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Ticinum Aerospace

Exposure Data Enhancement Sources and techniques to improve exposure data - Sample dataset in the Groningen (NL) area

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Contents

Disclaimer ................................................................................................................................................. 2

Exposure data ........................................................................................................................................... 3

Sample data .............................................................................................................................................. 5

About ........................................................................................................................................................ 6

Disclaimer The information contained in this document is confidential, privileged and only for the information of

the intended recipient and may not be used, published or redistributed without the prior written

consent of Ticinum Aerospace srl.

Ticinum Aerospace s.r.l.

Via Ferrini 17/C, Pavia (PV), 27100, Italy

Email: info@ticinumaerospace.com

Phone: +39 0382 1726539

www.ticinumaerospace.com

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Exposure data The Ticinum Aerospace (TA) team is tackling the problem of insufficient risk exposure data at the

global scale, by providing detailed, punctual, geocoded risk-related data. We understand the need of

private companies, and public bodies, to possess a buildings database as accurate as possible, and thus

we have developed different services exploiting several geospatial datasets including satellite data,

street-level images, and more.

Different options are described, which contribute to filling data gaps. The main data sources that have

been explored fall into the ‘geospatial’ field, and thus in using information characterized by geographic

coordinates.

Satellite images provide a clear overview of buildings from a nadiral observation point (Figure 1). They

are indeed useful to retrieve physical features of the buildings (e.g. footprint, roof-type, etc.) and their

characteristics (e.g. presence of swimming-pool, solar panels, etc..), but also to describe the context

where they fit in order to deduce additional context variables (e.g. average wealth of the

neighborhood, vegetation indexes, etc.). A large-scale view from a satellite would be also useful in

describing specific perils. In case of floods, images from space would be useful in identifying water

courses and natural vegetation barriers, or even simply reckon distances from the insured properties.

Nowadays, satellites are capable of sensing the Earth surface with a spatial resolution down to 30

centimeters (optical) or 1 meter (radar), with possibly daily revisits.

Figure 1. Optical satellite image which provide a top-view representation of buildings.

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Street-Level pictures, instead, provide a front-view of buildings, and are thus ideal for extracting several

risk-related building features. TA has developed a new framework able to automatically extract building

features by analyzing street-level pictures, and then attach them to building footprints (Figure 2).

Experiments have been run on different urban environments, such as Manila (The Philippines),

Medellin (Colombia), Milan (Italy), Eindhoven (The Netherlands), and Ankara (Turkey). Additional

details about the framework developed by TA are available at this link: WebURL. Moreover, the TA

team is also developing mobile Apps for on-site acquisitions, that can be used for on-demand

acquisitions and, consequently, targeted updating of the buildings database.

Figure 2. Overview of framework developed by TA.

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Sample data

The sample dataset available on the Oasis HUB includes over 145,000 buildings automatically labeled

as a result of processing street-level images.

The dataset covers a large fraction (almost 1.500 sqkm) of the province of Groningen, in the

Netherlands. The full list of attributes, and their meanings, associated to each exposure data is

summarized in the table below:

ID Unique identifier for each building.

DATE Acquisition date of the street-level image.

DISTANCE Distance of the building from the street. Multiple values refers to different, perpendicular, observation points.

N_FLOORS Count of storeys. The value ‘1’ refers to building with ground level only.

ATTICS Count of attics. An attic is defined here as a floor partly or fully located above the lower end of the roof.

EXCESS

Excess floors flag. If the building façade covers the entire image height, then the exact number of floors cannot be determined as some may be outside the image area. In this case, the number of visible storeys is identified anyway but a specific flag is set that reports the event (the so-called “excess floors”). This happens sometimes with tall buildings or with buildings located too close to the road. ‘1’ stands for an image having a building façade covering the entire image height.

STATS Values which have been determined statistically based on the neighbors buildings (e.g. image not available)

AREA Surface area in square meters.

OPENING Building openings (windows, doors, etc.) area ratio for the ground floor. It refers to the openings-to-total area ratio of visible building façades.

HEDGE Presence of green hedges. The values ‘0’ and ‘1’ refer respectively to absence and presence of green hedges.

TREES Presence of trees. The values ‘0’ and ‘1’ refer respectively to absence and presence of trees.

CHIMNEYS Presence of chimneys. The value ‘1’ refers to buildings with masonry chimneys. Otherwise ‘0’ is assigned.

OCCUPANCY It refers to the use of the building (e.g. residential, commercial, industrial, etc.).

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About

Ticinum Aerospace (TA) is a spin-off company from the University

of Pavia, Italy founded in early 2014 and focused on analyzing large

amounts of geospatial data through smart and effective use of

machine learning and computer vision techniques.

Since its inception, TA has received several awards, such as the ‘Big

Data Big Business Challenge’ prize and the ‘Copernicus Incubation’

grant, respectively organized by the European Space Agency and

the European Commission. Currently, TA enjoys the status of

‘Innovative PMI’ as per the definition established by the Italian law,

and 80% of its team members possess a PhD degree.

The mission of the company is to provide reliable, customer-oriented solutions taking advantage

of heterogeneous, Big Geospatial Data, with the final aim of “cutting the cost of uncertainties” for

its customers in the insurance business.