella Space now work on synthetic aperture radar (SAR) small satellites to reducerevisit time and capture scenes more frequently. Improvements in resolution andground equipment will enable intra-day insights.Advances in spatial and temporalresolution are yielding superior datacollection ndsat 1Landsat 2Landsat 3Landsat 4Landsat 5SPOT 1SPOT 2SPOT 3IRS-1DSPOT 4IKONOSEROS-AQuickbirdSPOT 5Resourcesat-1 ldView-1CARTOSAT-2AWorldView-2AISat-2ASPOT 6WorldView-3WorldView-4ALOS-3Black Sky ConstellationICEYE constellationCapella Space 07070809101214162018211821182169Goal 1:No poverty325Other SDG indicators36611Goal 2:Zerohunger111Goal 3:Goodhealth andwellbeing1Goal 4:Qualityeducation55Goal 6:Cleanwater andsanitationGoal 7:Affordableand cleanenergy7198Goal 9:Goal 11:Industry,SustainableInnovation,cities andandcommunitiesInfrastructure4767Goal 13:ClimateactionGoal 14:Life belowwaterGoal 15:Life on landSDG indicators with EO relevanceSource: United Nations, European Space Agency, Swiss Re InstituteRemote sensing combined with in-situdata sources and national statistics canenable better tracking of SDGs.High-resolution EO data can quickly and frequently reveal on-ground changes andsupport optimum allocation of resources. EO data for night light, vegetation cover,habitation patterns and hydrometeorology can be merged with ground surveys andother Big Data to create synthetic maps providing granular insights on differentindicators. Swiss Re Institute analysis of UN SDGs and data from the EuropeanSpace Agency suggests that at least 17% of the 231 SDG indicators can immediatelybenefit either directly or indirectly by using EO data. Many of these are also relevantfor insurability and areas of risk modelling (see Table 1).Temporal resolution in days (highest) – right axisNote: Spatial resolution depicted in the chart is highest across multiple sensors in a single satellite; temporal resolution is highest from either a single or aconstellation of satellites. This chart is constructed from publicly available information and current information may differ from this chart due to rapid developmentsin satellite resolutions.Source: University of Twente, European Space Agency, Swiss Re Institute334Figure 6Improvement in spatial and temporal resolutions over time (1972‒2021)Spatial resolution in meters (highest) – left axisFigure 7Collection of UN SDG indicators with earth observation relevance4SDGs promote economic and human development with the protection of environment. SeeTransforming our World: The 2030 Agenda for Sustainable Development, United Nations, 2015Start-Up Space, Bryce Tech, 2020Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20217

Innovation trends in remote sensingFigure 8Table 1Examples of SDGs, with relevance of earth observation and insurance elementsUN SustainableDevelopment Goals(SDGs)Goal 1:No povertyRelevantinsurance lineof businessSDG Indicators1.1.1 Proportion of populationliving below poverty line bygeographic location (urban/rural).Multiline1.5.2 Direct economic lossattributed to disasters in relationto global GDP.Property& CasualtyGoal 2:Zero hunger2.4.1 Proportion of area underproductive and sustainableagriculture.Property& CasualtyGoal 3:Good healthand well-being3.3.3 Malaria incidence per1 000.Life & Health3.9.1 Mortality rate attributed tohousehold and ambient airpollution.Life & HealthGoal 9: Industry,Innovation,Infrastructure9.4.1 CO2 emission per unit ofvalue added.MultilineGoal 11:Sustainable cities11.5.2 Economic loss, damage tocritical infrastructure, disruptionsto basic services attributed todisasters.Property& CasualtyGoal 13:Climate action13.1.1 Number of deaths,missing and directly affectedattributed to disasters per100 000 population.Life & HealthGoal 15:Life on land15.1.1 Forest area as a proportionof total land area.Multiline15.4.2 Mountain Green CoverIndex.MultilineRemote sensing applications for sustainable insuranceRelevant remote sensing elements for insurance and SDG indicatorsMaturity of EOtechnologyAvailability oftechnical capacityAvailability ofglobal EO dataSpatialscalabilityMeasuringoil reserves bymonitoring oilroof tanksStudyingurbanisationObservingCrop parametric sea levelsproduct throughsoil moistureindexMediumspread of diseasesUnderwriting & ProductsReducing trafficNavigatingjams with changeships safelythrough the most detectionoptimal routeAssessingroad neLife & HealthLowRemote sensing can enhance riskmodelling and overall insurancesector functions.Integration of remote sensing data in their value chain can help insurers better priceand monitor their own portfolio of insurance risks, and influence the design, usageand maintenance of clientsʼ assets and facilities. In addition, remote sensing canenable prevention and mitigation measures eg, early warning signals from SARsatellites for wildfire or landslide. Commercial data and satellite vendors have startedoffering customised data offerings to insurers. Such unique and large remote sensingdata enriched with insights from asset behaviour, connected objects and historicallosses can enable superior and more accurate risk modelling. The data can be usedfor pricing and real-time adjustment of coverage based on changing risk profiles.Insurers could also leverage the data insights to detect fraud and offer enhanced riskintelligence, prediction and prevention services.Commercial risks can benefit fromremote-sensing enabled insights.Insurers have already started using third-party data to auto-fill proposal forms anddevelop risk scores for SMEs and mid-corporate risks.6 There is still some way to goin digitising information critical for underwriting large corporate risks. This segmentwill benefit most if visual intelligence from remote sensing can be fused with livedata from industrial control and monitoring systems. The data could be built intoplatforms whereby clients are able to track inherent operations risks and predictfrequency and severity of losses by asset (building, machinery, stock) and location(see Figure 9). Integration of real-time data on commodity prices, interest rates,Remote sensing and insurance8Detectingseverity of roofand buildingdamageDetection offraudulent cropinsurance claimsSource: Swiss Re InstituteThe technical capacity to meaningfully apply EO data to measure progress on SDGsvaries significantly by country. Lack of processing infrastructure, talent and poorinter-operability across disparate data frameworks remain the biggest hurdles forintegrating EO with national statistics. Institutional structures within and beyondthe UN seek to address these challenges to improve regional and national decisionsupport systems. Insurers must take their seat at the table on such discussions.5The insurance industry is becoming increasingly aware of the potential of geospatialdata in risk modelling. Geospatial Insurance Consortium (GIC) is an example whereinsurers are coming together to develop geospatial intelligence customised for theindustry. Insurers can leverage insights derived from remote sensing across theirvalue chain, to enhance risk modelling and financial intelligence across many linesof business (see Figure 8).5Rapid damageassessment afternat cat eventsClaims & PreventionSpottingundeclaredpower plantsSource: ESA Compendium of Earth Observation contributions to the SDG Targets and Indicators, Swiss Re InstituteThere is growing awareness in insuranceof the potential that remote sensingtechnology offers.Crop yieldestimationthrough smartsamplingMeasuring andforecasting windspeedProviding earlySubsidenceEarningswarning signsloss assessmentprediction byfor faminestudying assetStudyingusageglacier meltsSupply chainStudyingCrop parametricPreservingmonitoring andlandslidesCalculatingproduct throughwetlandChargingsustainabilitydepth ofCHF indexecosystemshigher premiumsnowpackfor flood proneMeasuringzonesDevelopingAssessing fuelalgae as anMonitoringair qualityDetecting oilCrop parametriceconomy ofindicatorofvolcanoesindexspills in the sea vehicle emissionproduct throughenvironmentalDetectinghealthland cover/use NDVIPreventing theHighThe availability of technical capacity isthe biggest hurdle in widespreadadoption of EO for SDG tracking.PropertyparametricproductdevelopmentThe Group on Earth Observations (GEO) is a global initiative to promote open sharing of EO data andinfrastructure to build a sustainable world. EO4SDG is one of the GEO initiatives to utilise earthobservation data to enable SDG achievement.Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20216sigma 1/2020 - Data-driven insurance: ready for the next frontier?, Swiss Re InstituteSwiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 20219

Demand drivers of remote sensing in insuranceInnovation trends in remote sensinginflation and wages could further enhance platform value by enabling the runningof simulated estimates of losses and time needed to restore operations.Figure 9Stylised representation of a risk resilience platformRisk resilience platformRisk intelligenceIndustrial sensingAccumulation controlMobile crowd sensingLoss predictionHydrometeorological dataLine ofbusinessUse caseRemote ience consultingRisk domainProductmanagementUnderwriting& pricingMarketing& salesClaimsmanagementCustomerserviceSmart samplingenabled by bothpassive and active(SAR) remotesensing.Less manpowerrequired toestimate yields.Reliablesampling andcost saving.Establishingcorrelationbetween remotesensing vshistorical yielddata.Risk assessment,early warning forpreventable croplosses.ClaimsassessmentPropertyRapid damageassessmentafter large scaleflood events.Flood mapsenabled by bothpassive and active(SAR) remotesensing.Faster decisionson claimadmissibility andsettlement.Better reservingand lower moralhazard.Measuring peakflood height andtime for whichwater stands still.Loss predictionand riskmonitoring.Deployment andplanning of claimsadjusters to detectaffected policyowners.Underwritingand claimsassessmentPropertyDetectingseverity of roofand buildingstructuredamage.Passive (aerial)imagery analysedwith semanticsegmentation.Fasterunderwriting.Better reservingand lower moralhazard.Aerial imagery canbe costly anddifficult to acquireat short notice.Property lossprediction and riskmonitoring.ClaimsassessmentPropertySubsidence lossassessment andprediction forsinkingstructures.Active (DifferentialInterferometricSynthetic ApertureRadar or D-InSAR)images analysedover long time.Faster decisionon claimadmissibility andsettlement.Hard to achievehigher temporalresolution acrossdifferentgeneration ofsatellites.Landslide andsubsidence riskassessment andmonitoring.ProductdevelopmentAgricultureCrop parametricproduct fordrought risksbased on soilmoisture index.Passive(microwave)images to measuresoil moisturelevels.Automatedunderwritingand payout.Morecomprehensivethan NormalizedDifferenceVegetation Index(NDVI) andrainfall index.Morecustomisationrequired foradjusting theindex to crop typeand sowing stage.Similar productsfor pasture or yieldbased lood)parametricproduct basedon excessrainfall index.Passive remotesensing imagescombined withdata fromground-basedweather stations.Affordableproduct withautomatedunderwritingand payout.Longer timeseries of weatherdata.High basis risk.Not suitable forsingle location riskand retailcustomers.New windparametricproducts usingdata from satellitessuch as Aeolus.Risk researchSource: Swiss Re InstituteInsurers may need to invest in new databusinesses and initiatives.Such business models will require secure access to data including insights fromconnected objects, platform providers, behavioural insights from consumers andenvironmental data. The seven categories in Figure 9 are examples of data that willbe available to insurers. Some of these are newer (eg, consumer behavioural data),and others more traditional data sources (eg, from claims and preventive services).Different stakeholders will seek control over data-driven businesses and insurerswill need to keep up with developments in data aggregation to retain their relevance.For instance, if data brokers become omnipresent, over time it could happen thatinsurers face a risk of being relegated to the status of information suppliers.FutureapplicationsCrop yieldestimation toassess andsettle claims.Bespoke risk transferInsurancevalue chainChallengesAgricultureHistorical loss dataPropertyBenefitsClaimsassessmentLoss simulationClients’ asset footprintRemote sensing can expand the bounds of insurability and make households andbusinesses more resilient, thus contributing to a more sustainable future. Examplesof insurance processes that can benefit already are granular indices for parametricproducts, rapid claims assessment and early warning alerts for customers. Theseoutcomes either directly or indirectly can promote a number of UN SDGs and enablethe insurance industry to play its part in fostering sustainability and resilience forsociety at large (see Table 2).Table 2Primary use cases of remote sensing in insuranceValue chainMacroeconomic & financial dataTraditional riskdata (static)Claims assessment and parametricproduct development are the main usecases for remote sensing i

4 Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021 Swiss Re Institute Remote sensing innovation: progressing sustainability goals and expanding insurability August 2021 5 Supply side and economic factors driving adoption Remote sensing, which includes both space and earth observation (EO), is the

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