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# System prepended metadata

title: Ambee for Insurance PRD

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# Ambee for Insurance PRD
### Insurance Industry
Earth-observing satellites can map natural phenomena such as floods and earthquakes, track hurricanes and monitor land subsidence across the globe. HOwever, the information does not get extracted, recorded, stored properly for quick and immediate proessing and also the interfaces to insurance do not exist that could effectively solve the problem for insurance companies. Albeit, this information can be valuable to insurance companies for risk and damage assessment. Information obtained by Ambee's processed satellite observations can save money and make the insurance industry more efficient.

### Assessing Floods
Accurate and timely information about a flood’s extent can help insurers assess the impact and prepare to meet the claims.


### Flood Extent Map
Due to heavy rainfall in September of 2010, large areas in Slovenia were affected by floods. The most affected areas were the capital Ljubljana and its surroundings. Initial damage was estimated to reach €15 million. The animation shows how the floods retreated.



Assessment of flood caused damages currently requires field visits, costs a lot of money, takes time and is unreliable due to a lack of information.

Using multi-temporal and multi-sensor satellite data, it is possible to quickly asses the situation prior to the event, immediately after the event and monitor further activity a few weeks after the event as well.

### Process Workflow
Let’s take a step-by-step look at how a typical (e.g. hail damage) assessment workflow would look like.

1. Importing input data to the GIS in the cloud
1. Parcel boundaries (either SHP files or manual digitization with GIS tools)
1. Claim data (crop type, state of development, damage)
1. Markation of samples for damaged and non-damaged parts of the land (manually with GIS)
1. Automatic calculation of vegetation indices and imagery previews
1. True colour
1. False colour
1. NDVI (normalized difference vegetation index)
1. SAVI (Soil adjusted vegetation index)
1. LAI (Leaf area index)
1. Adjustment of the models to local conditions
1. Performed on sample part of the fields
1. Automatic calculation of the damage assessment data
1. State of development of the plant
1. Based on comparison of LAI on non-damaged parts with standard LAI for claimed crop.
1. Defoliation of crops in damaged areas based on LAI
1. Assessment of damaged areas
1. Division of the parcel to smaller areas based on damage percentages (100%, 90%,..)
1. Manual adjustment of the results using GIS tools and visual interpretation of all the available data
1. Comparison with the Sentinel/Landsat imagery archive can help with assessing the development of the crops in the past
1. Production of reports for each parcel (in XLS)
1. Comparison between claimed/observed data
1. Total area of the parcel
1. Total area damaged
1. State of development
1. Defoliation of the plant

### System Architecture


### UserFlow



### Features


### Market Study

#### Market Size

#### Competitor products

#### Differentiation

